WO2016153435A1

WO2016153435A1 - Location system and method for determining location of a vehicle

Info

Publication number: WO2016153435A1
Application number: PCT/SG2016/050144
Authority: WO
Inventors: Wenjie Xu; Jaya Shankar S/O Pathmasuntharam; Umer RASHEED; Chee Wei Ang
Original assignee: Agency for Science,Technology and Research
Priority date: 2015-03-26
Filing date: 2016-03-28
Publication date: 2016-09-29
Also published as: SG11201707895UA

Abstract

There is provided a method of determining a location of a vehicle including obtaining a curve segment of a path navigated by the vehicle (estimated by an inertial navigation system), selecting one or more candidate curve segments from a road network database resembling the curve segment of the navigated path obtained, identifying one of the candidate curve segments as matching the curve segment of the navigated path, and determining the location of the vehicle based on the matching curve segment identified. The method includes comparing the curve segment of the navigated path with a curve segment from the road network database to determine whether the curve segment from the road network database qualifies as a candidate curve segment, and scaling at least one of the curve segment of the navigated path and the curve segment from the road network database to reduce a difference in length therebetween for the comparison.

Description

LOCATION SYSTEM AND METHOD FOR DETERMINING LOCATION OF A

VEHICLE

CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims the benefit of priority of Singapore Patent Application No. 10201502413R, filed 26 March 2015, the content of which being hereby incorporated by reference in its entirety for all purposes.

TECHNICAL FIELD

[0002] The present invention generally relates to a location system and a method for determining a location of a vehicle. Further aspects of the present invention relate to a method of detecting an interference on a positioning system associated with a vehicle such as blocking, jamming and/or spoofing attacks, and a method of verifying location information of a vehicle provided by a positioning system associated with the vehicle.

BACKGROUND

[0003] Vehicle on-board unit (OBU) may include various sensors such as an inertial navigation system (INS), digital compass, global navigation satellite system (GNSS) receiver and commumcation devices/interfaces such as Wi-Fi, Bluetooth, dedicated short range communications (DSRC), and cellular system (e.g. 3G/4G cellular system). Applications and services that ride on such systems are likely to be dependent on such sensors and communication devices to operate properly. For example, road side parking system may depend on DSRC beacons or GNSS signals to track the location of the car and charge the associated tariff. However, intentional tempering of the sensors and communication devices may disrupt the proper operation of the systems.

[0004] A tolling system may depend heavily on GNSS and the GNSS may be augmented with other systems such as DSRC. Infrastructure for future tolling system may be kept light-weight with no gantry for areas with good GNSS coverage or augmented with a simple DSRC road side unit (RSU) mounted on a pole along the road for areas with poor GNSS coverage. Such a setup helps in keeping the charging zone location flexible, reducing costs and ensuring a smoother flow of traffic. However, such a light-weight setup may diminish the feasibility of using an ultra-reliable enforcement system to track the position of the vehicle as well as the proper operation of the communication devices and sensors. In particular, such a light-weight setup may be highly vulnerable to illegitimate/intentional interferences such as intentional jamming, blocking and spoofing by a user to circumvent the system.

[0005] As tolling systems may depend on location positioning and the sensors may rely mainly on radio reception, the sensors may be the main target of jamming, blocking and spoofing attacks. While jamming attack involves the introduction of a similar but stronger frequency noise signal that intentionally block legitimate signals from reaching the antenna of a receiver (e.g., OBU), a user may also simply block the radio reception on a receiver by using materials that prevents radio signals from reaching the antenna of the receiver. While jamming involves a similar frequency noise signal to block legitimate signals, spoofing involves the blocking of legitimate radio signals and introduction of other signals to fool the receiver into thinking it is in a different (false) position.

[0006] As road tolling systems based on GNSS or radio beacon can be vulnerable to jamming, spoofing and blocking attacks, the system may be subjected to tampering. Such attacks would be more rampant if there are no enforcement systems (e.g. video surveillance) to capture and audit the movement of a vehicle through a charging zone. The introduction of anti-jamming and anti-spoofmg hardware specific to a particular mode of communication or positioning device will add cost to the overall OBU.

[0007] For example, to prevent jamming and spoofing, two types of general systems have been developed, namely, infrastructure based and in-device based. Infrastructure based systems such as the jammer detection and location (JLOC) Network (e.g., in the United States) and Sentinel (in the United Kingdom) may not be ideal due to costs and operational issues. In-device systems may be more practical than infrastructure based systems as they are more localized and provide a closer detection to the source of attack. However, such in-device systems may not be sufficiently cost effective. For example, some commercial GNSS receivers (e.g. u-blox) have hardware and software to detect interference through the monitoring of the received signal strength indicator. U-blox receivers also contain one or two surface-acoustic wave (SAW) filters in the signal path to attenuate out-of-band signals. U-blox also uses low-pass anti-aliasing filter in the RF chip to remove signals that would create aliases during the digitization process in the A/D converter. While these techniques work for certain jamming signal, it would fail if proper modulated signals with controlled power are introduced. Besides this, such GNSS receivers are unable to handle simple blocking attack or spoofing.

[0008] Some military GNSS receivers are known to use more radical measures against attack by using smart antenna arrays that can detect and attenuate jammer signals. However, such methods are known to be costly. For example, there has been disclosed a technique to suppress jamming by steering the antenna beam pattern towards the desired signal. GNSS spoofing detection has also been research widely. For example, there has been disclosed a spoofing detection method based on statistical tests of the received GNSS signals. However, such a method is not able to detect jamming and blocking attacks.

[0009] A need therefore exists to provide a method of detecting an interference on a positioning system associated with a vehicle (such as jamming, blocking and/or spoofing attacks) that seeks to address or at least ameliorate one or more of the problems associated with conventional systems and methods. There also exists a need to provide a method of determining a location of a vehicle that seeks to be able to withstand or at least be less susceptible to such interferences. It is against this background that the present invention has been developed.

SUMMARY

[0010] According to a first aspect of the present invention, there is provided a method of determining a location of a vehicle, the method comprising:

obtaining a curve segment of a path navigated by the vehicle, the navigated path being estimated by an inertial navigation system associated with the vehicle;

selecting one or more candidate curve segments from a road network database resembling said curve segment of the navigated path obtained;

identifying one of the candidate curve segments as matching said curve segment of the navigated path; and

determining the location of the vehicle based on the matching curve segment identified, wherein selecting one or more candidate curve segments comprises comparing said curve segment of the navigated path with a curve segment from the road network database to determine whether said curve segment from the road network database qualifies as a candidate curve segment resembling said curve segment of the navigated path, and scaling at least one of said curve segment of the navigated path and said curve segment from the road network database to reduce a difference in length therebetween for the comparison.

[0011] In various embodiments, said scaling scales at least one of said curve segment of the navigated path and said curve segment from the road network database to have substantially the same length.

[0012] In various embodiments, the method further comprises processing the navigated path, including extracting curve segments of the navigated path and indexing each curve segment extracted, and processing a road network from the road network database, including extracting curve segments of the road network and indexing each curve segment extracted.

[0013] In various embodiments, extracting curve segments of the navigated path comprises converting the navigated path to a representation reflecting a heading angle change over distance along the navigated path and identifying the curve segments of the navigated path based on said representation of the navigated path, and extracting curve segments of the road network comprises converting a path in the road network to a representation reflecting a heading angle change over distance along the path of the road network and identifying the curve segments of the road network based on said representation of the road network.

[0014] In various embodiments, each curve segment of the navigated path extracted is indexed with an identification thereof and an identification of an immediate subsequent curve segment along the navigated path, and each curve segment of the road network extracted is indexed with an identification thereof and an identification of an immediate subsequent curve segment along the road network.

[0015] In various embodiments, said identifying comprises comparing the immediate subsequent curve segment of said curve segment of the navigated path with the immediate subsequent curve segment of the candidate curve segment to determine whether the immediate subsequent curve segment of the candidate curve segment resembles the immediate subsequent curve segment of said curve segment of the navigated path.

[0016] In various embodiments, each curve segment of the navigated path extracted is further indexed with one or more parameters relating to its characteristics, and each curve segment of the road network extracted is further indexed with one or more parameters relating to its characteristics, and

[0017] In various embodiments, said identifying comprises comparing at least one of the one or more parameters relating to the characteristics of the immediate subsequent curve segment of said curve segment of the navigated path with at least one of the one or more parameters relating to the characteristics of the immediate subsequent curve segment of the candidate curve segment to determine whether they resemble each other.

[0018] In various embodiments, said comparing said curve segment of the navigated path with a curve segment from the road network database comprises converting said curve segment of the navigated path and said curve segment from the road network database into respective tangent space curves, and comparing the respective tangent space curves, and wherein said scaling scales at least one of the tangent space curves to reduce a difference in length therebetween for the comparsion.

[0019] In various embodiments,

said obtaining comprises obtaining a plurality of curve segments of the path navigated by the vehicle; and

said identifying comprises identifying, for each of the plurality of curve segments of navigated path obtained, a curve segment from the road network database as matching the curve segment of the navigated path,

wherein the method further comprises connecting the matching curve segments identified to form a projected path, and comparing the projected path with the navigated path to determine whether they match.

[0020] According to a second aspect of the present invention, there is provided a method of detecting an interference on a positioning system associated with a vehicle configured to provide location information of the vehicle, the method comprising: determining the location of the vehicle using the method according to the first aspect of the present invention; and

detecting an interference on the positioning system based on a comparison between the location of the vehicle determined and the location information of the vehicle provided by the positioning system.

[0021] In various embodiments, the interference comprises blocking, jamming and/or spoofing attacks on the positioning system.

[0022] According to a third aspect of the present invention, there is provided a method of verifying location information of a vehicle provided by a positioning system associated with the vehicle, the method comprising:

determining the location of the vehicle using the method according to the first aspect of the present invention; and

verifying the location information provided by the positioning system based on a comparison between the location of the vehicle determined and the location information of the vehicle provided by the positioning system.

[0023] According to a fourth aspect of the present invention, there is provided a location system for determining a location of a vehicle, the location system comprising: a curve retrieval module configured to obtain a curve segment of a path navigated by the vehicle, the navigated path being estimated by an inertial navigation system associated with the vehicle;

a selection module configured to select one or more candidate curve segments from a road network database resembling said curve segment of the navigated path obtained;

a curve identifying module configured to identify one of the candidate curve segments as matching said curve segment of the navigated path; and

a location determination module configured to determine the location of the vehicle based on the matching curve segment identified,

wherein the selection module is configured to compare said curve segment of the navigated path with a curve segment from the road network database to determine whether said curve segment from the road network database qualifies as a candidate curve segment resembling said curve segment of the navigated path, and to scale at least one of said curve segment of the navigated path and said curve segment from the road network database to reduce a difference in length therebetween for the comparison.

[0024] In various embodiments, said scaling scales at least one of said curve segment of the navigated path and said curve segment from the road network database to have substantially the same length.

[0025] In various embodiments, the location system further comprises a processing module configured to process the navigated path, including extracting curve segments of the navigated path and indexing each curve segment extracted, and to process a road network from the road network database, including extracting curve segments of the road network and indexing each curve segment extracted.

[0026] In various embodiments, extracting curve segments of the navigated path comprises converting the navigated path to a representation reflecting a heading angle change over distance along the navigated path and identifying the curve segments of the navigated path based on said representation of the navigated path, and extracting curve segments of the road network comprises converting a path in the road network to a representation reflecting a heading angle change over distance along the path of the road network and identifying the curve segments of the road network based on said representation of the road network.

[0027] In various embodiments, each curve segment of the navigated path extracted is indexed with an identification thereof and an identification of an immediate subsequent curve segment along the navigated path, and each curve segment of the road network extracted is indexed with an identification thereof and an identification of an immediate subsequent curve segment along the road network.

[0028] In various embodiments, the curve identifying module is configured to compare the immediate subsequent curve segment of said curve segment of the navigated path with the immediate subsequent curve segment of the candidate curve segment to determine whether the immediate subsequent curve segment of the candidate curve segment resembles the immediate subsequent curve segment of said curve segment of the navigated path.

[0029] In various embodiments, each curve segment of the navigated path extracted is further indexed with one or more parameters relating to its characteristics, and each curve segment of the road network extracted is further indexed with one or more parameters relating to its characteristics, and

wherein the curve identifying module is configured to compare at least one of the one or more parameters relating to the characteristics of the immediate subsequent curve segment of said curve segment of the navigated path with at least one of the one or more parameters relating to the characteristics of the immediate subsequent curve segment of the candidate curve segment to determine whether they resemble each other.

[0030] In various embodiments, said comparing said curve segment of the navigated path with a curve segment from the road network database by the selection module comprises converting said curve segment of the navigated path and said curve segment from the road network database into respective tangent space curves, and comparing the respective tangent space curves, and wherein said scaling scales at least one of the tangent space curves to reduce a difference in length therebetween for the comparsion.

[0031] According to a fifth aspect of the present invention, there is provided a computer program product, embodied in one or more computer-readable storage mediums, comprising instructions executable by one or more computer processors to perform a method of determining a location of a vehicle, the method comprising:

determining the location of the vehicle based on the matching curve segment identified,

wherein selecting one or more candidate curve segments comprises comparing said curve segment of the navigated path with a curve segment from the road network database to determine whether said curve segment from the road network database qualifies as a candidate curve segment resembling said curve segment of the navigated path, and scaling at least one of said curve segment of the navigated path and said curve segment from the road network database to reduce a difference in length therebetween for the comparison.

BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Embodiments of the present invention will be better understood and readily apparent to one of ordinary skill in the art from the following written description, by way of example only, and in conjunction with the drawings, in which:

FIG. 1 depicts a flow diagram illustrating a method of determining a location of a vehicle according to various embodiments of the present invention;

FIG. 2 depicts a flow diagram illustrating a method of detecting an interference

(e.g., blocking, jamming and/or spoofing attacks) on a positioning system associated with a vehicle configured to provide location information of the vehicle;

FIG. 3 depicts a flow diagram illustrating a method of verifying location information of a vehicle provided by a positioning system associated with the vehicle;

FIG. 4 depicts a schematic drawing of a location system for determining a location of a vehicle according to various embodiments of the present invention;

FIG. 5 depicts a general architecture of a system associated with a method of tracking vehicle movement according to various example embodiments of the present invention;

FIG. 6 depicts a sample route at an arbitrary location which was navigated by a vehicle for collecting various data;

FIG. 7 depicts a flow diagram of a hierarchical vehicle localization framework with blocking, jamming and spoofing detection according to various example embodiments of the present invention;

FIG. 8 depicts a flow diagram illustrating a process of counter-verification and detection of jamming or blocking events (method of detecting interference) according to various example embodiments of the present invention;

FIG. 9 depicts a flow diagram of an INS-only based map matching method according to various example embodiments of the present invention;

FIG. 10 depicts an exemplary GIS map used to illustrate the method of FIG. 9 in an example; FIG. 11 depicts the exemplary GIS map with various road curves and intersections extracted according to the method of FIG. 9 and illustrated in the example;

FIG. 12A depicts an exemplary path formed between selected points from the GIS map in an example;

FIG. 12B depicts a heading angle change versus arc length representation/description of the exemplary path shown in FIG. 12A;

FIG. 13A depicts an example navigated path estimated by an INS associated with a vehicle from an example INS data collected in an example test drive in the method of FIG. 9;

FIG. 13B depicts curve segments that are extracted from the INS data collected in

FIG. 13 A;

FIG. 14 depicts a curve likeness matching process according to various example embodiments of the present invention; and

FIG. 15 illustrates an example path shape matching result of the navigated path (INS trace) to the corresponding/matching path of the GIS map.

DETAILED DESCRIPTION

[0033] Various embodiments of the present invention provide a method of determining a location of a vehicle, and a location system for determining a location of a vehicle. The method seeks to be able to withstand or at least be less susceptible to illegitimate/intentional interferences such as intentional jamming, blocking and spoofing attacks. With more reliable determination of the location of the vehicle, the method may then be used for various purposes such as to verify location information of a vehicle provided by a positioning system (e.g., global navigation satellite system (GNSS) receiver or on-board unit (OBU)) associated with the vehicle and to detect an interference on a positioning system associated with the vehicle according to various embodiments of the present invention.

[0034] Interferences to a system can be intentional or accidental. Examples of accidental interferences include harmonic emissions from commercial high power transmitters, radar, mobile satellites, personal electronic devices, and so on. Another example of an accidental interference could be meaconing which may occur when an old antenna (e.g. GPS) rebroadcasts the signal due to poor impedance matching in the amplified signal path from the low noise amplifier, which interferes with reception in an adjacent antenna. For example, three forms of intentional attacks may be possible for tolling and payment systems. These systems can be subjected to jamming, spoofing and blocking attacks. Blocking may be the most likely attack to occur as it may be the easiest to implement. For example, sensor devices such as GNSS receivers and communication devices such as 3G/4G and dedicated short range communications (DSRC) can be easily blocked. The second likely activity may be jamming. GNSS jammers are widely available at very low costs. Likewise, jammers for Wi-Fi or cellular networks are also widely available. Jamming noise can be based on simple or modulated signals. Some jammers can be based on repeaters that emulate the signals (GNSS) and transmit them with some delay. In such a case, it may trick the device to think that it is in, for example, an urban canyon or an area with multipath reflections. Spoofing may be the least likely attack to a system as it is generally more complicated and more expensive than blocking and jamming, but may nevertheless occur. An example of spoofing attack is the rebroadcasting of erroneous satellite ephemeris.

[0035] Tracking and detection of a possible attack is also made more complicated with true positive scenarios of missing sensor or communication data. For example, in a tunnel or completely covered underground car park, GNSS signals may be absent. In an underground car park, cellular signals may also be absent. Reflected signals are also common in urban areas. Therefore, various embodiments of the present inventions seek to develop a method and a system that avoid or minimizes false positive reporting.

[0036]

Table 1 - Possible jamming or blocking methods and their impact on various sensors and communication devices

[0037] Table 1 gives a summary of possible jamming or blocking methods and their impact on the communication devices and positioning sensors based on research/experiments carried out according to various exemplary embodiments of the present invention. Experimental results show that GNSS, 3G/4G, Wi-Fi and DSRC receivers would be affected by simply shielding the receivers or placing an interference signal emitter near the receivers. While intentional jamming with a jamming frequency on the receivers can be positively identified to some degree, intentional blocking poses a different challenge. In some places with poor signal coverage, it is possible to wrongly conclude that intentional blocking is occurring (false positive). Digital compass which measures earth magnetic field would also fail to function properly if a strong magnet is placed nearby. However, for intentional jamming of INS, the INS is able to pick up the signature of the intentional vibration and thus is able to detect such an event.

[0038]

Table 2 - Likelihood of or vulnerability to jamming and spoofing attack

[0039] Based on the experimental results, Table 2 indicates the likelihood of or vulnerability to jamming and spoofing attacks on various communication devices and positioning sensors (or position tracking modes). Jamming and blocking attacks can be readily be carried out on GNSS, 3G/4G, Wi-Fi and DSRC receivers and digital compass, and thus their vulnerability to such attacks are high. Spoofing is a more expensive and an elaborate process which is less likely to be used but can nevertheless still be applied to GNSS, 3G/4G, Wi-Fi and DSRC, and thus the likelihood of a spoofing attack on them are medium. Digital compass has a high risk of being subjected to jamming attacks. On the other hand, INS generally cannot be spoofed, and although it may be subjected to jamming, the jamming attack can be easily detected by examining the noise signal (e.g., the signature of the intentional vibration). Accordingly, it has been shown that at a basic level of detection, tracking of vehicle's localization using accelerometer and gyroscope (INS) measurements advantageously possesses low vulnerability to blocking, jamming and spoofing attacks. Thus, INS measurements are used as a counter-measure against such attacks according to various embodiments of the present invention.

[0040] Accordingly, various embodiments of the present invention seek to provide a method of determining a location of a vehicle reliably by using an INS comprising a motion sensor (accelerometer) and a rotation sensor (gyroscope). With more reliable determination of the location of the vehicle, the method may then be used for various purposes such as to verify location information of a vehicle provided by a positioning system associated with the vehicle and to detect an interference on a positioning system associated with the vehicle according to various embodiments of the present invention. For example, a reliable method of tracking movement of a vehicle may thus be provided capable of withstanding or is at least less prone to illegitimate interferences such as blocking, jamming and spoofing attacks. In this regard, the possibility of disrupting or tempering systems is high and thus a method that enables reliable determination of a vehicle's location and thus tracking of a vehicle's movement would be advantageous.

[0041] FIG. 1 depicts a flow diagram illustrating a method 100 of determining a location of a vehicle according to various embodiments of the present invention. The method 100 comprises a step 102 of obtaining a curve segment of a path or trajectory navigated by the vehicle, the navigated path being estimated by an INS associated with the vehicle, a step 104 of selecting one or more candidate curve segments from a road network database resembling the curve segment of the navigated path obtained, a step 106 of identifying one of the candidate curve segments as matching the curve segment of the navigated path, and a step 108 of determining the location of the vehicle based on the matching curve segment identified such as based on location information associated with the matching curve segment identified. In particular, the step 104 of selecting one or more candidate curve segments comprises comparing the curve segment of the navigated path with a curve segment from the road network database to determine whether the curve segment from the road network database qualifies as a candidate curve segment resembling the curve segment of the navigated path, and scaling at least one of the curve segment of the navigated path and the curve segment from the road network database to reduce a difference in length therebetween for the comparison. In various embodiments, the scaling scales at least one of the curve segment of the navigated path and the curve segment from the road network database to have substantially the same length. In this regard, performing the scaling of the curve segments before making the comparison advantageously improves the accuracy of the comparison for determining whether the curve segments resemble each other, which in turn improves the accuracy of locating/identifying the matching/corresponding curve segments from the road network database. Otherwise, the comparison may be inaccurate and actual/correct matching/corresponding curve segments from the road network may be overlooked.

[0042] The INS can be any device or system known in the art that is capable of providing measurements or readings using a motion sensor (accelerometer) and a rotation sensor (gyroscope) to continuously track the position and orientation of an object (in particular, a vehicle) relative to a known starting point and orientation, without the need for absolute location information (i.e., an external position reference). According to various embodiments of the present invention, from the navigated path estimated by the INS associated with (e.g., mounted on) the vehicle, feature segments (in particular, curve segments) are obtained. As will be illustrated later with reference to drawings, curve segments may include, but not limited to, curve parts/segments and intersections of the navigated path or road. For example, it can be appreciated to a person skilled in the art that curve segments may also include slope parts/segments of the navigated path or road. The curve segments obtained are then used to search for matching/corresponding curve segments from a road network database comprising feature segments with associated location information (e.g., geographic information system (GIS) map database or a road map database created by road mapping using a vehicle with suitable sensors to navigate and survey the roads to capture the road layout and associated features along with their location information). Once a matching/corresponding curve segment from the road network database is identified, the location of the vehicle can be determined based on the location information associated with the matching curve segment identified.

[0043] Accordingly, various embodiments of the present invention determine a location of a vehicle by matching the vehicle's navigated path (trajectory or path shape) estimated by an INS to a road network database (or road map database). In this regard, since the navigated path estimation does not depend on or require any wireless reception (e.g., GPS signals), such an estimation is robust to blocking, jamming and spoofing attacks and can thus be subsequently used for blocking, jamming and spoofing detection according to various embodiments of the present invention. Conventional map matching methods or techniques usually require absolute location information. This however makes them vulnerable to blocking, jamming and spoofing attacks as discussed hereinbefore. In contrast, the map matching process or technique according to various embodiments of the present invention does not require absolute location information, but is advantageously based on feature segment matching (in particular, curve segment matching) where for a given query curve segment, a matching curve segment from a collection of curve segments in a road network database is identified with appropriate translations, rotations and scaling of the curve segments being compared for improving the accuracy of the comparison. This advantageously addresses or at least ameliorates problems in comparing curve segments due to imperfect navigated path estimation by the INS.

[0044] As mentioned hereinbefore, the method may be utilized in various applications for various purposes according various embodiments of the present invention such as to verify location information of a vehicle provided by a positioning system associated with the vehicle and to detect an interference on a positioning system (e.g., GNSS receiver or OBU) associated with the vehicle.

[0045] FIG. 2 depicts a flow diagram illustrating a method 200 of detecting an interference (e.g., blocking, jamming and/or spoofing attacks) on a positioning system associated with a vehicle configured to provide location information of the vehicle (e.g., a GNSS receiver or an OBU mounted on the vehicle to provide the vehicle's location information). The method 200 comprises a step 202 of determining the location of the vehicle using the above-described method 100, and a step 204 of detecting an interference on the positioning system based on a comparison between the location of the vehicle determined and the location information of the vehicle provided by the positioning system. For example, a comparison showing a discrepancy above a certain or predetermined threshold may indicate that the vehicle's location information provided by the positioning system is not accurate or false, thus indicating the presence of an interference or attack on the positioning system.

[0046] FIG. 3 depicts a flow diagram illustrating a method 300 of verifying location information of a vehicle provided by a positioning system associated with the vehicle. The method 300 comprises a step 302 of determining the location of the vehicle using the above-described method 100, and a step 304 of verifying the location information provided by the positioning system based on a comparison between the location of the vehicle determined and the location information of the vehicle provided by the positioning system. For example, if the location information of the vehicle provided by the positioning system is the same as or is within an acceptable range (e.g., certain or predetermined error threshold) from the location of the vehicle determined by the method 100, the location information may be verified to be accurate or true.

[0047] FIG. 4 depicts a location system 400 for determining a location of a vehicle. The location system 400 comprises a curve retrieval module or circuit 402 configured to obtain a curve segment of a path navigated by the vehicle, the navigated path being estimated by an INS associated with the vehicle, a selection module or circuit 404 configured to select one or more candidate curve segments from a road network database resembling the curve segment of the navigated path obtained, a curve identifying module or circuit 406 configured to identify one of the candidate curve segments as matching the curve segment of the navigated path, and a location determination module or circuit 408 configured to determine the location of the vehicle based on the matching curve segment identified. In particular, the selection module 404 is configured to compare the curve segment of the navigated path with a curve segment from the road network database to determine whether the curve segment from the road network database qualifies as a candidate curve segment resembling the curve segment of the navigated path, and to scale at least one of the curve segment of the navigated path and the curve segment from the road network database to reduce a difference in length therebetween for the comparison.

[0048] For example, the location system 400 may be integrated with a navigation/positioning system (e.g., GNSS receiver or INS) or may be a separate system communicatively coupled with the positioning system. For example, the location system 400 may be implemented or located in a positioning system or may be implemented or located in a separate system communicatively coupled to the positioning system (either remotely or locally) during operation. For example, the functions or processing performed by the location system 400 may be carried out within the positioning system or remotely but in communication with the positioning system.

[0049] A computing system or a controller or a microcontroller or any other system providing a processing capability can be presented according to various embodiments in the present disclosure. Such a system can be taken to include a processor. For example, the positioning system described herein may include a processor/controller and a memory which are for example used in various processing carried out in the methods as described herein. A memory used in the embodiments may be a volatile memory, for example a DRAM (Dynamic Random Access Memory) or a non-volatile memory, for example a PROM (Programmable Read Only Memory), an EPROM (Erasable PROM), EEPROM (Electrically Erasable PROM), or a flash memory, e.g., a floating gate memory, a charge trapping memory, an MRAM (Magnetoresistive Random Access Memory) or a PCRAM (Phase Change Random Access Memory).

[0050] In various embodiments, a "circuit" may be understood as any kind of a logic implementing entity, which may be special purpose circuitry or a processor executing software stored in a memory, firmware, or any combination thereof. Thus, in an embodiment, a "circuit" may be a hard-wired logic circuit or a programmable logic circuit such as a programmable processor, e.g. a microprocessor (e.g. a Complex Instruction Set Computer (CISC) processor or a Reduced Instruction Set Computer (RISC) processor). A "circuit" may also be a processor executing software, e.g. any kind of computer program, e.g. a computer program using a virtual machine code such as e.g. Java. Any other kind of implementation of the respective functions which will be described in more detail below may also be understood as a "circuit" in accordance with various alternative embodiments. Similarly, a "module" may be a portion of a system according to various embodiments in the present invention and may encompass a "circuit" as above, or may be understood to be any kind of a logic-implementing entity therefrom.

[0051] Some portions of the present disclosure are explicitly or implicitly presented in terms of algorithms and functional or symbolic representations of operations on data within a computer memory. These algorithmic descriptions and functional or symbolic representations are the means used by those skilled in the data processing arts to convey most effectively the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities, such as electrical, magnetic or optical signals capable of being stored, transferred, combined, compared, and otherwise manipulated. [0052] Unless specifically stated otherwise, and as apparent from the following, it will be appreciated that throughout the present specification, discussions utilizing terms such as "scanning", "calculating", "determining", "replacing", "generating", "initializing", "outputting", or the like, refer to the action and processes of a computer system, or similar electronic device, that manipulates and transforms data represented as physical quantities within the computer system into other data similarly represented as physical quantities within the computer system or other information storage, transmission or display devices.

[0053] The present specification also discloses an apparatus for performing the operations of the methods. Such apparatus may be specially constructed for the required purposes, or may comprise a general purpose computer or other device selectively activated or reconfigured by a computer program stored in the computer. The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various general purpose machines may be used with programs in accordance with the teachings herein. Alternatively, the construction of more specialized apparatus to perform the required method steps may be appropriate.

[0054] In addition, the present specification also implicitly discloses a computer program or software/functional module, in that it would be apparent to the person skilled in the art that the individual steps of the methods described herein may be put into effect by computer code. The computer program is not intended to be limited to any particular programming language and implementation thereof. It will be appreciated that a variety of programming languages and coding thereof may be used to implement the teachings of the disclosure contained herein. Moreover, the computer program is not intended to be limited to any particular control flow. There are many other variants of the computer program, which can use different control flows without departing from the spirit or scope of the invention. It will be appreciated to a person skilled in the art that various modules described herein (e.g., the curve retrieval module 402, the selection module 404, the location determination module 406, and the curve identifying module 408) may be software module(s) realized by computer program(s) or set(s) of instructions executable by a computer processor to perform the required functions, or may be hardware module(s) being functional hardware unit(s) designed to perform the required functions. It will also be appreciated that a combination of hardware and software modules may be implemented.

[0055] Furthermore, one or more of the steps of the computer program or method may be performed in parallel rather than sequentially. Such a computer program may be stored on any computer readable medium. The computer readable medium may include storage devices such as magnetic or optical disks, memory chips, or other storage devices suitable for interfacing with a general purpose computer. The computer program when loaded and executed on such a general-purpose computer effectively results in an apparatus that implements the steps of the methods described herein.

[0056] In various embodiments, there is provided a computer program product, embodied in one or more computer-readable storage mediums, comprising instructions (e.g., the curve retrieval module 402, the selection module 404, the location determination module 406, and the curve identifying module 408) executable by one or more computer processors to perform a method 100 of determining a location of a vehicle as described hereinbefore with reference to FIG. 1.

[0057] The software or functional modules described herein may also be implemented as hardware modules. More particularly, in the hardware sense, a module is a functional hardware unit designed for use with other components or modules. For example, a module may be implemented using discrete electronic components, or it can form a portion of an entire electronic circuit such as an Application Specific Integrated Circuit (ASIC). Numerous other possibilities exist. Those skilled in the art will appreciate that the system can also be implemented as a combination of hardware and software modules.

[0058] It will be appreciated to a person skilled in the art that the terminology used herein is for the purpose of describing various embodiments only and is not intended to be limiting of the present invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

[0059] In order that the present invention may be readily understood and put into practical effect, various example embodiments of the present inventions will be described hereinafter by way of examples only and not limitations. It will be appreciated by a person skilled in the art that the present invention may, however, be embodied in various different forms and should not be construed as limited to the example embodiments set forth hereinafter. Rather, these example embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the present invention to those skilled in the art.

[0060] According to various example embodiments of the present invention, to address or at least mitigate the jamming, spoofing, and blocking attacks, an antijamming/spoofing/blocking method is provided to track vehicle movement which capitalizes on a hierarchical fusion of signals derived from multi-modal sensors and communication devices. The methods use different modes as counter verification techniques to authenticate the integrity of the data from a particular sensor or communication device. The counter verification and fusion process may be carried out at different levels of complexity depending on the availability of the type of sensor or communication data collected. At a most basic level of tracking according to embodiments of the present invention, an INS only based path tracking technique/algorithm is provided. For example, this caters for the case where most of the devices that are vulnerable to attacks do not have any data from other modes of communication such as GNSS, DSRC, Cellular communications. Advantageously, the basic level technique/algorithm for INS does not depend on the need for an accurate starting point which is typically required in standard/conventional map matching techniques.

[0061] FIG. 5 depicts a general architecture of a system 500 associated with the above-mentioned method which comprises a two-step process. A first step (step 1) of the method involves several offline modules (first module 502, second module 504, third module 506, and fourth module 508) that are configured to capture, process and store associated sensors and communication data related to a road network map (e.g., GIS map). The first module 502 may comprise, for example, capturing of RSSI fingerprint and cell tower ID data for cellular networks, RSSI fingerprint for Wi-Fi and DSRC access points and ranging data. These data are stored in a database and associated (tagging) to a road network map. The association of the fingerprint to various segments of the road network may be used to perform the counter-verification process for detecting possible jamming, spoofing or blocking event according to various embodiments of the present invention. The second module 504 may comprise, for example, capturing of cell tower, Wi-Fi and DSRC access point (AP) locations and the tagging thereof to the road network map. For example, such location information/data can be used to derive the relative distance of a mobile device from these known locations of the APs. The third module 506 may comprise, for example, a pre-processed road network map with distinct/characteristic features (in particular, curve segments) extracted, indexed and stored in a database. The third module 506 forms the most basic level technique used along with INS in the search and verification of possible jamming, blocking and spoofing of the system according to various embodiments of the present invention. The third module 506 and the various road network features will be described in more details later. The fourth module 508 may comprise, for example, collection of the expected GNSS data quality at different segments of the road network and the tagging thereof to the road network map. For example, this data may be used by the system to check if the OBU has been deliberately blocked or jammed from receiving GNSS data at certain locations of the road network map.

[0062] A second step of the method may comprise two possible sub-steps 2a or 2b. In step 2a, the method involves an online processing (i.e., sent remotely for processing) of the collected sensory and communication data. A hierarchical fusion of signals derived from multi-modal sensors and communication devices are used to detect possible jamming, blocking and spoofing event. The process of the data fusion, counter- verification with different modes of data and individual mode map matching will be described in more details later. Step 2b may be an optional step and is similar to step 2a except that the fusion, counter-verification and detection of jamming, spoofing and blocking is performed offline (i.e., processed locally) instead of online in the navigation/positioning system (e.g., GNSS receiver or OBU). In this case, the sensor and communication data are collected from the system and transferred to the back end server side for processing. Such a process can be scheduled periodically for periodic checking of possible tempering of the system.

[0063] In various embodiments, fingerprint data can be used to approximate the rough location of the movement trajectory of the vehicle. For example, the fingerprint ID and associated RSSI data can be collected with a positioning system (e.g., OBU) comprising cellular, Wi-Fi and DSRC devices. As an example illustration only, FIG. 6 shows a sample route 602 at an arbitrary location which was navigated to collect Cell Tower ID and Wi-Fi fingerprints.

[0064] FIG. 7 depicts a flow diagram 700 of a hierarchical vehicle localization framework with blocking, jamming and spoofing detection according to various example embodiments of the present invention. A hierarchical approach of counter-verification is carried out to authenticate the integrity of the sensor data which are prone to jamming, spoofing and blocking attacks. At an initial stage (e.g., when the vehicle's ignition or movement is detected), data collection (sensor and communication data) of the various modes (e.g., GNSS, cellular, Wi-Fi, DSRC, gyro, accelerometer, compass, etc.) may commence. In various example embodiments, the verification of the various modes starts from the highest level to the lowest level (e.g., from level 4 to level 1) depending on availability of the data or modes. The level of processing and map-matching to identify the trajectory of the vehicle generally becomes more complex as the level shown in FIG. 7 decreases. The processing at the different levels is described below by way of examples only according to various example embodiments of the present invention.

[0065] At the highest level (level 4 - GNSS and INS), the GNSS data integrity is verified. If GNSS data is available, GNSS information may be authenticated by examining the similarity between the GNSS trace and the navigated path (or path shape) estimated by the INS. If the similarity and consistency is high, map matching process with Kalman filtering applied to GNSS and INS data can be carried out. In this regard, Kalman filtering is applied to fuse GNSS location information with INS sensor inputs to produce a smoothed trajectory and more accurate positioning. In the example embodiment, the GNSS trajectory is compared with the trajectory obtained/estimated from the INS, and the trajectory obtained from the INS is computed by dead-reckoning as part of the Kalman filtering.

[0066] At level 3 (digital compass, cell tower ID/Wi-Fi AP/DSRC AP, and INS), the integrity of Wi-Fi AP/cell tower ID/DSRC AP sensed is verified. For example, the integrity of Wi-Fi AP/cell tower ID/DSRC AP may be verified by checking if the cell tower's position, Wi-Fi AP's position or DSRC AP's position matches or is consistent with the movement trajectory of the INS data. If the cell tower information is authenticated, it can be used to reduce the size of map database so that the search time in the map matching process can be greatly reduced. If the similarity and consistency is high, map matching using the Wi-Fi AP/Cell tower ID/DSRC AP and INS can be carried out.

[0067] At level 2 (digital compass and INS), the integrity of digital compass is verified. The integrity of the digital compass may be verified by checking the consistency between heading changes computed from the gyro and the digital compass. If the compass information is authenticated, the map matching process based on INS and compass can be carried out.

[0068] At level 1 (INS only), there are no other sensor data from other modes available for map matching except for the collected INS data. The map matching process is thus carried out based on INS data.

[0069] FIG. 8 depicts a flow diagram 800 illustrating a process of counter- verification and detection of jamming or blocking events (method of detecting interference) according to various example embodiments of the present invention. It will be appreciated that a spoofing attack can be detected in a similar manner and thus will not be specifically described in this example. Map matching is performed between the navigated path estimated by the INS and the road network database, without sensor data from other modes such as GNSS, WiFi/Cell Tower/DSRC, and compass data. As shown in FIG. 8, if the map-matching process is able to identify the movement trajectory of the vehicle with a high degree of consistency (step 802), then other processes and counter- verification may be carried out. For example, based on the offline collection of expected GNSS data for a road segment, the process may counter check if GNSS data is supposed to be present at the road segment (step 804). If GNSS signal is supposed to be present at a road segment but no GNSS signal has been detected at the road segment, this indicates that a blocking or jamming event on the GNSS receiver may have occurred which can then be reported. Similarly, based on the collection of expected Wi-Fi AP, cell tower ID, and DSRC AP at or near a road segment, the process may check whether certain Wi-Fi AP, cell tower ID, and DSRC AP are supposed to be at a road segment (step 806). If certain Wi-Fi AP, cell tower ID, and DSRC AP are supposed to be at a road segment but no such Wi-Fi AP, cell tower ID, and DSRC AP have been detected at the road segment, this indicates that a blocking or jamming attack the receiver may have occurred. Similarly, a digital compass reading at a road segment may also be compared with the INS data to determine whether they are consistent or resemble (step 808). If they do not resemble, this indicates that a jamming attack on the digital compass may have occurred.

[0070] Various embodiments of the present invention are particularly directed to the INS-only based map matching method/technique. In particular, the method may advantageously be used to counter-verify other sensor modes or may be used solely when there are no data from other modes. Furthermore, as discussed and shown in Tables 1 and 2, INS is the most robust sensor against various attacks such as blocking, jamming and spoofing attacks. Accordingly, an INS-only based map matching method 900 according to various example embodiments of the present invention will now be described with reference to FIG. 9, which can be utilized to determine the location of a vehicle as shown in FIG. 1.

[0071] As shown in steps 902 and 906 of FIG. 9, the method 900 comprises processing the navigated path / path shape (step 906), including extracting curve segments of the navigated path and indexing each curve segment extracted, and processing a road network from the road network database (step 902), including extracting curve segments of the road network and indexing each curve segment extracted. For example, offline pre-processing of map feature extraction and indexing is carried out as described hereinbefore under Step 1 in FIG. 5. As an example, a GIS map as shown in FIG. 10 is used to illustrate the process. A GIS map for road networks may contain many useful features that can be used in the INS based map-matching process. Some maps provide good details of movement coding, lane coordinates, location of intersections, stop lines, curbs, U-turn locations, curves, and so on. However, some maps may only provide limited information about the road networks. In any case, features of the road network can be either extracted from the maps or extracted from vehicles carrying reliable sensors and plying the roads. By way of examples only and without limitations, some of the information which may be stored in the road network database are:

• coordinates of points (nodes) taken from the road;

• directional road segments described by the node which it starts from and the node which it leads to;

• divider type (e.g., this may cater for abrupt turns, 3-point turns, and so on, that may occur at the roads);

• U-turn points (e.g., legal and illegal); and/or

• features of road segments (e.g., curve, intersections, bumps/humps, and slopes).

[0072] In step 902, some of the features that are extracted for map-matching are the road curves characteristic and their associated location. For example, some of the road curves and intersections from the GIS map shown in FIG. 10 are extracted and shown in FIG. 11 enclosed in circles 1102.

[0073] According to various example embodiments, extracting curve segments of the navigated path comprises converting the navigated path to a representation reflecting a heading angle change over distance along the navigated path and identifying the curve segments of the navigated path based on such a representation of the navigated path. Similarly, extracting curve segments of the road network comprises converting a path in the road network to a representation reflecting a heading angle change over distance along the path of the road network and identifying the curve segments of the road network based on such a representation of the road network. For example, in order to extract curves, paths between selected points from the GIS map are formed based on the shortest path algorithm (such as Dijkstra's algorithm). An example of the formed path from the GIS map is shown in FIG. 12A. Subsequently, the path is converted to a heading angle change versus arc length representation/description as shown in FIG. 12B, whereby the arc length refers the distance travelled along the path. From FIG. 12B, it can be observed that each cluster of points 1202 and points 1204 having relatively a larger value (a relatively longer length in FIG. 12B) corresponds to a curve shown in FIG. 12A. Hence, according to an example embodiment, a K-means clustering method is used to identify points 1202, 1204, i.e., curves. It can be understood that longer lines in FIG. 12B represent sharper curves since the heading angle change is larger. Curves of the navigated path may also be extracted in the same manner to obtain curve segments.

[0074] According to various example embodiments of the present invention, each curve segment of the navigated path extracted is indexed with an identification thereof and an identification of an immediate subsequent curve segment along the navigated path. Similarly, each curve segment of the road network extracted is indexed with an identification thereof and an identification of an immediate subsequent curve segment along the road network. Furthermore, each curve segment of the navigated path extracted may be further indexed with one or more parameters relating to its characteristics, and each curve segment of the road network extracted may be further indexed with one or more parameters relating to its characteristics.

[0075] For example, the curve segments that are extracted from the GIS map and the navigated path are further analysed and characterized with other parameters. By way of example only and without limitations, a curve segment may be characterized by:

• right bending or left bending;

• curvature profile: set of discreet angle/direction change per meter points with location coordinates or distance between the points;

· maximum angle/direction change per meter value;

• mean value of angle/direction change per meter;

• standard deviation σ of angle/direction change per meter;

• set of points with values of angle/direction change per meter within a fixed factor C of a (e.g. Ca);

· total length of points with values of angle/direction change per meter within a fixed factor C of σ;

• total change in angle for the length of interest (e.g. over Ca); and/or

• the immediate folio wing/subsequent curve's ID.

[0076] One or more of these parameters/characteristics may then be indexed and stored in a table database which may then be used in the map matching process. For example, turns at intersections may share common curvature characteristics, such as left turns. Hence the immediate following/subsequent curve's ID is important for speeding up the search process as this allows the search routine to quickly narrow down to the candidate curves with a following curve which matches to that from INS trace (navigated path). By way of example only and without limitation, an example map feature database is shown in Table 3.

Table 3 - Example Map Feature Database

[0077] FIG. 13 A depicts an example navigated path 1302 estimated by an INS associated with a vehicle from an example INS data collected in an example test drive in step 904 of FIG. 9. Features from the INS data may then be extracted. FIG. 13B shows the curve segments 1306 (illustrated as dashed segments/sections) that are extracted from the INS data in the example. Each of the curve segments 1306 may be processed in the manner as described above whereby key characteristics are extracted from each curve segment and used as index to search features in the pre-stored database.

[0078] In step 908 of FIG. 9, one or more candidate curve segments are selected from the road network database (map feature database) resembling the curve segment of the navigated path obtained. In various example embodiments, a curve likeness matching process may be carried out to select curves from the map feature database which are similar to the curve extracted from INS trace (navigated path). For example, the process may start with the characteristic INS curves which have fewer counterparts in the map feature database, thus advantageously reducing search time.

[0079] FIG. 14 illustrates a curve likeness matching process according to various example embodiments of the present invention. In the process, comparing the curve segment of the navigated path with a curve segment from the road network database may comprise converting the curve segment of the navigated path and the curve segment from the road network database into respective tangent space curves, and comparing the respective tangent space curves. Furthermore, the scaling scales at least one of the tangent space curves to reduce a difference in length therebetween for the comparsion. By way of an example only, the process may comprise the following steps:

• the curve is converted into tangent space representation 1402, 1404 (heading angle versus arc length).

• the tangent curve % 1402 is shifted vertically to equalize the weighted average of the angle values of _x 1402 and a₂ 1404. For the original curves, this may mean a rotation of % 1402 to fit better. For example, since the vertical or y-axis of the tangent space representation refers to heading angle, shifting vertically (i.e. along the direction of y-axis) means incrementing or decrementing the heading angle. This would thus correspond to rotation of the original curve. In the example embodiment, the amount of vertical shift is determined/calculated such that the weighted averages of the curves are the same. In other words, to shift vertically (rotate) until curve c½ 1402 have the same general direction or substantially the same direction as curve a₂ 1404.

• the resulting curves are scaled to the same length. Let l(_.a₂) > i(c½), then the scaling factor sf is given by sf = ί(α₂)/'(^αι)·

• the absolute value norm difference is taken. L(a₁, a₂) = || _α— ₂ ||_ί,₁, hence L is the area between the two tangent space curves.

• the comparison measure is defined as D{a_lt a₂) = ^L^'"^2"> x sf.

[0080] In various example embodiments, identifying one of the candidate curve segments from the road network database as matching the curve segment of the navigated path comprises comparing the immediate subsequent curve segment of the curve segment of the navigated path with the immediate subsequent curve segment of the candidate curve segment to determine whether the immediate subsequent curve segment of the candidate curve segment resembles the immediate subsequent curve segment of the curve segment of the navigated path. For example, the immediate following curves of the INS trace is compared or matched to that of selected map features so as to further reduce the candidate map segments. [0081] In various example embodiments, the above identifying process comprises comparing at least one of the one or more parameters relating to the characteristics of the immediate subsequent curve segment of the curve segment of the navigated path with at least one of the one or more parameters relating to the characteristics of the immediate subsequent curve segment of the candidate curve segment to determine whether they resemble each other. For example, in step 910, spatial proximity rules and feature characteristics such as relative locations and relative bearing change of each pair of neighbouring curves are used to prune the search space.

[0082] In various example embodiments, the process comprises connecting the matching curve segments identified to form a projected path, and comparing the projected path with the navigated path to determine whether they match. For example, in step 912, curve fitting and likeness verification for extracted features with identified map segments are performed. For example, projected paths on the GIS map can be formed by connecting the identified map segments. Then, the entire INS trace and the projected paths are compared using the curve likeness matching process described with reference to FIG. 14. In this manner, the entire INS trace could be matched to the exact path on the GIS map. By way of an example only, FIG. 15 illustrates an example path shape matching result of the navigated path (INS trace) to the corresponding/matching path of the GIS map.

[0083] In various example embodiments, when the digital compass data is available, the absolute directions provided by the digital compass may be chosen as the feature and the map matching algorithm given in FIG. 9 can be used to match INS trace to corresponding paths on the GIS map. The number of paths with the same shape representation shrinks drastically compared to the INS-only map matching. Therefore this results in much faster map pre-processing and searching times.

[0084] Accordingly, various embodiments of the present invention provide a method of determining a location of a vehicle, and a location system for determining a location of a vehicle reliably based on INS measurements/tracing. The method advantageously seeks to be able to withstand or at least be less susceptible to illegitimate/intentional interferences such as intentional jamming, blocking and spoofing attacks. With more reliable determination of the location of the vehicle, the method may then be used for various purposes such as to verify location information of a vehicle providing by a positioning system (e.g., GNSS or OBU) associated with the vehicle and to detect interference on a positioning system associated with the vehicle according to various embodiments of the present invention.

[0085] While embodiments of the present invention have been particularly shown and described with reference to specific embodiments, it should be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the scope of the present invention as defined by the appended claims. The scope of the present invention is thus indicated by the appended claims and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced.

Claims

What is claimed is: 1. A method of determining a location of a vehicle, the method comprising:

2. The method according to claim 1, wherein said scaling scales at least one of said curve segment of the navigated path and said curve segment from the road network database to have substantially the same length.

3. The method according to claim 1 or 2, further comprising processing the navigated path, including extracting curve segments of the navigated path and indexing each curve segment extracted, and processing a road network from the road network database, including extracting curve segments of the road network and indexing each curve segment extracted.

4. The method according to claim 3, wherein extracting curve segments of the navigated path comprises converting the navigated path to a representation reflecting a heading angle change over distance along the navigated path and identifying the curve segments of the navigated path based on said representation of the navigated path, and extracting curve segments of the road network comprises converting a path in the road network to a representation reflecting a heading angle change over distance along the path of the road network and identifying the curve segments of the road network based on said representation of the road network.

5. The method according to claim 3 or 4, wherein each curve segment of the navigated path extracted is indexed with an identification thereof and an identification of an immediate subsequent curve segment along the navigated path, and each curve segment of the road network extracted is indexed with an identification thereof and an identification of an immediate subsequent curve segment along the road network.

6. The method according to claim 5, wherein said identifying comprises comparing the immediate subsequent curve segment of said curve segment of the navigated path with the immediate subsequent curve segment of the candidate curve segment to determine whether the immediate subsequent curve segment of the candidate curve segment resembles the immediate subsequent curve segment of said curve segment of the navigated path.

7. The method according to claim 6, wherein each curve segment of the navigated path extracted is further indexed with one or more parameters relating to its

characteristics, and each curve segment of the road network extracted is further indexed with one or more parameters relating to its characteristics, and

wherein said identifying comprises comparing at least one of the one or more parameters relating to the characteristics of the immediate subsequent curve segment of said curve segment of the navigated path with at least one of the one or more parameters relating to the characteristics of the immediate subsequent curve segment of the candidate curve segment to determine whether they resemble each other.

8. The method according to any one of claims 1 to 7, wherein said comparing said curve segment of the navigated path with a curve segment from the road network database comprises converting said curve segment of the navigated path and said curve segment from the road network database into respective tangent space curves, and comparing the respective tangent space curves, and wherein said scaling scales at least one of the tangent space curves to reduce a difference in length therebetween for the comparsion.

9. The method according to any one of claims 1 to 8, wherein

10. A method of detecting an interference on a positioning system associated with a vehicle configured to provide location information of the vehicle, the method comprising: determining the location of the vehicle using the method according to any one of claims 1 to 9; and

11. The method according to claim 10, wherein the interference comprises blocking, jamming and/or spoofing attacks on the positioning system.

12. A method of verifying location information of a vehicle provided by a positioning system associated with the vehicle, the method comprising:

determining the location of the vehicle using the method according to any one of claims 1 to 9; and

13. A location system for determining a location of a vehicle, the location system comprising:

a curve retrieval module configured to obtain a curve segment of a path navigated by the vehicle, the navigated path being estimated by an inertial navigation system associated with the vehicle;

14. The location system according to claim 13, wherein said scaling scales at least one of said curve segment of the navigated path and said curve segment from the road network database to have substantially the same length.

15. The location system according to claim 13 or 14, further comprising a processing module configured to process the navigated path, including extracting curve segments of the navigated path and indexing each curve segment extracted, and to process a road network from the road network database, including extracting curve segments of the road network and indexing each curve segment extracted.

16. The location system according to claim 15, wherein extracting curve segments of the navigated path comprises converting the navigated path to a representation reflecting a heading angle change over distance along the navigated path and identifying the curve segments of the navigated path based on said representation of the navigated path, and extracting curve segments of the road network comprises converting a path in the road network to a representation reflecting a heading angle change over distance along the path of the road network and identifying the curve segments of the road network based on said representation of the road network.

17. The location system according to claim 15 or 16, wherein each curve segment of the navigated path extracted is indexed with an identification thereof and an identification of an immediate subsequent curve segment along the navigated path, and each curve segment of the road network extracted is indexed with an identification thereof and an identification of an immediate subsequent curve segment along the road network.

18. The location system according to claim 17, wherein the curve identifying module is configured to compare the immediate subsequent curve segment of said curve segment of the navigated path with the immediate subsequent curve segment of the candidate curve segment to determine whether the immediate subsequent curve segment of the candidate curve segment resembles the immediate subsequent curve segment of said curve segment of the navigated path.

19. The location system according to claim 18, wherein each curve segment of the navigated path extracted is further indexed with one or more parameters relating to its characteristics, and each curve segment of the road network extracted is further indexed with one or more parameters relating to its characteristics, and

20. The location system according to any one of claims 13 to 19, wherein said comparing said curve segment of the navigated path with a curve segment from the road network database by the selection module comprises converting said curve segment of the navigated path and said curve segment from the road network database into respective tangent space curves, and comparing the respective tangent space curves, and wherein said scaling scales at least one of the tangent space curves to reduce a difference in length therebetween for the comparsion.

21. A computer program product, embodied in one or more computer-readable storage mediums, comprising instructions executable by one or more computer processors to perform a method of determining a location of a vehicle, the method comprising: