US20120179358A1 - System and method for automated updating of map information - Google Patents

System and method for automated updating of map information Download PDF

Info

Publication number
US20120179358A1
US20120179358A1 US13/425,707 US201213425707A US2012179358A1 US 20120179358 A1 US20120179358 A1 US 20120179358A1 US 201213425707 A US201213425707 A US 201213425707A US 2012179358 A1 US2012179358 A1 US 2012179358A1
Authority
US
United States
Prior art keywords
roadway
information
traffic
map database
control
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Abandoned
Application number
US13/425,707
Inventor
Paul A.C. Chang
Matthew L. Ginsberg
Kevin Scavezze
Bryan Smith
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
GREEN DRIVER Inc
On Time Systems Inc
Original Assignee
On Time Systems Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by On Time Systems Inc filed Critical On Time Systems Inc
Priority to US13/425,707 priority Critical patent/US20120179358A1/en
Assigned to ON TIME SYSTEMS, INC. reassignment ON TIME SYSTEMS, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: CHANG, PAUL A.C., GINSBERG, MATTHEW L., SCAVEZZE, KEVIN, SMITH, BRYAN
Priority to US13/542,938 priority patent/US10083607B2/en
Publication of US20120179358A1 publication Critical patent/US20120179358A1/en
Priority to US13/747,145 priority patent/US20130131980A1/en
Priority to US13/775,649 priority patent/US20130166109A1/en
Assigned to GREEN DRIVER, INC. reassignment GREEN DRIVER, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: ONTIME SYSTEMS, INC.
Priority to US15/076,116 priority patent/US9852624B2/en
Priority to US15/822,715 priority patent/US10311724B2/en
Abandoned legal-status Critical Current

Links

Images

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/38Electronic maps specially adapted for navigation; Updating thereof
    • G01C21/3804Creation or updating of map data
    • G01C21/3807Creation or updating of map data characterised by the type of data
    • G01C21/3815Road data
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/38Electronic maps specially adapted for navigation; Updating thereof
    • G01C21/3804Creation or updating of map data
    • G01C21/3807Creation or updating of map data characterised by the type of data
    • G01C21/3811Point data, e.g. Point of Interest [POI]
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/38Electronic maps specially adapted for navigation; Updating thereof
    • G01C21/3804Creation or updating of map data
    • G01C21/3833Creation or updating of map data characterised by the source of data
    • G01C21/3837Data obtained from a single source
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/38Electronic maps specially adapted for navigation; Updating thereof
    • G01C21/3804Creation or updating of map data
    • G01C21/3833Creation or updating of map data characterised by the source of data
    • G01C21/3844Data obtained from position sensors only, e.g. from inertial navigation

Definitions

  • the present invention generally relates to updating and correcting databases of road map information that can be used for vehicle navigation or similar purposes.
  • Digital databases of road map information are essential components of a variety of useful applications, such as vehicle routing.
  • the road map information databases used in vehicle routing systems describe the geographical location and intersections of the roads, or usage restrictions such as one-way restrictions or turn restrictions.
  • the road map information databases also contain other metadata pertinent to vehicle routing, including traffic speeds over the various road segments, the names and address ranges of the roads, the road classification (residential, collector, arterial, highway/freeway) and the like.
  • such databases allow a vehicle routing system to determine the location of a user's vehicle and to take actions useful to the user, such as computing an optimal route from the current location to a desired destination, providing detailed directions for traversing a route, or providing an estimate of the arrival time at the destination. Updates and augmentation of the database further increase the accuracy and capabilities of the applications using the database. For example, the addition to a database of information regarding real-time traffic conditions enables a vehicle routing application to compute a route that not only minimizes overall distance, but also minimizes driving time as well, based on the current traffic speeds associated with the route.
  • GPS Global Positioning System
  • databases frequently lack important categories of information that applications could use to provide more accurate results or entirely new categories of features.
  • Some examples of information that is not generally available are the locations of stop signs and traffic signals, information about whether traffic signals are pre-timed to coordinate with traffic flow, and turn restrictions that may only be active at certain times of day.
  • information about turn restrictions that are active at certain times of day could be used to detect that a route that was optimal at 11:00 AM would be entirely prohibited at 6:00 PM, thus avoiding proposing an invalid route to the user.
  • database information may contain inaccuracies due to human error on the part of those creating the database, or due to failures to update the database to reflect actual changes in the roads subsequent to the creation of the database.
  • a database may erroneously indicate that an intersection has no stop signs even months after stop signs have been installed.
  • a crossing between two roads may be incorrectly identified as an at-grade intersection where, in reality, the crossing involves a bridge, overpass or tunnel.
  • map database information is augmented using information obtained by recording the movements of a vehicle equipped with a positioning system device, deriving additional map details based upon those movements, and updating the database to reflect the additional details.
  • the information is obtained by inference from related roadway characteristic information, such as may be stored in a database.
  • Some embodiments of the invention augment a map database by deriving entirely new categories of information not previously tracked by the database.
  • the presence of stop signs is detected by observed or otherwise obtained information about relative sizes of intersecting roadways, vehicle speeds, speed limits, or traffic volumes.
  • embodiments of the invention can be used to derive additional information about characteristics of the roads themselves, independent of current traffic conditions.
  • stop signs may reasonably be inferred for a small two-lane road with a known 30 mph speed limit where it has an at-grade intersection with a four-lane divided highway with a known 55 mph speed limit.
  • stop signs may also be inferred for a residential road where it has an at-grade intersection with a non-residential road.
  • Certain turn restrictions may also be inferred from either the geometry or type of road segment. For instance, on-ramps onto the eastbound lanes of a freeway generally cannot be used to maneuver a vehicle onto the westbound lanes. Or, a short road segment which is used as a dedicated right turn lane cannot be used for making left turns.
  • FIG. 1 is a flowchart illustrating high-level steps performed according to one embodiment.
  • FIG. 2 is a high-level block diagram illustrating a computing device for implementing a preferred embodiment.
  • FIG. 3 is a diagram of an intersection for which stop sign information is inferred as described herein.
  • Embodiments of the invention perform various map database augmentation and correction techniques to derive additional information not currently within the map database. Such techniques conform to the general pattern set forth in FIG. 1 .
  • roadway information is obtained.
  • such information is obtained by receipt of vehicle readings provided, for example, by conventional satellite-based GPS systems, and include location (e.g. latitude and longitude) and velocity (e.g. speed and heading) information for the vehicle to which they correspond.
  • location e.g. latitude and longitude
  • velocity e.g. speed and heading
  • the map database already includes pertinent road characteristic information (e.g., classification of roadways as local as opposed to through highways, speed limits, historical traffic volumes, number of travel lanes) for roadway segments, such information for roadway segments forming the intersection are fetched.
  • pertinent road characteristic information e.g., classification of roadways as local as opposed to through highways, speed limits, historical traffic volumes, number of travel lanes
  • step 110 the information thus obtained is analyzed to determine whether it is reasonable to infer the presence of a traffic control, for example a stop sign.
  • a traffic control for example a stop sign.
  • step 105 it is determined that an east-west traffic segment has vehicles traveling at 55 mph (whether determined by a database including the speed limit for that segment or as observed using GPS readings from actual vehicles), and it is also determined in the same manner that a segment of an intersecting north-south road has vehicles traveling at 30 mph, it is reasonable to infer the presence of stop signs at the north-south road.
  • the inference of a stop sign is accurate. There may be a yield sign, a blinking red or yellow light, or a full tri-color traffic light at the intersection. For many applications, however, all that is important is to recognize that traffic on, say, the east-west roadway will likely not be slowed as much at the intersection as traffic on the north-south roadway.
  • Such inferences can be useful in situations that involve estimated travel times for trip planning, for computing routes that minimize delays caused by such traffic controls, and the like. The amount of time spent at one such traffic control may not be significant, but the accumulation of such delays on a route that traverses 100 such intersections can have a significant negative impact on the desirability or optimality of this route.
  • stop sign Another case where the inference of a stop sign is useful, even though such an inference may be incorrect, is to bias against routes that cut through residential neighborhoods in order to avoid traffic jams or traffic signals.
  • inferred stop signs along the residential roads are used to impede traffic flow through the residential neighborhoods.
  • the additional inferred information is added to the database.
  • the database is updated to reflect the inference that a pair of stop signs are controlling the north-south road of the intersection.
  • Some commercial road map databases have existing fields that can be directly populated with information such as the location and nature of a traffic control device, such as a stop sign or traffic signal, and such information is simply entered in the required manner.
  • Other databases may not have such a provision already available, and for such databases an ancillary structure is created to allow for entry of such inferred information. For instance, a new field may be created in the database that associates a traffic control device with a particular location and direction of travel.
  • a database is “augmented,” “updated,” or “modified” by creating an entirely new instance of the database or, in some embodiments, creating an entirely new type of database (e.g., as may be best suited for the nature of information now to be included).
  • “updating” as used herein are to be interpreted broadly to include any such manner of including such new information in a database, as may be evident to those skilled in the art.
  • FIG. 2 is a high-level block diagram illustrating a computing device 200 for modifying map databases according to the general technique set forth in FIG. 1 .
  • user device 200 is a general purpose computer programmed and configured to provide the operations described herein.
  • Processor 202 is conventionally coupled to memory 206 and bus 204 . Also coupled to the bus 204 are memory 206 , storage device 208 , and data reception unit 210 .
  • the data constituting the map database is contained in storage device 208 and loaded into memory 206 .
  • the general structure of a map database is well-known to those of skill in the art, and conventionally involves storing a series of data objects representing the series of road segments that describes the road, including the spatial extent of the road segment and information associated with the segment, such as speed limit.
  • processor 202 is any general or specific purpose processor such as an INTEL 386 compatible central processing unit (CPU).
  • Storage device 208 is any device capable of persistently storing large amounts of data as required by the map database, such as a hard drive or a high-capacity memory card.
  • Memory 206 holds instructions and data used by the processor 202 .
  • the data reception unit 210 receives road characteristic information, such as whether a road is classified as a local residential street or a divided highway, from an external source (not shown, e.g., a municipality's server site via the Internet). Thus, data reception unit 210 can also be considered an input subsystem providing roadway characteristics.
  • processor 202 The instructions stored in the memory 206 and executed by the processor 202 allow the derivation of additional map information based upon the vehicle readings and the subsequent storing of the additional information within the map database for later use by a navigation or other program.
  • processor 202 and memory 206 operating together can also be considered an information inference module.
  • the above-disclosed user device 200 of FIG. 2 is implemented in one embodiment as a fixed computer (e.g., a blade-type server accessed via a client computer with a web-based user interface and a shared map database that is globally accessible) and in another embodiment as a user device 200 that is located within a vehicle, so that the map database is local.
  • a separate computer hosts and provides a global version of the map database, while user device 200 retains a local copy thereof.
  • map database augmentation and correction techniques performed by embodiments of the invention as set forth in FIG. 1 are now described in more detail below.
  • one such approach applies the information, such as vehicle speed, provided by a user device to existing information stored in the map database, deriving additional information and updating the database therewith.
  • Data from vehicles may be too sparse in some circumstances to determine whether vehicle stops occur frequently enough at an intersection to warrant an inference of a stop sign. However, there may be sufficient data indicating that traffic in the area regularly travels at high speed on an east-west road and a significantly lower speed on an intersecting north-south road. Alternatively, a map database may be available that includes entries indicating a significantly higher posted speed limit on the east-west road than on the intersecting north-south road. Another usable parameter is volume of traffic. The intersection of one roadway having historically high traffic volumes with another, low-volume roadway suggests there may be a traffic control on the low-volume roadway.
  • a classification for a roadway such as “residential”, “local”, “through road”, “business route” or “federal highway”, and significant classification differences between two intersecting roadways will in some embodiments support an inference of whether a traffic control is present. While such information may not be sufficient to predict with certainty the presence of any particular traffic control device, for an application not requiring absolute accuracy it may be sufficient to recognize a reasonable likelihood that such a traffic control device is present.
  • a map database that includes stop sign information but does not currently indicate that there are any stop signs at a particular intersection. If a shopping center is built nearby, that may increase traffic flow sufficiently that the municipality decides to put in a pair of stop signs on one of the two roads forming the intersection. Review of traffic volume data over time as described herein may lead to an inference that a stop sign or traffic light has been added, warranting a visual inspection of the intersection to confirm that this is true. If such a stop sign has been added, then correction of the old database information is appropriate, and can either be done manually after the visual inspection or automatically in a provisional manner (subject to verification by later visual inspection).
  • a commercial map database may represent the larger road 310 differently than the smaller road 320 , for instance indicating that the larger road is a four-lane highway while the smaller road is only two lanes wide (or possibly less). Assuming that the map database also indicates that the intersection 300 is an at-grade intersection, as opposed to being an intersection involving a bridge or other overpass or tunnel, the difference in size of the roadways alone may be sufficient to infer that the municipality or other controlling roadway authority has installed stop signs 321 and 322 to control traffic on the north-south roadway.
  • each roadway is often usable as a factor in how best to infer the type of traffic control.
  • a small country road intersects a large U.S. highway at grade level, it is highly likely that there will be stop signs for the small road but not for the U.S. highway.
  • the presence of a divided highway i.e., a boulevard or other roadway with a median strip separating travel lanes) further increases the likelihood of such a traffic control on an intersecting smaller roadway.
  • a smaller road meets a larger road with an arc-shaped segment rather than at a right angle, there is an increased likelihood that a yield sign rather than a stop sign is controlling traffic from the smaller roadway.
  • a pseudo-code representation of processing to infer presence of a stop sign in one embodiment is:
  • the determined speed for each roadway is obtained either by observation of readings from GPS devices in vehicles over time, or by reference to speed limit data (e.g., from an existing database).
  • each “arm” of an intersection is considered separately, such that road 310 is processed separately for its western arm (“ 310 W”) and eastern arm (“ 310 E”) and road 320 is likewise broken up into arms “ 320 N” and “ 320 S”.
  • processing in this manner is implemented by considering “local” roads to be those classified as residential or having speed limits of 25 mph or less, while “nonlocal” roads are those classified as “collectors” or having classifications higher than “residential”. Then, if a map database does not already indicate that an intersection is signalized, processing is implemented in one embodiment as:
  • Various other roadway and traffic control characteristics can be inferred in a similar manner.
  • traffic light characteristics for example. Many traffic lights are synchronized along a roadway and are coordinated with one another such that vehicles traveling at a specified speed will be able to go through many intersections without encountering a red signal.
  • the specified speed varies based on factors such as traffic congestion levels. There may be no database record indicating that certain lights are synchronized or what the synchronization scheme is.
  • certain intersections have phases of operation that vary based on congestion or time of day.
  • a green left turn signal may appear before a general green signal, during the general green or after.
  • Controller timing parameters may be programmed by a municipality based on any number of factors.
  • phasing of various traffic signals is determined by comparison of real time state information among sets of adjacent traffic signals. This is particularly straightforward in the case of one-way streets.
  • information from vehicles equipped with GPS and communications devices serves as a proxy for such real-time information about phasing.
  • sensor information e.g., from inductive loops embedded in roadways
  • municipalities that can likewise be used to track movement of individual vehicles over time and thus provide a basis for deriving traffic signal phasing information.
  • vehicles routinely “bunch” at red lights and the size of a particular bunch of vehicles can be used to track those vehicles as a group to determine whether they stop only rarely on a roadway with many signaled intersections (suggesting synchronized traffic lights) or stop fairly frequently and in a somewhat random temporal pattern (suggesting that the segment of roadway does not enjoy synchronized traffic lights).
  • some municipalities provide traffic phase mappings or related information (e.g., so-called “ring diagrams”) that likewise can be used to infer map database information.
  • a municipality may mark an intersection as subject to a generalized phase, such as “Phase 7—Pedestrian Fully Protected”.
  • inferences can be made that at least during certain time periods, stopping times will be increased for vehicles because they will be subject not only to red lights to allow orthogonally-directed traffic to pass, but also red lights to allow pedestrians to pass.
  • published information noting signaling configuration for “inbound commute” suggests potential use of center lane or breakdown lane restrictions that provide an extra lane in the inbound direction or synchronized lights favoring inbound traffic. All such information is available in various embodiments to be used as factors for derivation of map database information.
  • embodiments of the invention allow the capture of numerous additional types of information not previously reflected within the map database, leading to greater functionality and greater accuracy for the increasing number of map applications that rely on such information.
  • any reference to “one embodiment” or “an embodiment” means that a particular element, feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment.
  • the appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.
  • the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having” or any other variation thereof, are intended to cover a non-exclusive inclusion.
  • a process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
  • “or” refers to an inclusive or and not to an exclusive or. For example, a condition A or B is satisfied by any one of the following: A is true (or present) and B is false (or not present), A is false (or not present) and B is true (or present), and both A and B are true (or present).
  • Certain aspects of the present invention include process steps and instructions described herein in the form of a method. It should be noted that the process steps and instructions of the present invention could be embodied in software, firmware or hardware.
  • the computer program for deriving additional information is preferably persistently stored in a computer readable storage medium, such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, application specific integrated circuits (ASICs), or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus.
  • a computer readable storage medium such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, application specific integrated circuits (ASICs), or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus.

Abstract

The characteristics of two intersecting roadways are compared to determine whether an inference can be made as to whether there are traffic controls (e.g., stop signs) on one of the roadways. If a larger road with characteristically higher speed intersects with a small road with lower speed, the small road is determined to have a stop sign. A map database is updated with the information regarding the inferred traffic control, and that information is then usable for purposes such as trip planning.

Description

    RELATED APPLICATIONS
  • This application is a continuation in part of co-pending U.S. patent application Ser. No. 11/851,953, filed Sep. 7, 2007, entitled “System and Method for Automated Updating of Map Information”, which is incorporated herein by reference.
  • BACKGROUND
  • 1. Field of Art
  • The present invention generally relates to updating and correcting databases of road map information that can be used for vehicle navigation or similar purposes.
  • 2. Description of the Related Art
  • Digital databases of road map information are essential components of a variety of useful applications, such as vehicle routing. The road map information databases used in vehicle routing systems describe the geographical location and intersections of the roads, or usage restrictions such as one-way restrictions or turn restrictions. The road map information databases also contain other metadata pertinent to vehicle routing, including traffic speeds over the various road segments, the names and address ranges of the roads, the road classification (residential, collector, arterial, highway/freeway) and the like. In conjunction with real-time location data, such as that provided by a satellite-based Global Positioning System (GPS), such databases allow a vehicle routing system to determine the location of a user's vehicle and to take actions useful to the user, such as computing an optimal route from the current location to a desired destination, providing detailed directions for traversing a route, or providing an estimate of the arrival time at the destination. Updates and augmentation of the database further increase the accuracy and capabilities of the applications using the database. For example, the addition to a database of information regarding real-time traffic conditions enables a vehicle routing application to compute a route that not only minimizes overall distance, but also minimizes driving time as well, based on the current traffic speeds associated with the route.
  • However, databases frequently lack important categories of information that applications could use to provide more accurate results or entirely new categories of features. Some examples of information that is not generally available are the locations of stop signs and traffic signals, information about whether traffic signals are pre-timed to coordinate with traffic flow, and turn restrictions that may only be active at certain times of day. As an example of the utility of such information, information about turn restrictions that are active at certain times of day could be used to detect that a route that was optimal at 11:00 AM would be entirely prohibited at 6:00 PM, thus avoiding proposing an invalid route to the user.
  • Of additional concern is the fact that database information may contain inaccuracies due to human error on the part of those creating the database, or due to failures to update the database to reflect actual changes in the roads subsequent to the creation of the database. For example, a database may erroneously indicate that an intersection has no stop signs even months after stop signs have been installed. Or, a crossing between two roads may be incorrectly identified as an at-grade intersection where, in reality, the crossing involves a bridge, overpass or tunnel.
  • Some commentators have discussed the possibility of using in-vehicle GPS units to correct one of the deficiencies found in many databases—the lack of real-time information on traffic speeds—by aggregating the individual vehicle speeds recorded by GPS units over many vehicles to obtain statistical information about likely vehicle speeds on a particular road segment at a given time. However, there remain many other database information deficiencies for which no automated solutions have been discussed, although the need to address these deficiencies becomes ever more pressing as the number of related routing applications grows.
  • SUMMARY
  • As disclosed herein, map database information is augmented using information obtained by recording the movements of a vehicle equipped with a positioning system device, deriving additional map details based upon those movements, and updating the database to reflect the additional details. Alternatively, the information is obtained by inference from related roadway characteristic information, such as may be stored in a database.
  • Some embodiments of the invention augment a map database by deriving entirely new categories of information not previously tracked by the database. In one embodiment, the presence of stop signs is detected by observed or otherwise obtained information about relative sizes of intersecting roadways, vehicle speeds, speed limits, or traffic volumes. Thus, embodiments of the invention can be used to derive additional information about characteristics of the roads themselves, independent of current traffic conditions.
  • Certain road characteristics (e.g., presence of stop signs) are inferred from other, already known road characteristics. For instance, stop signs may reasonably be inferred for a small two-lane road with a known 30 mph speed limit where it has an at-grade intersection with a four-lane divided highway with a known 55 mph speed limit. Similarly, stop signs may also be inferred for a residential road where it has an at-grade intersection with a non-residential road. Certain turn restrictions may also be inferred from either the geometry or type of road segment. For instance, on-ramps onto the eastbound lanes of a freeway generally cannot be used to maneuver a vehicle onto the westbound lanes. Or, a short road segment which is used as a dedicated right turn lane cannot be used for making left turns.
  • The features and advantages described in the specification are not all inclusive and, in particular, many additional features and advantages will be apparent to one of ordinary skill in the art in view of the drawings, specification, and claims. Moreover, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter.
  • BRIEF DESCRIPTION OF DRAWINGS
  • The disclosed embodiments have other advantages and features which will be more readily apparent from the following detailed description and the appended claims, when taken in conjunction with the accompanying drawings, in which:
  • FIG. 1 is a flowchart illustrating high-level steps performed according to one embodiment.
  • FIG. 2 is a high-level block diagram illustrating a computing device for implementing a preferred embodiment.
  • FIG. 3 is a diagram of an intersection for which stop sign information is inferred as described herein.
  • DETAILED DESCRIPTION
  • The figures and the following description relate to preferred embodiments by way of illustration only. It should be noted that from the following discussion, alternative embodiments of the structures and methods disclosed herein will be readily recognized as viable alternatives that may be employed without departing from the principles of the claimed invention.
  • Method Overview
  • Embodiments of the invention perform various map database augmentation and correction techniques to derive additional information not currently within the map database. Such techniques conform to the general pattern set forth in FIG. 1. At step 105 of FIG. 1, roadway information is obtained. In one embodiment, such information is obtained by receipt of vehicle readings provided, for example, by conventional satellite-based GPS systems, and include location (e.g. latitude and longitude) and velocity (e.g. speed and heading) information for the vehicle to which they correspond. As an alternative or additional step, if the map database already includes pertinent road characteristic information (e.g., classification of roadways as local as opposed to through highways, speed limits, historical traffic volumes, number of travel lanes) for roadway segments, such information for roadway segments forming the intersection are fetched.
  • At step 110, the information thus obtained is analyzed to determine whether it is reasonable to infer the presence of a traffic control, for example a stop sign. To illustrate, if in step 105 it is determined that an east-west traffic segment has vehicles traveling at 55 mph (whether determined by a database including the speed limit for that segment or as observed using GPS readings from actual vehicles), and it is also determined in the same manner that a segment of an intersecting north-south road has vehicles traveling at 30 mph, it is reasonable to infer the presence of stop signs at the north-south road.
  • In many applications, it may not matter that the inference of a stop sign is accurate. There may be a yield sign, a blinking red or yellow light, or a full tri-color traffic light at the intersection. For many applications, however, all that is important is to recognize that traffic on, say, the east-west roadway will likely not be slowed as much at the intersection as traffic on the north-south roadway. Such inferences can be useful in situations that involve estimated travel times for trip planning, for computing routes that minimize delays caused by such traffic controls, and the like. The amount of time spent at one such traffic control may not be significant, but the accumulation of such delays on a route that traverses 100 such intersections can have a significant negative impact on the desirability or optimality of this route. Another case where the inference of a stop sign is useful, even though such an inference may be incorrect, is to bias against routes that cut through residential neighborhoods in order to avoid traffic jams or traffic signals. In particular, inferred stop signs along the residential roads are used to impede traffic flow through the residential neighborhoods.
  • Finally, at step 115 the additional inferred information is added to the database. For example, the database is updated to reflect the inference that a pair of stop signs are controlling the north-south road of the intersection. Some commercial road map databases have existing fields that can be directly populated with information such as the location and nature of a traffic control device, such as a stop sign or traffic signal, and such information is simply entered in the required manner. Other databases may not have such a provision already available, and for such databases an ancillary structure is created to allow for entry of such inferred information. For instance, a new field may be created in the database that associates a traffic control device with a particular location and direction of travel. In still other embodiments, a database is “augmented,” “updated,” or “modified” by creating an entirely new instance of the database or, in some embodiments, creating an entirely new type of database (e.g., as may be best suited for the nature of information now to be included). Thus, terms such as “updating” as used herein are to be interpreted broadly to include any such manner of including such new information in a database, as may be evident to those skilled in the art.
  • System Architecture
  • FIG. 2 is a high-level block diagram illustrating a computing device 200 for modifying map databases according to the general technique set forth in FIG. 1. In one embodiment, user device 200 is a general purpose computer programmed and configured to provide the operations described herein. Processor 202 is conventionally coupled to memory 206 and bus 204. Also coupled to the bus 204 are memory 206, storage device 208, and data reception unit 210. The data constituting the map database is contained in storage device 208 and loaded into memory 206. The general structure of a map database is well-known to those of skill in the art, and conventionally involves storing a series of data objects representing the series of road segments that describes the road, including the spatial extent of the road segment and information associated with the segment, such as speed limit.
  • In a typical embodiment, processor 202 is any general or specific purpose processor such as an INTEL 386 compatible central processing unit (CPU). Storage device 208 is any device capable of persistently storing large amounts of data as required by the map database, such as a hard drive or a high-capacity memory card. Memory 206 holds instructions and data used by the processor 202. The data reception unit 210 receives road characteristic information, such as whether a road is classified as a local residential street or a divided highway, from an external source (not shown, e.g., a municipality's server site via the Internet). Thus, data reception unit 210 can also be considered an input subsystem providing roadway characteristics. The instructions stored in the memory 206 and executed by the processor 202 allow the derivation of additional map information based upon the vehicle readings and the subsequent storing of the additional information within the map database for later use by a navigation or other program. Thus, processor 202 and memory 206 operating together can also be considered an information inference module.
  • One of skill in the art would recognize that the above described system is merely for purposes of example, and that many other configurations for implementing the invention are equally possible. For example, the above-disclosed user device 200 of FIG. 2 is implemented in one embodiment as a fixed computer (e.g., a blade-type server accessed via a client computer with a web-based user interface and a shared map database that is globally accessible) and in another embodiment as a user device 200 that is located within a vehicle, so that the map database is local. In one embodiment, a separate computer hosts and provides a global version of the map database, while user device 200 retains a local copy thereof.
  • Database Updating Operations
  • The various map database augmentation and correction techniques performed by embodiments of the invention as set forth in FIG. 1 are now described in more detail below. As previously discussed, one such approach applies the information, such as vehicle speed, provided by a user device to existing information stored in the map database, deriving additional information and updating the database therewith.
  • In some environments, such as described in co-pending commonly owned U.S. patent application Ser. No. 11/851,953, published Mar. 12, 2009 as US 2009-0070031 (the contents of which is hereby incorporated by reference as if fully set forth herein), the presence or absence of traffic control devices such as stop signs or traffic signals is detected by observing the speed of a vehicle arriving at an intersection, and the nature/duration of delay of the vehicle at an intersection (e.g., a consistent delay of a few seconds suggests a stop sign, while a green/yellow/red traffic light is suggested by vehicles sometimes being delayed by a significant amount and sometimes not being delayed at all). Data from vehicles may be too sparse in some circumstances to determine whether vehicle stops occur frequently enough at an intersection to warrant an inference of a stop sign. However, there may be sufficient data indicating that traffic in the area regularly travels at high speed on an east-west road and a significantly lower speed on an intersecting north-south road. Alternatively, a map database may be available that includes entries indicating a significantly higher posted speed limit on the east-west road than on the intersecting north-south road. Another usable parameter is volume of traffic. The intersection of one roadway having historically high traffic volumes with another, low-volume roadway suggests there may be a traffic control on the low-volume roadway. Still further, there may be information stored in a database as to a classification for a roadway, such as “residential”, “local”, “through road”, “business route” or “federal highway”, and significant classification differences between two intersecting roadways will in some embodiments support an inference of whether a traffic control is present. While such information may not be sufficient to predict with certainty the presence of any particular traffic control device, for an application not requiring absolute accuracy it may be sufficient to recognize a reasonable likelihood that such a traffic control device is present.
  • In a related application, consider a map database that includes stop sign information but does not currently indicate that there are any stop signs at a particular intersection. If a shopping center is built nearby, that may increase traffic flow sufficiently that the municipality decides to put in a pair of stop signs on one of the two roads forming the intersection. Review of traffic volume data over time as described herein may lead to an inference that a stop sign or traffic light has been added, warranting a visual inspection of the intersection to confirm that this is true. If such a stop sign has been added, then correction of the old database information is appropriate, and can either be done manually after the visual inspection or automatically in a provisional manner (subject to verification by later visual inspection).
  • Referring now to FIG. 3, there is illustrated an intersection 300 between a large east-west roadway 310 and a smaller north-south roadway 320. A commercial map database may represent the larger road 310 differently than the smaller road 320, for instance indicating that the larger road is a four-lane highway while the smaller road is only two lanes wide (or possibly less). Assuming that the map database also indicates that the intersection 300 is an at-grade intersection, as opposed to being an intersection involving a bridge or other overpass or tunnel, the difference in size of the roadways alone may be sufficient to infer that the municipality or other controlling roadway authority has installed stop signs 321 and 322 to control traffic on the north-south roadway.
  • As a slight variation, instead of two intersecting automobile roads, if a roadway intersects a railroad track at grade, that almost certainly indicates a practical need for certain types of vehicles (e.g., school buses, commercial vehicles) to stop before crossing the railroad track. Therefore, for commercial route planning purposes and the like, the addition of a virtual traffic control (the mandatory stop at a railroad track for such vehicles) to a map database allows more accurate navigational services such as travel time planning.
  • The nature of each roadway is often usable as a factor in how best to infer the type of traffic control. Where a small country road intersects a large U.S. highway at grade level, it is highly likely that there will be stop signs for the small road but not for the U.S. highway. The presence of a divided highway (i.e., a boulevard or other roadway with a median strip separating travel lanes) further increases the likelihood of such a traffic control on an intersecting smaller roadway. Where a smaller road meets a larger road with an arc-shaped segment rather than at a right angle, there is an increased likelihood that a yield sign rather than a stop sign is controlling traffic from the smaller roadway.
  • Using the example illustrated in FIG. 3, a pseudo-code representation of processing to infer presence of a stop sign in one embodiment is:
  • Process Road 310:
      • a. Augment Counter310 by number of lanes
      • b. Augment Counter310 if roadway is divided
      • c. Augment Counter310 by determined speed in mph/10
      • d. Augment Counter310 if historical traffic volume>20% over average for that type of road
  • Process Road 320:
      • a. Augment Counter320 by number of lanes
      • b. Augment Counter320 if roadway is divided
      • c. Augment Counter320 by determined speed in mph/10
      • d. Augment Counter320 if historical traffic volume>20% over average for that type of road
  • If Counter310−Counter320>3, then infer stop sign on Road 320
  • If Counter320−Counter310>3, then infer stop sign on Road 310
  • As noted above, the determined speed for each roadway is obtained either by observation of readings from GPS devices in vehicles over time, or by reference to speed limit data (e.g., from an existing database).
  • In many instances, each “arm” of an intersection is considered separately, such that road 310 is processed separately for its western arm (“310W”) and eastern arm (“310E”) and road 320 is likewise broken up into arms “320N” and “320S”. This allows more detailed processing that is expected, in various environments, to be more accurate in inferring the presence of traffic controls. In one embodiment, processing in this manner is implemented by considering “local” roads to be those classified as residential or having speed limits of 25 mph or less, while “nonlocal” roads are those classified as “collectors” or having classifications higher than “residential”. Then, if a map database does not already indicate that an intersection is signalized, processing is implemented in one embodiment as:
  • Case: Intersection has both local and nonlocal arms
      • a. If there is only one incoming nonlocal arm, then
        • i. If there is a local arm which is a continuation of the non-local arm (same road name or no turn from the non-local arm), then infer stop signs are present on all other local arms
        • ii. Otherwise, infer stop signs on all arms.
      • b. If there is more than one incoming non-local arm, then infer stop signs on all local arms
        • i. If there is only one local arm and another non-local arm that is a continuation of the local one, infer stop sign on the non-local continuation arm as well.
  • Case: T-intersection involving non-local arms only
      • a. If the two “thru road” arms (i.e., the two arms that are collinear) have a speed limit equal or higher than the “side” arm (i.e, the arm that is orthogonal to the thru road arms), infer stop sign on the side arm.
  • Case: Four-way intersection involving non-local roads only
      • a. If one road has a speed limit 10 mph or lower than the other, infer stop signs on the lower speed limit road only; otherwise infer stop signs on all arms.
  • Various other roadway and traffic control characteristics can be inferred in a similar manner. Consider traffic light characteristics, for example. Many traffic lights are synchronized along a roadway and are coordinated with one another such that vehicles traveling at a specified speed will be able to go through many intersections without encountering a red signal. In some implementations, the specified speed varies based on factors such as traffic congestion levels. There may be no database record indicating that certain lights are synchronized or what the synchronization scheme is. Likewise, certain intersections have phases of operation that vary based on congestion or time of day. A green left turn signal may appear before a general green signal, during the general green or after. Controller timing parameters may be programmed by a municipality based on any number of factors. It is not typical for municipalities to provide this detailed information about phasing or timing patters of traffic lights in a form readily usable by map databases. However, if the actual (i.e., real time) state information from each of the lights is available from the municipality, as is now often the case, the phasing and lighting patterns for each light and each intersection in general can be derived from historical analysis of the concurrent states of various lights, and in some instances comparison with other factors such as time of day, congestion levels and the like.
  • In one embodiment, phasing of various traffic signals is determined by comparison of real time state information among sets of adjacent traffic signals. This is particularly straightforward in the case of one-way streets.
  • In another embodiment, information from vehicles equipped with GPS and communications devices serves as a proxy for such real-time information about phasing. In some environments, sensor information (e.g., from inductive loops embedded in roadways) is available from municipalities that can likewise be used to track movement of individual vehicles over time and thus provide a basis for deriving traffic signal phasing information. For example, vehicles routinely “bunch” at red lights and the size of a particular bunch of vehicles can be used to track those vehicles as a group to determine whether they stop only rarely on a roadway with many signaled intersections (suggesting synchronized traffic lights) or stop fairly frequently and in a somewhat random temporal pattern (suggesting that the segment of roadway does not enjoy synchronized traffic lights).
  • In still another embodiment, some municipalities provide traffic phase mappings or related information (e.g., so-called “ring diagrams”) that likewise can be used to infer map database information. For example, a municipality may mark an intersection as subject to a generalized phase, such as “Phase 7—Pedestrian Fully Protected”. In this instance, inferences can be made that at least during certain time periods, stopping times will be increased for vehicles because they will be subject not only to red lights to allow orthogonally-directed traffic to pass, but also red lights to allow pedestrians to pass. Similarly, published information noting signaling configuration for “inbound commute” suggests potential use of center lane or breakdown lane restrictions that provide an extra lane in the inbound direction or synchronized lights favoring inbound traffic. All such information is available in various embodiments to be used as factors for derivation of map database information.
  • Those skilled in the art will readily recognize other algorithms that may be employed in other embodiments or environments to obtain reasonable map database inferences, for instance where traffic controls such as stop signs are likely to be located.
  • Thus, embodiments of the invention allow the capture of numerous additional types of information not previously reflected within the map database, leading to greater functionality and greater accuracy for the increasing number of map applications that rely on such information.
  • As used herein any reference to “one embodiment” or “an embodiment” means that a particular element, feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.
  • As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having” or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Further, unless expressly stated to the contrary, “or” refers to an inclusive or and not to an exclusive or. For example, a condition A or B is satisfied by any one of the following: A is true (or present) and B is false (or not present), A is false (or not present) and B is true (or present), and both A and B are true (or present).
  • In addition, the words “a” or “an” are employed to describe elements and components of the invention. This is done merely for convenience and to give a general sense of the invention. This description should be read to include one or at least one and the singular also includes the plural unless it is obvious that it is meant otherwise.
  • Certain aspects of the present invention include process steps and instructions described herein in the form of a method. It should be noted that the process steps and instructions of the present invention could be embodied in software, firmware or hardware.
  • The computer program for deriving additional information is preferably persistently stored in a computer readable storage medium, such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, application specific integrated circuits (ASICs), or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus.
  • Upon reading this disclosure, those of skill in the art will appreciate still additional alternative structural and functional designs for a system and a process for automated updating of a map database through the disclosed principles herein. Thus, while particular embodiments and applications have been illustrated and described, it is to be understood that the present invention is not limited to the precise construction and components disclosed herein and that various modifications, changes and variations which will be apparent to those skilled in the art may be made in the arrangement, operation and details of the method and apparatus of the present invention disclosed herein without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (20)

1. A system for updating a map database, comprising:
a data reception module configured to obtain a first roadway size characteristic associated with a first road and a second roadway size characteristic associated with a second road, the first road and the second road meeting at an intersection; and
an inference processor operably connected to the data reception module and configured to derive from the first roadway size characteristic and the second roadway size characteristic additional information not already stored within the map database and automatically update the map database to reflect the additional information, wherein the additional information denotes presence of a traffic control device.
2. A system, comprising:
a map database comprising information associated with roads;
an input subsystem configured to provide roadway characteristics;
an information inference module configured to derive from the roadway characteristics additional information not already stored within the map database and automatically update the map database to reflect the additional information.
3. The system of claim 2, wherein the information inference module is further configured to determine presence or absence of a traffic control based upon vehicle speeds on each of two roadways forming an intersection, in order to derive the additional information.
4. The system of claim 2, wherein the vehicle speeds are derived from GPS readings sent by vehicles.
5. The system of claim 2, wherein the vehicle speeds are derived from speed limits on each of the two roadways.
6. The system of claim 2, wherein the vehicle speeds are derived from operation of fixed roadway sensors.
7. The system of claim 2, wherein the information inference module is further configured to determine a type of a traffic control based upon vehicle speeds, in order to derive the additional information.
8. The system of claim 7, wherein the type includes at least one of the group consisting of: a stop sign, a traffic light, a synchronized traffic light, a yield sign, a turn restriction control, a lane restriction control, a railroad crossing control and a pedestrian crossing control.
9. The system of claim 3, wherein the traffic control includes at least one of the group consisting of: a stop sign, a traffic light, a synchronized traffic light, a yield sign, a turn restriction control, a lane restriction control, a railroad crossing control and a pedestrian crossing control.
10. The system of claim 2, wherein the roadway characteristics include at least one of the group consisting of: roadway classification, roadway historical traffic volume, traffic signal phasing, traffic signal state information, roadway sensor information.
11. The system of claim 1, wherein the additional information further denotes characteristics of the traffic control device.
12. A method for automatically updating a map database, comprising:
receiving map information from the map database, the map database comprising information associated with roads;
receiving roadway characteristic information;
deriving additional information not already stored within the map database responsive to the roadway characteristic information; and
updating the map database to reflect the derived additional information.
13. The method of claim 12, wherein deriving additional information comprises determining presence or absence of a traffic control based upon vehicle speeds on each of two roadways forming an intersection.
14. The method of claim 12, wherein the vehicle speeds are derived from GPS readings sent by vehicles.
15. The method of claim 12, wherein the vehicle speeds are derived from speed limits on each of the two roadways.
16. The method of claim 12, wherein the vehicle speeds are derived from operation of fixed roadway sensors.
17. The method of claim 12, wherein deriving additional information includes determining a type of a traffic control based upon vehicle speeds.
18. The method of claim 17, wherein the type includes at least one of the group consisting of: a stop sign, a traffic light, a synchronized traffic light, a yield sign, a turn restriction control, a lane restriction control, a railroad crossing control and a pedestrian crossing control.
19. The method of claim 12, wherein the traffic control includes at least one of the group consisting of: a stop sign, a traffic light, a synchronized traffic light, a yield sign, a turn restriction control, a lane restriction control, a railroad crossing control and a pedestrian crossing control.
20. The method of claim 12, wherein the roadway characteristics include at least one of the group consisting of: roadway classification, roadway historical traffic volume, traffic signal phasing, traffic signal state information, roadway sensor information.
US13/425,707 2007-09-07 2012-03-21 System and method for automated updating of map information Abandoned US20120179358A1 (en)

Priority Applications (6)

Application Number Priority Date Filing Date Title
US13/425,707 US20120179358A1 (en) 2007-09-07 2012-03-21 System and method for automated updating of map information
US13/542,938 US10083607B2 (en) 2007-09-07 2012-07-06 Driver safety enhancement using intelligent traffic signals and GPS
US13/747,145 US20130131980A1 (en) 2007-09-07 2013-01-22 Resolving gps ambiguity in electronic maps
US13/775,649 US20130166109A1 (en) 2007-09-07 2013-02-25 Driver Red Light Duration Notification System
US15/076,116 US9852624B2 (en) 2007-09-07 2016-03-21 Network security system with application for driver safety system
US15/822,715 US10311724B2 (en) 2007-09-07 2017-11-27 Network security system with application for driver safety system

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US11/851,953 US9043138B2 (en) 2007-09-07 2007-09-07 System and method for automated updating of map information
US13/425,707 US20120179358A1 (en) 2007-09-07 2012-03-21 System and method for automated updating of map information

Related Parent Applications (3)

Application Number Title Priority Date Filing Date
US11/851,953 Continuation-In-Part US9043138B2 (en) 2007-09-07 2007-09-07 System and method for automated updating of map information
US11/851,953 Continuation US9043138B2 (en) 2007-09-07 2007-09-07 System and method for automated updating of map information
US13/352,013 Continuation-In-Part US20120139754A1 (en) 2007-09-07 2012-01-17 Driver Safety Enhancement Using Intelligent Traffic Signals and GPS

Related Child Applications (2)

Application Number Title Priority Date Filing Date
US12/639,770 Continuation-In-Part US20110037619A1 (en) 2007-09-07 2009-12-16 Traffic Routing Using Intelligent Traffic Signals, GPS and Mobile Data Devices
US13/542,938 Continuation-In-Part US10083607B2 (en) 2007-09-07 2012-07-06 Driver safety enhancement using intelligent traffic signals and GPS

Publications (1)

Publication Number Publication Date
US20120179358A1 true US20120179358A1 (en) 2012-07-12

Family

ID=40432793

Family Applications (2)

Application Number Title Priority Date Filing Date
US11/851,953 Active - Reinstated 2030-12-20 US9043138B2 (en) 2007-09-07 2007-09-07 System and method for automated updating of map information
US13/425,707 Abandoned US20120179358A1 (en) 2007-09-07 2012-03-21 System and method for automated updating of map information

Family Applications Before (1)

Application Number Title Priority Date Filing Date
US11/851,953 Active - Reinstated 2030-12-20 US9043138B2 (en) 2007-09-07 2007-09-07 System and method for automated updating of map information

Country Status (1)

Country Link
US (2) US9043138B2 (en)

Cited By (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110196644A1 (en) * 2008-09-04 2011-08-11 Davidson Mark J Determining speed parameters in a geographic area
US8793046B2 (en) * 2012-06-01 2014-07-29 Google Inc. Inferring state of traffic signal and other aspects of a vehicle's environment based on surrogate data
WO2016016760A1 (en) * 2014-07-29 2016-02-04 Here Global B.V. An apparatus and associated methods for designating a traffic lane
US9396657B1 (en) 2013-04-12 2016-07-19 Traffic Technology Solutions, LLC Prediction of traffic signal state changes
US9610893B2 (en) 2015-03-18 2017-04-04 Car1St Technologies, Llc Methods and systems for providing alerts to a driver of a vehicle via condition detection and wireless communications
US20170350712A1 (en) * 2016-06-03 2017-12-07 Denso Corporation Apparatus for identifying position of own vehicle and method for identifying position of own vehicle
US9928738B2 (en) 2013-04-12 2018-03-27 Traffic Technology Services, Inc. Red light warning system based on predictive traffic signal state data
US10008113B2 (en) 2013-04-12 2018-06-26 Traffic Technology Services, Inc. Hybrid distributed prediction of traffic signal state changes
US10328855B2 (en) 2015-03-18 2019-06-25 Uber Technologies, Inc. Methods and systems for providing alerts to a connected vehicle driver and/or a passenger via condition detection and wireless communications
US10453004B2 (en) 2008-09-04 2019-10-22 United Parcel Service Of America, Inc. Vehicle routing and scheduling systems
US10678776B1 (en) * 2010-10-06 2020-06-09 Google Llc Automated identification of anomalous map data
US11024165B2 (en) 2016-01-11 2021-06-01 NetraDyne, Inc. Driver behavior monitoring
US20210284195A1 (en) * 2020-03-13 2021-09-16 Baidu Usa Llc Obstacle prediction system for autonomous driving vehicles
US11314209B2 (en) 2017-10-12 2022-04-26 NetraDyne, Inc. Detection of driving actions that mitigate risk
US11322018B2 (en) 2016-07-31 2022-05-03 NetraDyne, Inc. Determining causation of traffic events and encouraging good driving behavior
US11840239B2 (en) 2017-09-29 2023-12-12 NetraDyne, Inc. Multiple exposure event determination

Families Citing this family (52)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9852624B2 (en) 2007-09-07 2017-12-26 Connected Signals, Inc. Network security system with application for driver safety system
US20110037619A1 (en) * 2009-08-11 2011-02-17 On Time Systems, Inc. Traffic Routing Using Intelligent Traffic Signals, GPS and Mobile Data Devices
US10083607B2 (en) 2007-09-07 2018-09-25 Green Driver, Inc. Driver safety enhancement using intelligent traffic signals and GPS
US20110037618A1 (en) * 2009-08-11 2011-02-17 Ginsberg Matthew L Driver Safety System Using Machine Learning
US9043138B2 (en) * 2007-09-07 2015-05-26 Green Driver, Inc. System and method for automated updating of map information
RU2010123016A (en) * 2007-11-06 2011-12-20 Теле Атлас Норт Америка Инк. (Us) METHOD AND SYSTEM FOR USING MEASUREMENT DATA FROM MANY VEHICLES FOR DETECTING REAL-WORLD CHANGES FOR USE WHEN MAP UPDATES
US8762035B2 (en) 2008-05-19 2014-06-24 Waze Mobile Ltd. System and method for realtime community information exchange
US8612136B2 (en) * 2008-08-27 2013-12-17 Waze Mobile Ltd. System and method for road map creation
US9683850B2 (en) * 2009-02-03 2017-06-20 Telenav, Inc. Method for navigation using adaptive coverage
US8271057B2 (en) * 2009-03-16 2012-09-18 Waze Mobile Ltd. Condition-based activation, shut-down and management of applications of mobile devices
US8953838B2 (en) * 2009-06-24 2015-02-10 Here Global B.V. Detecting ground geographic features in images based on invariant components
US9129163B2 (en) * 2009-06-24 2015-09-08 Here Global B.V. Detecting common geographic features in images based on invariant components
US8761435B2 (en) * 2009-06-24 2014-06-24 Navteq B.V. Detecting geographic features in images based on invariant components
US9291463B2 (en) * 2009-08-03 2016-03-22 Tomtom North America, Inc. Method of verifying or deriving attribute information of a digital transport network database using interpolation and probe traces
US10198942B2 (en) 2009-08-11 2019-02-05 Connected Signals, Inc. Traffic routing display system with multiple signal lookahead
US8340894B2 (en) * 2009-10-08 2012-12-25 Honda Motor Co., Ltd. Method of dynamic intersection mapping
US8818641B2 (en) 2009-12-18 2014-08-26 Honda Motor Co., Ltd. Method of intersection estimation for a vehicle safety system
JP5471626B2 (en) * 2010-03-09 2014-04-16 ソニー株式会社 Information processing apparatus, map update method, program, and information processing system
US8823556B2 (en) 2010-09-02 2014-09-02 Honda Motor Co., Ltd. Method of estimating intersection control
US8618951B2 (en) 2010-09-17 2013-12-31 Honda Motor Co., Ltd. Traffic control database and distribution system
US9002545B2 (en) 2011-01-07 2015-04-07 Wabtec Holding Corp. Data improvement system and method
US8618952B2 (en) 2011-01-21 2013-12-31 Honda Motor Co., Ltd. Method of intersection identification for collision warning system
US8587453B2 (en) * 2011-04-13 2013-11-19 Jeffrey L. Cripps Portable traffic signaling system
SE1100537A1 (en) * 2011-07-15 2013-01-16 Scania Cv Ab Handling errors in map data
US8493198B1 (en) 2012-07-11 2013-07-23 Google Inc. Vehicle and mobile device traffic hazard warning techniques
DE102012216788A1 (en) 2012-09-19 2014-05-28 Bayerische Motoren Werke Aktiengesellschaft Method for obtaining quality data relating to information of switching times/conditions of traffic lights and/or variable message signs, involves comparing actual and expected states of traffic lights and/or variable message signs
US20140143184A1 (en) * 2012-11-21 2014-05-22 Microsoft Corporation Turn restriction inferencing
DE102013205392A1 (en) 2013-03-27 2014-10-02 Bayerische Motoren Werke Aktiengesellschaft Backend for driver assistance systems
CN105224582B (en) * 2014-07-03 2018-11-09 联想(北京)有限公司 Information processing method and equipment
US10088322B2 (en) * 2014-12-16 2018-10-02 Ford Global Technologies, Llc Traffic control device detection
DE102015212027A1 (en) * 2015-06-29 2016-12-29 Bayerische Motoren Werke Aktiengesellschaft Method and device for automatic determination of traffic regulations at road intersections
FR3041105A1 (en) * 2015-09-14 2017-03-17 Peugeot Citroen Automobiles Sa METHOD FOR EVALUATING ENERGY DEMAND OF A VEHICLE
US10126136B2 (en) 2016-06-14 2018-11-13 nuTonomy Inc. Route planning for an autonomous vehicle
US10309792B2 (en) 2016-06-14 2019-06-04 nuTonomy Inc. Route planning for an autonomous vehicle
US11092446B2 (en) 2016-06-14 2021-08-17 Motional Ad Llc Route planning for an autonomous vehicle
US10681513B2 (en) 2016-10-20 2020-06-09 nuTonomy Inc. Identifying a stopping place for an autonomous vehicle
US10473470B2 (en) 2016-10-20 2019-11-12 nuTonomy Inc. Identifying a stopping place for an autonomous vehicle
US10857994B2 (en) 2016-10-20 2020-12-08 Motional Ad Llc Identifying a stopping place for an autonomous vehicle
US10331129B2 (en) 2016-10-20 2019-06-25 nuTonomy Inc. Identifying a stopping place for an autonomous vehicle
WO2018157541A1 (en) * 2017-03-01 2018-09-07 华为技术有限公司 Method and device for drawing roads in electronic map
DE102017217299A1 (en) * 2017-09-28 2019-03-28 Continental Automotive Gmbh Method and device
DE112017008262T5 (en) * 2017-12-12 2020-08-20 Mitsubishi Electric Corporation Map update apparatus, map update system and map update method
CN110309237A (en) * 2018-03-05 2019-10-08 北京京东尚科信息技术有限公司 A kind of method and apparatus updating map
US10783389B2 (en) 2018-08-02 2020-09-22 Denso International America, Inc. Systems and methods for avoiding misrecognition of traffic signs and signals by hacking
CN109387208B (en) * 2018-11-13 2021-03-19 百度在线网络技术(北京)有限公司 Map data processing method, device, equipment and medium
JP7044038B2 (en) * 2018-11-21 2022-03-30 トヨタ自動車株式会社 Map information system
US11249984B2 (en) * 2019-05-22 2022-02-15 Here Global B.V. System and method for updating map data in a map database
JP2020193955A (en) * 2019-05-30 2020-12-03 クラリオン株式会社 Driving information provision system, on-vehicle device, and method for providing driving information
US11521398B2 (en) * 2019-11-26 2022-12-06 GM Global Technology Operations LLC Method and apparatus for traffic light positioning and mapping using crowd-sensed data
CN111854771B (en) * 2020-06-09 2023-01-24 阿波罗智能技术(北京)有限公司 Map quality detection processing method and device, electronic equipment and storage medium
SG10202007346XA (en) 2020-08-01 2020-10-29 Grabtaxi Holdings Pte Ltd Processing apparatus and method for generating route navigation data
CN112330827B (en) * 2020-10-13 2022-09-13 北京精英路通科技有限公司 Parking charging method and device

Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5999878A (en) * 1997-04-11 1999-12-07 Navigation Technologies Corp. System and method for acquiring geographic data for forming a digital database of road geometry in a geographic region
US6047234A (en) * 1997-10-16 2000-04-04 Navigation Technologies Corporation System and method for updating, enhancing or refining a geographic database using feedback
US6173232B1 (en) * 1997-07-08 2001-01-09 Aisin Aw Co., Ltd. Vehicle navigation system and a recording medium
US20010029425A1 (en) * 2000-03-17 2001-10-11 David Myr Real time vehicle guidance and traffic forecasting system
US6338021B1 (en) * 1999-09-29 2002-01-08 Matsushita Electric Industrial Co., Ltd. Route selection method and system
US6343301B1 (en) * 1999-02-24 2002-01-29 Navigation Technologies Corp. Method and system for collecting data for updating a geographic database
US6353785B1 (en) * 1999-03-12 2002-03-05 Navagation Technologies Corp. Method and system for an in-vehicle computer architecture
US6526352B1 (en) * 2001-07-19 2003-02-25 Intelligent Technologies International, Inc. Method and arrangement for mapping a road
US6850841B1 (en) * 2003-05-15 2005-02-01 Navtech North American, Llc Method and system for obtaining lane data
US20060282214A1 (en) * 2005-06-09 2006-12-14 Toyota Technical Center Usa, Inc. Intelligent navigation system
US20100094583A1 (en) * 2006-10-05 2010-04-15 Claudio Borean Sensor network
US8576069B2 (en) * 2009-10-22 2013-11-05 Siemens Corporation Mobile sensing for road safety, traffic management, and road maintenance

Family Cites Families (180)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA1235782A (en) 1984-05-09 1988-04-26 Kazuo Sato Apparatus for calculating position of vehicle
JPS61137009A (en) * 1984-12-07 1986-06-24 Nissan Motor Co Ltd Position measuring apparatus for vehicle
NL8503378A (en) * 1985-12-06 1987-07-01 Theo Jogchum Poelstra NEW METHOD FOR BUILDING AND TRACKING DATA FILES FOR ROAD TRAFFIC.
JPS63196812A (en) 1987-02-10 1988-08-15 Yazaki Corp Vehicle position display apparatus
KR910004416B1 (en) * 1987-03-13 1991-06-27 미쓰비시덴기 가부시기가이샤 Navigator
JP2680318B2 (en) * 1987-12-28 1997-11-19 アイシン・エィ・ダブリュ株式会社 Navigation device
US5060162A (en) 1988-12-09 1991-10-22 Matsushita Electric Industrial Co., Ltd. Vehicle in-situ locating apparatus
US5087919A (en) * 1989-09-05 1992-02-11 Pioneer Electronic Corporation On-board navigation apparatus
JP2536190B2 (en) 1989-10-24 1996-09-18 三菱電機株式会社 Navigation device for mobile
US5179519A (en) * 1990-02-01 1993-01-12 Pioneer Electronic Corporation Navigation system for vehicle
US5390125A (en) * 1990-02-05 1995-02-14 Caterpillar Inc. Vehicle position determination system and method
US5469360A (en) 1991-03-10 1995-11-21 Matsushita Electric Industrial Co., Ltd. Vehicle position detecting apparatus
JP2785511B2 (en) * 1991-03-28 1998-08-13 日産自動車株式会社 Congested road display device for vehicles
US5272638A (en) * 1991-05-31 1993-12-21 Texas Instruments Incorporated Systems and methods for planning the scheduling travel routes
JPH0739960B2 (en) * 1991-06-18 1995-05-01 住友電気工業株式会社 Position detector
US6738697B2 (en) 1995-06-07 2004-05-18 Automotive Technologies International Inc. Telematics system for vehicle diagnostics
JP3267310B2 (en) 1991-07-10 2002-03-18 パイオニア株式会社 GPS navigation device
JPH0579846A (en) * 1991-09-19 1993-03-30 Matsushita Electric Ind Co Ltd Vehicle position calculator
DE69214092T2 (en) 1991-10-29 1997-04-03 Philips Electronics Nv Navigation device and car with such a device
EP0716315A1 (en) 1992-04-20 1996-06-12 Sumitomo Electric Industries, Ltd. Vehicle heading correcting apparatus
US6144916A (en) * 1992-05-15 2000-11-07 Micron Communications, Inc. Itinerary monitoring system for storing a plurality of itinerary data points
DE69326799T2 (en) 1992-07-23 2000-04-27 Aisin Aw Co Navigation device for vehicles to determine a new route if a vehicle deviates from its route
US5530651A (en) * 1992-08-03 1996-06-25 Mazda Motor Corporation Running-safety system for an automotive vehicle
JP3221746B2 (en) 1992-10-14 2001-10-22 パイオニア株式会社 Navigation device
JP3157923B2 (en) * 1992-10-20 2001-04-23 パイオニア株式会社 Distance error correction method for navigation device
US5374933A (en) 1993-01-05 1994-12-20 Zexel Corporation Position correction method for vehicle navigation system
DE4310531C2 (en) * 1993-03-31 1997-02-13 Preh Elektro Feinmechanik Device for the transmission of information in motor vehicle traffic
US5416712A (en) * 1993-05-28 1995-05-16 Trimble Navigation Limited Position and velocity estimation system for adaptive weighting of GPS and dead-reckoning information
JPH06347278A (en) * 1993-06-10 1994-12-20 Alpine Electron Inc Existent link detection method of vehicle
US5504482A (en) * 1993-06-11 1996-04-02 Rockwell International Corporation Automobile navigation guidance, control and safety system
US5488559A (en) * 1993-08-02 1996-01-30 Motorola, Inc. Map-matching with competing sensory positions
JP3223220B2 (en) * 1993-10-28 2001-10-29 本田技研工業株式会社 Vehicle intercommunication device
JP3488969B2 (en) 1994-03-09 2004-01-19 本田技研工業株式会社 Vehicle guidance device
DE4413886C1 (en) 1994-04-21 1995-04-06 Leica Ag System for detecting (collecting) traffic information in vehicles
US5848373A (en) 1994-06-24 1998-12-08 Delorme Publishing Company Computer aided map location system
US5485161A (en) * 1994-11-21 1996-01-16 Trimble Navigation Limited Vehicle speed control based on GPS/MAP matching of posted speeds
JP3483962B2 (en) * 1994-12-05 2004-01-06 株式会社ザナヴィ・インフォマティクス Navigation equipment
NO944954D0 (en) * 1994-12-20 1994-12-20 Geco As Procedure for integrity monitoring in position determination
DE69535394T2 (en) * 1994-12-28 2007-10-31 Omron Corp. Traffic Information System
JPH08269921A (en) 1995-04-03 1996-10-15 Hitachi Vlsi Eng Corp Detecting system for road-sign or the like and automobile
DE19516476A1 (en) * 1995-05-05 1996-11-07 Bosch Gmbh Robert Device for informing a driver
US5608391A (en) * 1995-05-11 1997-03-04 Minnesota Mining And Manufacturing Company Electronic license plate architecture
US6768944B2 (en) 2002-04-09 2004-07-27 Intelligent Technologies International, Inc. Method and system for controlling a vehicle
US5941934A (en) 1995-06-09 1999-08-24 Xanavi Informatics Corporation Current position calculating device
JP3545839B2 (en) 1995-06-09 2004-07-21 株式会社ザナヴィ・インフォマティクス Current position calculation device
FI101118B (en) 1995-06-29 1998-04-15 Ericsson Telefon Ab L M Mobile network traffic management
DE19525291C1 (en) * 1995-07-03 1996-12-19 Mannesmann Ag Method and device for updating digital road maps
US5774824A (en) * 1995-08-24 1998-06-30 The Penn State Research Foundation Map-matching navigation system
JP3448134B2 (en) * 1995-08-25 2003-09-16 アイシン・エィ・ダブリュ株式会社 Vehicle navigation system
KR960042490A (en) * 1995-11-09 1996-12-21 모리 하루오 Vehicle navigation device and recording medium therefor
JP2902340B2 (en) 1995-12-28 1999-06-07 アルパイン株式会社 Vehicle position correction method
US5862511A (en) * 1995-12-28 1999-01-19 Magellan Dis, Inc. Vehicle navigation system and method
US5951620A (en) * 1996-01-26 1999-09-14 Navigation Technologies Corporation System and method for distributing information for storage media
US5771484A (en) * 1996-02-28 1998-06-23 Sun Microsystems, Inc. Automated positive control traffic system for weather
US5847661A (en) 1996-03-15 1998-12-08 Intelligent Ideation, Inc. Vehicle data acquisition system
JPH09297030A (en) 1996-05-02 1997-11-18 Pioneer Electron Corp Method and device for calculating moving body position, and method and device for correcting moving body position
US5699986A (en) * 1996-07-15 1997-12-23 Alternative Safety Technologies Railway crossing collision avoidance system
US5925090A (en) 1996-08-16 1999-07-20 Alpine Electronics, Inc. Sign text display method and apparatus for vehicle navigation system
KR100278972B1 (en) * 1996-08-21 2001-01-15 모리 하루오 Navigation device
JP3919855B2 (en) * 1996-10-17 2007-05-30 株式会社ザナヴィ・インフォマティクス Navigation device
US5987378A (en) * 1996-10-24 1999-11-16 Trimble Navigation Limited Vehicle tracker mileage-time monitor and calibrator
US5982298A (en) * 1996-11-14 1999-11-09 Microsoft Corporation Interactive traffic display and trip planner
JP3220408B2 (en) * 1997-03-31 2001-10-22 富士通テン株式会社 Route guidance device
EP0922201B1 (en) * 1997-07-01 2002-09-11 Siemens Aktiengesellschaft Navigation system for use in a vehicle
JPH1153686A (en) 1997-07-31 1999-02-26 Toyota Motor Corp Intersection warning device
JP3603927B2 (en) * 1997-08-08 2004-12-22 アイシン・エィ・ダブリュ株式会社 Vehicle navigation device and navigation method
DE19736774A1 (en) 1997-08-23 1999-02-25 Bosch Gmbh Robert Information display method in vehicle
US5959577A (en) * 1997-08-28 1999-09-28 Vectorlink, Inc. Method and structure for distribution of travel information using network
US5973639A (en) * 1997-09-23 1999-10-26 Trimble Navigation Limited Global positioning system having postprocessed realtime corrected data
US6381533B1 (en) * 1997-10-16 2002-04-30 Navigation Technologies Corp. Method and system using positions of cellular phones matched to road network for collecting data
US6008740A (en) 1997-12-17 1999-12-28 Stmicroelectronics, Inc. Electronic speed limit notification system
US6252544B1 (en) * 1998-01-27 2001-06-26 Steven M. Hoffberg Mobile communication device
US6057785A (en) * 1998-03-18 2000-05-02 Guthrie; Donald A. Vehicle warning sign system
US6553308B1 (en) * 1999-04-29 2003-04-22 Donnelly Corporation Vehicle-based navigation system with smart map filtering, portable unit home-base registration and multiple navigation system preferential use
US20010001848A1 (en) * 1998-06-25 2001-05-24 Hidekazu Oshizawa Vehicle navigation system providing traffic advisories based on traffic information and learned route
JP3495258B2 (en) * 1998-07-09 2004-02-09 三菱電機株式会社 Traffic information providing device
JP2000046574A (en) * 1998-07-24 2000-02-18 Honda Motor Co Ltd Navigation device for vehicle
DE19852631C2 (en) 1998-11-14 2001-09-06 Daimler Chrysler Ag Device and method for traffic sign recognition
US6150961A (en) * 1998-11-24 2000-11-21 International Business Machines Corporation Automated traffic mapping
US6333703B1 (en) * 1998-11-24 2001-12-25 International Business Machines Corporation Automated traffic mapping using sampling and analysis
US6351709B2 (en) * 1998-12-02 2002-02-26 Lear Automotive Dearborn, Inc. Vehicle navigation system with route updating feature
US6246948B1 (en) * 1998-12-10 2001-06-12 Ericsson Inc. Wireless intelligent vehicle speed control or monitoring system and method
DE19904909C2 (en) * 1999-02-06 2003-10-30 Daimler Chrysler Ag Method and device for providing traffic information
WO2000062019A1 (en) * 1999-04-07 2000-10-19 Mitsubishi Denki Kabushiki Kaisha Navigator
US6466862B1 (en) 1999-04-19 2002-10-15 Bruce DeKock System for providing traffic information
US6122593A (en) * 1999-08-03 2000-09-19 Navigation Technologies Corporation Method and system for providing a preview of a route calculated with a navigation system
US6317058B1 (en) 1999-09-15 2001-11-13 Jerome H. Lemelson Intelligent traffic control and warning system and method
US6490519B1 (en) * 1999-09-27 2002-12-03 Decell, Inc. Traffic monitoring system and methods for traffic monitoring and route guidance useful therewith
US6360165B1 (en) * 1999-10-21 2002-03-19 Visteon Technologies, Llc Method and apparatus for improving dead reckoning distance calculation in vehicle navigation system
US6516273B1 (en) * 1999-11-04 2003-02-04 Veridian Engineering, Inc. Method and apparatus for determination and warning of potential violation of intersection traffic control devices
US7162367B2 (en) * 1999-11-29 2007-01-09 American Gnc Corporation Self-contained/interruption-free positioning method and system thereof
US7382274B1 (en) 2000-01-21 2008-06-03 Agere Systems Inc. Vehicle interaction communication system
JP4024450B2 (en) 2000-03-03 2007-12-19 パイオニア株式会社 Navigation system
US6317685B1 (en) 2000-03-13 2001-11-13 Navigation Technologies Corp. Method and system for providing alternate routes with a navigation system
KR100335906B1 (en) 2000-06-08 2002-05-08 이계안 System for controlling speed according to traffic signal of vehicle
DE10029198A1 (en) 2000-06-19 2001-12-20 Bosch Gmbh Robert Selecting map information for navigation device involves starting from defined polygonal course on digital map; selected map information also contains parameter-dependent route corridor
WO2002001532A1 (en) * 2000-06-26 2002-01-03 Custom Traffic Pty Ltd Method and system for providing traffic and related information
US6317686B1 (en) * 2000-07-21 2001-11-13 Bin Ran Method of providing travel time
US6675085B2 (en) * 2000-08-17 2004-01-06 Michael P. Straub Method and apparatus for storing, accessing, generating and using information about speed limits and speed traps
US6941220B2 (en) 2000-09-12 2005-09-06 Center Comm Corporation Apparatus and method for vehicle navigation
JP2002123894A (en) * 2000-10-16 2002-04-26 Hitachi Ltd Method and apparatus for controlling probe car and traffic control system using probe car
US6801850B1 (en) * 2000-10-30 2004-10-05 University Of Illionis - Chicago Method and system for tracking moving objects
US6603405B2 (en) * 2000-12-05 2003-08-05 User-Centric Enterprises, Inc. Vehicle-centric weather prediction system and method
JP5041638B2 (en) * 2000-12-08 2012-10-03 パナソニック株式会社 Method for transmitting location information of digital map and device used therefor
US6741933B1 (en) * 2000-12-27 2004-05-25 Advanced Tracking Technologies, Inc. Travel tracker
US6463382B1 (en) * 2001-02-26 2002-10-08 Motorola, Inc. Method of optimizing traffic content
US6515596B2 (en) * 2001-03-08 2003-02-04 International Business Machines Corporation Speed limit display in a vehicle
JP4417583B2 (en) * 2001-05-08 2010-02-17 パイオニア株式会社 Navigation device
US6615135B2 (en) 2001-05-24 2003-09-02 Prc Inc. Satellite based on-board vehicle navigation system including predictive filtering and map-matching to reduce errors in a vehicular position
US6577946B2 (en) 2001-07-10 2003-06-10 Makor Issues And Rights Ltd. Traffic information gathering via cellular phone networks for intelligent transportation systems
US6539300B2 (en) * 2001-07-10 2003-03-25 Makor Issues And Rights Ltd. Method for regional system wide optimal signal timing for traffic control based on wireless phone networks
US20030016143A1 (en) * 2001-07-23 2003-01-23 Ohanes Ghazarian Intersection vehicle collision avoidance system
US6604047B1 (en) 2001-08-03 2003-08-05 Scott C. Harris Non real time traffic system for a navigator
ATE280424T1 (en) 2001-08-29 2004-11-15 Siemens Ag METHOD AND ARRANGEMENT FOR CONTROLLING A SYSTEM OF SEVERAL TRAFFIC SIGNALS
US6587785B2 (en) * 2001-09-21 2003-07-01 General Motors Corporation Method and system for mobile vehicle re-routing
US6621420B1 (en) 2001-11-29 2003-09-16 Siavash Poursartip Device and method for integrated wireless transit and emergency vehicle management
US6992598B2 (en) * 2002-01-10 2006-01-31 Poltorak Alexander I Apparatus and method for providing travel information
US6751549B1 (en) * 2002-01-17 2004-06-15 Navigation Technologies Corporation Method and system for route calculation that avoids railroad crossings
US7221287B2 (en) * 2002-03-05 2007-05-22 Triangle Software Llc Three-dimensional traffic report
US6708107B2 (en) * 2002-04-02 2004-03-16 Lockheed Martin Corporation Real-time ad hoc traffic alert distribution
US8102253B1 (en) * 2002-06-27 2012-01-24 Earthcomber, Llc System and method for notifying a user of people, places or things having attributes matching a user's stated preference
US8427303B1 (en) * 2002-06-27 2013-04-23 Geomass Limited Liability Company System and method for providing media content having attributes matching a user's stated preference
IL150894A0 (en) 2002-07-24 2003-04-10 A method for measuring road traffic load based on analyzing cellular communications
US7499949B2 (en) * 2002-08-07 2009-03-03 Navteq North America, Llc Method and system for obtaining recurring delay data using navigation systems
US7433889B1 (en) * 2002-08-07 2008-10-07 Navteq North America, Llc Method and system for obtaining traffic sign data using navigation systems
KR100495635B1 (en) 2002-09-02 2005-06-16 엘지전자 주식회사 Method for correcting position error in navigation system
US6711493B1 (en) * 2002-12-09 2004-03-23 International Business Machines Corporation Method and apparatus for collecting and propagating information relating to traffic conditions
US7818116B1 (en) 2002-12-30 2010-10-19 Mapquest, Inc. Presenting a travel route in a ground-based vehicle
WO2004066241A1 (en) * 2003-01-17 2004-08-05 Siemens Vdo Automotive Corporation Traffic signal priority system based on mobile event
US7239962B2 (en) 2003-02-21 2007-07-03 Sony Corporation Method and apparatus for a routing agent
JP4062148B2 (en) 2003-03-27 2008-03-19 株式会社日立製作所 Mobile terminal and information providing system using the same
CA2531662C (en) * 2003-07-07 2016-04-26 Sensomatix Ltd. Traffic information system
ES2293004T3 (en) 2003-08-21 2008-03-16 Stichting Noble House MODULAR TRAFFIC INFORMATION SYSTEM.
US7079946B2 (en) 2003-08-29 2006-07-18 Denso Corporation Iterative logical renewal of navigable map database
US7053780B1 (en) * 2003-09-30 2006-05-30 Garmin Ltd. Methods, systems, and devices for location specific alerts
US6989766B2 (en) * 2003-12-23 2006-01-24 International Business Machines Corporation Smart traffic signal system
US20050187701A1 (en) 2004-02-23 2005-08-25 Baney Douglas M. Traffic communication system
US7983835B2 (en) * 2004-11-03 2011-07-19 Lagassey Paul J Modular intelligent transportation system
US7366606B2 (en) * 2004-04-06 2008-04-29 Honda Motor Co., Ltd. Method for refining traffic flow data
US20050283312A1 (en) 2004-06-16 2005-12-22 Daimlerchrysler Ag Method and device for improving GPS-based positioning of vehicles on roads
US7835859B2 (en) * 2004-10-29 2010-11-16 Aol Inc. Determining a route to a destination based on partially completed route
US7522940B2 (en) * 2004-11-16 2009-04-21 Sony Ericsson Mobile Communications Ab Methods and mobile terminals for display of mobile terminal density information
JP2006242911A (en) 2005-03-07 2006-09-14 Denso Corp Position detector
US20060265294A1 (en) 2005-05-23 2006-11-23 De Sylva Robert F System and method for facilitating tasks involving travel between locations
US7432826B2 (en) 2005-06-16 2008-10-07 Global Traffic Technologies, Llc Traffic preemption system with headway management
US7589643B2 (en) * 2005-06-30 2009-09-15 Gm Global Technology Operations, Inc. Vehicle speed monitoring system
JP4531646B2 (en) * 2005-07-01 2010-08-25 株式会社デンソー Navigation system and coefficient determination program used for the navigation system
WO2007008837A2 (en) 2005-07-08 2007-01-18 Idaho Research Foundation, Inc. Distributed intelligence for traffic signal control
JP4605051B2 (en) * 2005-07-22 2011-01-05 株式会社デンソー Navigation device
DE102005041066A1 (en) * 2005-08-30 2007-03-15 Siemens Ag Method and device for automatic generation of traffic management strategies
US20070103341A1 (en) * 2005-11-04 2007-05-10 Kreiner Barrett M Multifacted monitoring
US7813870B2 (en) 2006-03-03 2010-10-12 Inrix, Inc. Dynamic time series prediction of future traffic conditions
US7466227B2 (en) 2006-03-17 2008-12-16 Alcatel-Lucent Usa Inc. Location based vehicle traffic signal alert system
KR101060320B1 (en) 2006-03-21 2011-08-29 스카이메터 코포레이션 Private, auditable vehicle positioning system and on-board unit for it
JP4730165B2 (en) * 2006-03-27 2011-07-20 株式会社デンソー Traffic information management system
CN101093168B (en) 2006-06-21 2010-08-25 北京腾瑞万里信息技术有限公司 Navigation system, and navigation method
US7706964B2 (en) * 2006-06-30 2010-04-27 Microsoft Corporation Inferring road speeds for context-sensitive routing
JP4950586B2 (en) 2006-08-02 2012-06-13 クラリオン株式会社 Statistical traffic information generation method and statistical traffic information generation device
JP4899756B2 (en) * 2006-09-29 2012-03-21 アイシン・エィ・ダブリュ株式会社 Traffic information creation device and traffic information creation method
US20080088479A1 (en) * 2006-10-13 2008-04-17 Toyota Engineering & Manufacturing North America, Inc. Traffic light warning method and system
US9076332B2 (en) * 2006-10-19 2015-07-07 Makor Issues And Rights Ltd. Multi-objective optimization for real time traffic light control and navigation systems for urban saturated networks
US7755510B2 (en) 2007-01-22 2010-07-13 Mergex Traffic Systems Corporation Intelligent system for managing vehicular traffic flow
US20080249713A1 (en) 2007-04-03 2008-10-09 Darren Brett Sessions Gps position accuracy using feedback from a map database
KR101467557B1 (en) 2007-05-02 2014-12-10 엘지전자 주식회사 Selecting Route According To Traffic Information
US20090005984A1 (en) 2007-05-31 2009-01-01 James Roy Bradley Apparatus and method for transit prediction
US7912637B2 (en) * 2007-06-25 2011-03-22 Microsoft Corporation Landmark-based routing
US20090051568A1 (en) * 2007-08-21 2009-02-26 Kevin Michael Corry Method and apparatus for traffic control using radio frequency identification tags
US10083607B2 (en) 2007-09-07 2018-09-25 Green Driver, Inc. Driver safety enhancement using intelligent traffic signals and GPS
US20120139754A1 (en) 2009-08-11 2012-06-07 Ginsberg Matthew L Driver Safety Enhancement Using Intelligent Traffic Signals and GPS
US9043138B2 (en) * 2007-09-07 2015-05-26 Green Driver, Inc. System and method for automated updating of map information
US20090088965A1 (en) * 2007-10-02 2009-04-02 International Business Machines Corporation Enhancement for navigation systems for using weather information when predicting a quickest travel path
KR100986372B1 (en) * 2007-11-28 2010-10-08 현대자동차주식회사 Terminal for Collecting Traffic Information and Method for Generating Traffic Information
JP2009245326A (en) 2008-03-31 2009-10-22 Toyota Central R&D Labs Inc Signal information reporting apparatus
US20100070128A1 (en) 2008-09-15 2010-03-18 Microsoft Corporation vehicle operation by leveraging traffic related data
US20100145587A1 (en) 2008-11-07 2010-06-10 Myunghee Son Method and apparatus for managing traffic information using light communication
US8255151B2 (en) 2008-12-09 2012-08-28 Motorola Mobility Llc Method and system for providing environmentally-optimized navigation routes
US8040254B2 (en) 2009-01-06 2011-10-18 International Business Machines Corporation Method and system for controlling and adjusting traffic light timing patterns
JP5051283B2 (en) * 2010-08-02 2012-10-17 株式会社デンソー Engine automatic control system
US8717192B2 (en) * 2010-10-08 2014-05-06 Navteq B.V. Method and system for using intersecting electronic horizons
US9069653B2 (en) 2012-05-04 2015-06-30 Ford Global Technologies, Llc Methods for utilizing stop sign and traffic light detections to enhance fuel economy and safety

Patent Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5999878A (en) * 1997-04-11 1999-12-07 Navigation Technologies Corp. System and method for acquiring geographic data for forming a digital database of road geometry in a geographic region
US6173232B1 (en) * 1997-07-08 2001-01-09 Aisin Aw Co., Ltd. Vehicle navigation system and a recording medium
US6516267B1 (en) * 1997-10-16 2003-02-04 Navigation Technologies Corporation System and method for updating, enhancing or refining a geographic database using feedback
US6047234A (en) * 1997-10-16 2000-04-04 Navigation Technologies Corporation System and method for updating, enhancing or refining a geographic database using feedback
US6343301B1 (en) * 1999-02-24 2002-01-29 Navigation Technologies Corp. Method and system for collecting data for updating a geographic database
US6353785B1 (en) * 1999-03-12 2002-03-05 Navagation Technologies Corp. Method and system for an in-vehicle computer architecture
US6338021B1 (en) * 1999-09-29 2002-01-08 Matsushita Electric Industrial Co., Ltd. Route selection method and system
US20010029425A1 (en) * 2000-03-17 2001-10-11 David Myr Real time vehicle guidance and traffic forecasting system
US6526352B1 (en) * 2001-07-19 2003-02-25 Intelligent Technologies International, Inc. Method and arrangement for mapping a road
US6850841B1 (en) * 2003-05-15 2005-02-01 Navtech North American, Llc Method and system for obtaining lane data
US20060282214A1 (en) * 2005-06-09 2006-12-14 Toyota Technical Center Usa, Inc. Intelligent navigation system
US20100094583A1 (en) * 2006-10-05 2010-04-15 Claudio Borean Sensor network
US8576069B2 (en) * 2009-10-22 2013-11-05 Siemens Corporation Mobile sensing for road safety, traffic management, and road maintenance

Cited By (35)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110196644A1 (en) * 2008-09-04 2011-08-11 Davidson Mark J Determining speed parameters in a geographic area
US9128809B2 (en) * 2008-09-04 2015-09-08 United Parcel Service Of America, Inc. Determining speed parameters in a geographic area
US10453004B2 (en) 2008-09-04 2019-10-22 United Parcel Service Of America, Inc. Vehicle routing and scheduling systems
US10678776B1 (en) * 2010-10-06 2020-06-09 Google Llc Automated identification of anomalous map data
US9804601B2 (en) 2012-06-01 2017-10-31 Waymo Llc Inferring state of traffic signal and other aspects of a vehicle's environment based on surrogate data
US11845472B2 (en) 2012-06-01 2023-12-19 Waymo Llc Inferring state of traffic signal and other aspects of a vehicle's environment based on surrogate data
US9327734B2 (en) 2012-06-01 2016-05-03 Google Inc. Inferring state of traffic signal and other aspects of a vehicle's environment based on surrogate data
US11474520B2 (en) 2012-06-01 2022-10-18 Waymo Llc Inferring state of traffic signal and other aspects of a vehicle's environment based on surrogate data
US10831196B2 (en) 2012-06-01 2020-11-10 Waymo Llc Inferring state of traffic signal and other aspects of a vehicle's environment based on surrogate data
US8793046B2 (en) * 2012-06-01 2014-07-29 Google Inc. Inferring state of traffic signal and other aspects of a vehicle's environment based on surrogate data
US10331133B2 (en) 2012-06-01 2019-06-25 Waymo Llc Inferring state of traffic signal and other aspects of a vehicle's environment based on surrogate data
US9928738B2 (en) 2013-04-12 2018-03-27 Traffic Technology Services, Inc. Red light warning system based on predictive traffic signal state data
US10008113B2 (en) 2013-04-12 2018-06-26 Traffic Technology Services, Inc. Hybrid distributed prediction of traffic signal state changes
US9396657B1 (en) 2013-04-12 2016-07-19 Traffic Technology Solutions, LLC Prediction of traffic signal state changes
WO2016016760A1 (en) * 2014-07-29 2016-02-04 Here Global B.V. An apparatus and associated methods for designating a traffic lane
US9576478B2 (en) 2014-07-29 2017-02-21 Here Global B.V. Apparatus and associated methods for designating a traffic lane
US10611304B2 (en) 2015-03-18 2020-04-07 Uber Technologies, Inc. Methods and systems for providing alerts to a connected vehicle driver and/or a passenger via condition detection and wireless communications
US11358525B2 (en) 2015-03-18 2022-06-14 Uber Technologies, Inc. Methods and systems for providing alerts to a connected vehicle driver and/or a passenger via condition detection and wireless communications
US10493911B2 (en) 2015-03-18 2019-12-03 Uber Technologies, Inc. Methods and systems for providing alerts to a driver of a vehicle via condition detection and wireless communications
US10328855B2 (en) 2015-03-18 2019-06-25 Uber Technologies, Inc. Methods and systems for providing alerts to a connected vehicle driver and/or a passenger via condition detection and wireless communications
US10089871B2 (en) 2015-03-18 2018-10-02 Uber Technologies, Inc. Methods and systems for providing alerts to a driver of a vehicle via condition detection and wireless communications
US9610893B2 (en) 2015-03-18 2017-04-04 Car1St Technologies, Llc Methods and systems for providing alerts to a driver of a vehicle via condition detection and wireless communications
US10850664B2 (en) 2015-03-18 2020-12-01 Uber Technologies, Inc. Methods and systems for providing alerts to a driver of a vehicle via condition detection and wireless communications
US11827145B2 (en) 2015-03-18 2023-11-28 Uber Technologies, Inc. Methods and systems for providing alerts to a connected vehicle driver via condition detection and wireless communications
US9824582B2 (en) 2015-03-18 2017-11-21 Uber Technologies, Inc. Methods and systems for providing alerts to a driver of a vehicle via condition detection and wireless communications
US11364845B2 (en) 2015-03-18 2022-06-21 Uber Technologies, Inc. Methods and systems for providing alerts to a driver of a vehicle via condition detection and wireless communications
US11113961B2 (en) 2016-01-11 2021-09-07 NetraDyne, Inc. Driver behavior monitoring
US11074813B2 (en) * 2016-01-11 2021-07-27 NetraDyne, Inc. Driver behavior monitoring
US11024165B2 (en) 2016-01-11 2021-06-01 NetraDyne, Inc. Driver behavior monitoring
US10480949B2 (en) * 2016-06-03 2019-11-19 Denso Corporation Apparatus for identifying position of own vehicle and method for identifying position of own vehicle
US20170350712A1 (en) * 2016-06-03 2017-12-07 Denso Corporation Apparatus for identifying position of own vehicle and method for identifying position of own vehicle
US11322018B2 (en) 2016-07-31 2022-05-03 NetraDyne, Inc. Determining causation of traffic events and encouraging good driving behavior
US11840239B2 (en) 2017-09-29 2023-12-12 NetraDyne, Inc. Multiple exposure event determination
US11314209B2 (en) 2017-10-12 2022-04-26 NetraDyne, Inc. Detection of driving actions that mitigate risk
US20210284195A1 (en) * 2020-03-13 2021-09-16 Baidu Usa Llc Obstacle prediction system for autonomous driving vehicles

Also Published As

Publication number Publication date
US20090070031A1 (en) 2009-03-12
US9043138B2 (en) 2015-05-26

Similar Documents

Publication Publication Date Title
US20120179358A1 (en) System and method for automated updating of map information
US10782138B2 (en) Method, apparatus, and computer program product for pedestrian behavior profile generation
JP4905044B2 (en) Traffic information distribution device
US20130131980A1 (en) Resolving gps ambiguity in electronic maps
US7499949B2 (en) Method and system for obtaining recurring delay data using navigation systems
US7433889B1 (en) Method and system for obtaining traffic sign data using navigation systems
US7925425B2 (en) Navigation information distribution systems, methods, and programs
EP2228779B1 (en) Traffic flow model to provide traffic flow information
US11521487B2 (en) System and method to generate traffic congestion estimation data for calculation of traffic condition in a region
US11244177B2 (en) Methods and systems for roadwork zone identification
US20080091339A1 (en) Navigation system
EP1906375A2 (en) Navigation device for receiving traffic information
JPH11249552A (en) System and method to update, expand and improve geographical data base using feedback
US7706966B2 (en) Navigation systems, methods, and programs
US20200217685A1 (en) Navigation system with maneuver guidance mechanism and method of operation thereof
US7113866B2 (en) Method and device for determining vehicle lane changes using a vehicle heading and a road heading
US11022458B2 (en) Navigation system with roadway lane guidance mechanism and method of operation thereof
EP4002322A1 (en) System and method for determining dynamic road capacity data for traffic condition
US20230204372A1 (en) Method, apparatus, and system for determining road work zone travel time reliability based on vehicle sensor data
JP2005267472A (en) Intersection determination method
JP5454559B2 (en) Traffic information distribution device
US11003190B2 (en) Methods and systems for determining positional offset associated with a road sign
US10883839B2 (en) Method and system for geo-spatial matching of sensor data to stationary objects
JP4702228B2 (en) Navigation device
EP3945512A1 (en) Method, apparatus, and system for identifying mobile work zones

Legal Events

Date Code Title Description
AS Assignment

Owner name: ON TIME SYSTEMS, INC., OREGON

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:CHANG, PAUL A.C.;GINSBERG, MATTHEW L.;SMITH, BRYAN;AND OTHERS;REEL/FRAME:027904/0136

Effective date: 20120320

AS Assignment

Owner name: GREEN DRIVER, INC., OREGON

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:ONTIME SYSTEMS, INC.;REEL/FRAME:032212/0754

Effective date: 20130823

STCB Information on status: application discontinuation

Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION