Method and system for generating a confidence value in a position overlap check using vehicle threshold models

Abstract

The disclosed embodiments include systems and methods performed by participants in a Vehicle-to-Everything (V2X) system to effectively identify location overlap misconduct situations by using vehicle threshold model data rather than specific second vehicle dimension data. The vehicle threshold model data may include confidence values ​​for the included dimension data, enabling the calculation of confidence levels for identifying location overlap misconduct situations. The calculated confidence values ​​can allow V2X system participants to decide whether to generate a Misconduct Detection Report (MBR) and send the MBR to a misconduct management agency.

Description

[Technical Field]

[0001] This case relates to a method and system for generating confidence values ​​in location overlap checks using a vehicle threshold model. [Previous Technology]

[0002] Intelligent Transportation Systems (ITS) aim to provide services related to different modes of transportation and traffic management, enabling users to be better informed and making the use of transportation networks safer, more coordinated, and "smarter." These transportation networks include advanced remote information processing and hybrid communications, including Internet Protocol (IP)-based communications and ad-hoc direct communications between vehicles and between vehicles and infrastructure. The evolving Collaborative ITS (C-ITS) seeks to improve road safety and pave the way for fully autonomous driving based on information exchange via C-ITS and dedicated direct wireless short-range communications for Road Transport and Traffic Remote Information Processing (RTTT).

[0003] Standards for vehicle-based communication systems and functions are being developed in various regions around the world. For example, the Institute of Electrical and Electronics Engineers (IEEE) and the Society of Automotive Engineers (SAE) are developing standards for use in North America, while the European Telecommunications Standards Institute (ETSI) and the European Committee for Standardization (CEN) are developing standards for use in Europe. Part of this system is the ability of vehicles to broadcast Basic Safety Messages (BSM) in North America or Cooperative Awareness Messages (CAM) in Europe, which other vehicles can receive and process to improve traffic safety. The processing of such BSM information in the sending and receiving vehicles occurs in onboard devices that provide Vehicle-to-Everything (V2X) functionality (referred to herein as "V2X onboard devices").

[0004] The Cellular Vehicle-to-Everything (C-V2X) protocol is one such protocol being developed as the basis for vehicle-based wireless communication. This protocol can be used to support smart highways, autonomous and semi-autonomous vehicles, and improve the overall efficiency and safety of highway transportation systems.

[0005] The C-V2X protocol defines two transmission modes that together provide 360° non-line-of-sight awareness and higher levels of predictability for enhanced road safety and autonomous driving. The first transmission mode includes direct C-V2X, which encompasses vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-pedestrian (V2P) communication and provides enhanced range and reliability in the 5.9 GHz spectrum of a dedicated intelligent transportation system (ITS) independent of cellular networks. The second transmission mode includes vehicle-to-network (V2N) communication in mobile broadband systems and technologies such as third-generation (3G) wireless mobile communication technologies (e.g., GSM-EDGE, CDMA 2000, etc.), fourth-generation (4G) wireless mobile communication technologies (e.g., LTE, LTE-Advanced, Mobile World Congress Microwave Access (Mobile WiMAX), etc.), and fifth-generation (5G NR, etc.). Other V2X wireless technologies are also being considered in different regions of the world. The technology described in this patent is applicable to any V2X wireless technology. [Summary of the Invention]

[0006] Each state includes a method executed by the processor of a V2X system participant to efficiently detect overlapping inappropriate behavior conditions by utilizing vehicle dimensions included in a preset threshold vehicle model. By utilizing vehicle dimensions included in the preset threshold vehicle model, the V2X system participant processor does not need to obtain a plurality of possible vehicle dimensions. Instead, overlapping conditions can be determined using a finite set of possible vehicle dimensions. By restricting the set of possible dimensions, overlapping conditions can be determined more efficiently.

[0007] Each state may include: determining the first vehicle position and first vehicle orientation of the first vehicle; determining the first vehicle dimension boundary, wherein the first vehicle dimension boundary is based on the first vehicle length, first vehicle width, first vehicle position and first vehicle orientation; receiving V2X messages from the second vehicle, wherein the V2X messages include the second vehicle position and second vehicle orientation; selecting a vehicle threshold model for the second vehicle from a set of vehicle threshold models, wherein the selected vehicle threshold model includes the selected vehicle threshold model length and the selected vehicle threshold model width, and the selected vehicle threshold model confidence value; determining the second vehicle dimension boundary, wherein the second vehicle dimension boundary is based on the second vehicle position, second vehicle orientation, selected vehicle threshold model length and selected vehicle threshold model width received in the V2X messages; determining whether any part of the first vehicle dimension boundary overlaps with any part of the second vehicle dimension boundary; identifying a position overlap misconduct situation in response to the determination that any part of the first vehicle dimension boundary overlaps with any part of the second vehicle dimension boundary; generating a misconduct report identifying the position overlap misconduct situation; and sending the misconduct report to the misconduct management agency.

[0008] Some states may further include: in response to the determination that the first vehicle has left the first geographic region and entered the current geographic region, selecting a vehicle threshold model from a set of vehicle threshold models for the current geographic region, wherein the set of vehicle threshold models for the current geographic region is different from the set of vehicle threshold models for the first geographic region.

[0009] Some formats may further include: a first vehicle calculating a confidence level for the identification that an overlapping misconduct situation has occurred in response to the identification of such an situation, wherein the confidence level for the identification that an overlapping misconduct situation has occurred is based on a selected vehicle threshold model confidence value. By calculating the determined confidence level for the overlapping misconduct situation, the V2X system participant processor can determine whether the detection of the overlapping misconduct situation should be confirmed by a separate entity such as a misconduct management agency. In some formats, length and width may be assigned to a second vehicle in the vehicle threshold model based on the distribution of vehicle length and width in the current geographic area. In some formats, the first vehicle may be assigned a first vehicle length and a first vehicle width from values ​​included in the selected vehicle threshold model.

[0010] In some cases, the vehicle threshold model set may include: a minimum vehicle threshold model, which includes a minimum dimension and a minimum vehicle confidence value for the minimum vehicle size in the current geographic region, wherein the minimum dimension may include the minimum vehicle length and the minimum vehicle width; and a maximum vehicle threshold model, which includes a maximum dimension and a maximum vehicle confidence value for the maximum vehicle size in the current geographic region, wherein the maximum dimension may include a maximum vehicle length greater than the actual longest vehicle length and a maximum vehicle width greater than the actual widest vehicle width.

[0011] In some cases, the selected vehicle threshold model may include the selected threshold vehicle length, the selected threshold vehicle width, and the selected threshold vehicle confidence value of the threshold vehicles in the current geographic region, wherein the selected threshold vehicle confidence value of the threshold vehicles may be the percentage of all vehicles in the current geographic region minus the percentage of vehicles in the current geographic region that have an actual length greater than the length of the vehicle threshold model and an actual width greater than the width of the vehicle threshold model, and wherein the calculated confidence value is equal to the percentage of vehicles smaller than the minimum dimension minus the selected vehicle threshold model confidence value of the threshold vehicles in the current geographic region.

[0012] In some cases, the vehicle threshold model set may include a minimum vehicle threshold model, which may include a minimum dimension and a minimum vehicle confidence value for the minimum vehicle size in the current geographic region, wherein the minimum dimension may include a minimum vehicle length less than the actual shortest vehicle length and a minimum vehicle width less than the actual narrowest vehicle width; and a maximum vehicle threshold model, which may include a maximum dimension and a maximum vehicle confidence value for the maximum vehicle size in the current geographic region, wherein the maximum dimension may include the maximum vehicle length and the maximum vehicle width.

[0013] In some cases, the selected vehicle threshold model may include the selected threshold vehicle length, the selected threshold vehicle width, and the selected threshold vehicle confidence value for the threshold vehicles in the current geographic region. The selected threshold vehicle confidence value may be the percentage of all vehicles in the current geographic region minus the percentage of vehicles in the current geographic region that have an actual length greater than the length of the vehicle threshold model and an actual width greater than the width of the vehicle threshold model. The calculated confidence level is equal to the percentage of vehicles larger than the maximum dimension minus the selected vehicle threshold model confidence value for the threshold vehicles in the current geographic region.

[0014] A further embodiment may include a V2X system having a processor configured to perform one or more operations of any of the methods summarized above. A further embodiment may include a V2X system having components for performing any of the methods summarized above. A further embodiment may include a non-transitory processor-readable storage medium having processor-executable instructions stored thereon configured to cause the V2X system processor to perform operations of any of the methods summarized above.

Implementation Method

[0030] Various embodiments will be described in detail with reference to the accompanying drawings. Wherever possible, the same reference numerals used throughout the drawings will denote the same or similar parts. References to specific instances and implementations are for illustrative purposes and are not intended to limit the scope of the claims.

[0031] As used herein, the term "mobile device" means any or all of the following: wireless router devices, wireless electrical appliances, cellular phones, smartphones, portable computing devices, personal or mobile multimedia players, laptops, tablets, smart computers, ultrabooks, handheld computers, wireless email receivers, cellular phones that support multimedia internet, medical devices and equipment, biometric sensors / devices, wearable devices (including smartwatches, smart clothing, smart glasses, smart wristbands, smart jewelry (e.g., smart rings, smart bracelets, etc.)), entertainment devices (e.g., wireless game controllers, music and video players, satellite radios, etc.), Internet of Things (IoT) devices that support wireless networks (including smart meters / sensors, industrial manufacturing equipment, large and small machinery and electrical appliances used in homes or businesses, wireless communication elements in autonomous and semi-autonomous vehicles, mobile devices attached to or incorporated into various mobile platforms, GPS devices, and similar electronic devices including memory, wireless communication components, and programmable processors).

[0032] The term "system-on-chip (SOC)" as used herein refers to a single integrated circuit (IC) chip comprising a plurality of resources and / or processors integrated on a single substrate. A single SOC can contain circuits for digital, analog, mixed-signal and radio frequency functions. A single SOC can also include any number of general-purpose and / or dedicated processors (digital signal processors, modem processors, video processors, etc.), blocks (e.g., ROM, RAM, flash memory, etc.), and resources (e.g., timers, voltage regulators, oscillators, etc.). SOCs can also include software used to control integrated resources and processors, and software used to control peripherals.

[0033] The term "system-level package (SIP)" may be used herein to refer to a single module or package containing multiple resources, computing units, cores, and / or processors on two or more IC chips, substrates, or SOCs. For example, a SIP may include a single substrate on which multiple IC wafers or semiconductor grains are stacked in a vertical configuration. Similarly, a SIP may include one or more multi-chip modules (MCMs) on which a plurality of IC or semiconductor chips are encapsulated in a unified substrate. The SIP may also include a plurality of separate SOCs coupled together via a high-speed communication circuit and extremely closely packaged in, for example, a single motherboard or a single mobile device. The proximity of SOC favors high-speed communication and memory and resource sharing.

[0034] As used in this case, the terms "part," "system," "unit," "module," or the like, include entities relating to a computer, such as, but not limited to, hardware, firmware, combination of hardware and software, software, or software in execution that are configured to perform a particular operation or function. For example, a part may be, but is not limited to, a program, processor, object, executable, thread, program, and / or computer running on the processor. By way of illustration, both the application performed on the communication equipment and the communication equipment may be referred to as components. One or more components may reside in a program and / or execution thread, and the components may be located on one processor or core and / or distributed between two or more processors or cores. In addition, these parts can be executed from various non-temporary computer-readable media having various instructions and / or data structures stored thereon. Parts may communicate via local and / or remote programs, function or program dialing, electronic signals, data packets, memory read / write, and other known communication methods related to computers, processors, and / or programs.

[0035] In general, various embodiments include methods and systems for effectively detecting when an overlap misbehavior condition has occurred and determining a confidence level for detecting the overlap misbehavior condition. In V2X communications, misbehavior conditions can be detected by analyzing various sensor data to ensure that the vehicle operates in a consistent manner. One type of obvious misbehavior condition is a location overlap condition or a simple overlap condition. An overlap misbehavior condition occurs when the dimensional boundary of one entity (e.g., a vehicle or pedestrian) overlaps with the dimensional boundary of another entity. This condition indicates that these entities have collided or come into contact with each other. If the vehicle is continuing to move, it is likely that the vehicles are not actually overlapping, and therefore indicate that one or the other of the vehicle's location and / or boundary dimensions is unreliable and thus exhibits an overlap misbehavior condition.

[0036] In most cases, there may be a wide variety of entity dimensions. For example, different types of entities (e.g., vehicles, pedestrians, trucks, motorcycles, and bicycles) have significantly different dimensions, particularly different lengths and widths. Furthermore, different models of vehicles have different dimensions (length and width). Even vehicles of the same model can be customized to have different dimensions (e.g., length and width). Therefore, it is helpful to have an accurate understanding of each of these varying dimensions of two or more entities (e.g., vehicles) to accurately determine whether overlapping misconduct is occurring. Given the variability of entity dimensions, storing the dimensions of every possible entity that might be encountered in the ITS may be too costly or resource-intensive. Although entity dimensions can be estimated, such estimation may lead to inaccurate misconduct detection. Therefore, to improve the accuracy of the V2X system, it may be beneficial to determine a confidence level for detecting misconduct. For example, in cases where the confidence level for detecting misconduct is below a threshold level, the detection of the occurrence of misconduct can be sent to a misconduct management agency for confirmation of the misconduct.

[0037] An overlap condition may occur when a portion of the dimensional boundary of one entity overlaps with the dimensional boundary of another entity in any of its length, width, and height. For ease of reference, various embodiments are described in the context of surface travel (i.e., roads), where the length and width dimensions are critical, and thus the vehicle dimension may be referred to as length and width. However, in some applications, overlap in all three dimensions will be important, such as for flying drones, airplanes, and ships. The various embodiments disclosed herein are not intended to be limited to surface travel. Various embodiments may include three-dimensional boundaries based on the length, width, and height of an entity.

[0038] In some situations, actual overlap may occur (e.g., collision, accident, etc.). However, vehicles generally attempt to avoid collisions and thus avoid overlap. Nevertheless, a V2X system can detect overlap when positioning and / or orientation sensors malfunction and provide inaccurate positioning and / or orientation data for entities (e.g., vehicles or pedestrians). Similarly, a V2X system can detect overlap when a malicious actor compromises the V2X system and injects corrupt or inaccurate entity positioning data into the system. Therefore, in cases where sensor data supporting the conclusion that an overlap has occurred is inaccurate, the detection of the overlap may be inaccurate, indicating an improper behavior. Therefore, detecting an overlap inconsistent with other information, such as a vehicle continuing to travel at a normal speed, can be considered as detecting an improper behavior.

[0039] In response to the detection of misconduct, a Misconduct Status Report (MBR) can be generated and sent to the misconduct management agency to confirm that the misconduct has actually occurred. The MBR may contain sensor data supporting the conclusion that a misconduct has occurred. In some cases, the misconduct management agency can analyze the received MBR and supporting sensor data to determine that various sensors are malfunctioning and require replacement or repair. In other cases, the misconduct management agency can analyze the received MBR and supporting sensor data to determine that a malicious actor may have infiltrated the V2X system and compromised sensor data.

[0040] As more and more V2X systems come online, several potential MBRs are being generated and sent, which may become overwhelming. Therefore, it may be desirable to calculate the accurate confidence level of the detected misconduct status before the MBR is generated and sent to the misconduct management agency.

[0041] V2X systems and technologies promise to improve traffic flow and vehicle safety for collision and other safety functions by enabling vehicles to share information about their location, speed, direction of travel, braking, and other factors that may be useful to other vehicles. Vehicles equipped with V2X / V2V onboard units will frequently (e.g., up to 20 times per second) transmit their vehicle information in packets known as Basic Safety Messages (BSM) or Cooperative Awareness Messages (CAM). With all V2X-equipped vehicles transmitting such BSM / CAM messages, all receiving vehicles have the information needed to control their own speed and direction to avoid collisions and to position themselves effectively and safely relative to each other. It is conceivable that V2X-equipped vehicles may be able to improve traffic flow by safely reducing spacing, queuing several vehicles together, and preventing vehicle malfunctions.

[0042] For ease of reference, some embodiments described herein are performed by V2X system participants operating within the V2X proper nouns. However, it should be understood that various embodiments cover any or all V2X / V2V or vehicle-based communication standards, messages, or technologies. Therefore, nothing in this application should be construed as limiting the claims to V2X / V2V systems unless expressly stated in the claims. Furthermore, the embodiments described herein discuss onboard equipment for performing V2X / V2V communications. In a V2X / V2V system, system participant equipment may include, but is not limited to, onboard equipment, mobile devices, and roadside units (RSUs) of a vehicle. RSUs may include fixed equipment such as traffic signals, roadside beacons, traffic cameras, etc. Each system participant equipment may broadcast information to other system participant equipment. For example, a vehicle may include onboard / dashboard equipment and sensors that report vehicle status (e.g., location, orientation, speed, latitude, etc.). Mobility devices carried by pedestrians or vehicle riders (e.g., motorcyclists, car riders, bicycle riders) may include sensors that report information about pedestrian conditions (e.g., location, orientation, speed, latitude, etc.). Each of the vehicle, pedestrian, and RSU can be a V2X system participant. A processor included in a dashboard / vehicle unit or mobility device can be considered a V2X system participant processor. V2X communication between V2X system participant equipment can allow applications executed on each V2X system participant equipment to provide safety applications (e.g., applications that can determine imminent dangers (such as sudden braking of a vehicle or speeding on a tactile paving path)) or mobility (planning traffic signal changes) for vehicles and pedestrians, or to provide other useful functions within the vehicle transportation system as a whole. For ease of reference, the various embodiments described below discuss vehicles (e.g., automobiles). This discussion is not intended to limit any embodiments used with vehicles. Rather, the embodiments described herein can be used by any V2X system participant to detect overlap with any other V2X system participant.

[0043] Misconduct reporting is a critical component of safety systems used for V2X communications. In misconduct reporting, field devices—vehicles or roadside units (RSUs)—observe V2X messages, determine which V2X message contents are inconsistent with the sum of sensor and observation data from V2X system participants, and generate a misconduct report (MBR), which can be sent to a misconduct management authority. In cases where V2X messages are inconsistent with the sum of vehicle sensor and observation data, misconduct can be detected and an MBR can be generated. The misconduct management authority can aggregate MBRs from different reporting V2X system participants across its jurisdiction and determine possible responses to the MBRs. Potential responses could be wide-ranging, including: determining that the MBR did not actually report a valid misconduct condition; determining that the reported MBR misconduct condition was actual, but the damage was so minor that fixing it would be more costly than letting it continue; determining that the V2X participant had bad software and needed to be updated; determining that the signing keys associated with the V2X participant had been extracted from the V2X system participant and used to carry out a nationwide bad message attack, and therefore the device keys need to be revoked so that no one trusts them anymore, etc.

[0044] In most V2X systems, the misconduct management authority can require sufficient evidence to verify / confirm the accuracy of the generated MBR, that is, if the presented evidence is correct, then the misconduct situation reported in the MBR is indeed misconduct. Sufficient evidence may vary depending on the specific type of misconduct situation. For example, an MBR claiming to be from a V2X participant traveling at 1000 mph can itself be considered a misconduct situation without any evidence, since no known vehicle operating within the V2X system can reach this speed. In other cases, the reporting V2X participant (i.e., the first vehicle) can be requested to send additional data, such as sensor data—for example, in such cases where the reported V2X message is from a vehicle claiming to be near the reporting V2X participant (i.e., the second vehicle), but the reporting V2X participant's sensor data did not detect any of this nearby second vehicle. Therefore, the reporting V2X participant (e.g., the first vehicle) receiving the original V2X message can determine that the allegedly nearby second vehicle has engaged in misconduct. However, in many cases, while including sensor data may raise concerns about the credibility of the reporter, including this sensor data may allow misconduct management agencies to obtain a more complete picture of potential misconduct, and therefore including this supporting sensor data with MBR should be standard practice.

[0045] A clearly inappropriate behavioral condition that can be detected by a V2X system is position overlap. Since position overlap in reality indicates a collision or impending collision between two vehicles, vehicles maneuver to avoid it. To determine whether position overlap has occurred, it is necessary to know the position, orientation, and dimensions of both the first and second vehicles. Simplifying any of these variables can allow for more effective detection of position overlap conditions. Therefore, various embodiments disclosed herein include methods for simplifying the detection of position overlap conditions by limiting the number of possible vehicle dimensions that need to be evaluated. While such limitation can allow for more effective detection of position overlap conditions, such detection may be inaccurate. Therefore, various embodiments include methods for determining a confidence level for the detection of position overlap conditions.

[0046] Various embodiments can be implemented in various V2X system participants, with Figures 1A and 1B illustrating an example vehicle 101. Referring to Figures 1A and 1B, vehicle 101 may include a control unit 140 and a plurality of sensors 144-170, including a satellite geolocation system receiver 142, occupancy sensors 144, 146, 148, 150, 152, tire pressure sensors 154, 156, cameras 158, 160, microphones 162, 164, a collision sensor 166, radar 168, and lidar 170. The plurality of sensors 144-170 disposed in or on the vehicle can be used for various purposes, such as autonomous and semi-autonomous navigation and control, collision avoidance, location determination, etc., and to provide sensor data about objects and people in or on vehicle 101. Sensors 144-170 may include one or more of a variety of sensors capable of detecting various information useful for navigation and collision avoidance. Each of sensors 144-170 can communicate with control unit 140 and with each other via wired or wireless means. Specifically, sensors may include one or more cameras 158, 160, or other optical or photoelectric sensors. Sensors may further include other types of object detection and ranging sensors, such as radar 168, lidar 170, IR sensors, and ultrasonic sensors. The sensors may further include tire pressure sensors 154, 156, humidity sensors, temperature sensors, satellite geolocation sensors 142, control input sensors 145, accelerometers, vibration sensors, gyroscopes, gravimeters, impact sensors 166, force gauges, stress gauges, strain sensors, fluid sensors, chemical sensors, gas content analyzers, pH sensors, radiation sensors, Geiger counters, neutron detectors, biomaterial sensors, microphones 162, 164, occupancy sensors 144, 146, 148, 150, 152, proximity sensors, and other sensors.

[0047] The vehicle control unit 140 may be configured to have processor-executable instructions to perform navigation and collision avoidance operations using information received from various sensors, particularly cameras 158 and 160. In some embodiments, the control unit 140 may supplement the processing of camera images with distance and relative position (e.g., relative azimuth) obtainable from radar 168 and / or lidar 170 sensors. The control unit 140 may be further configured to control the steering, braking, and speed of the vehicle 101 when operating in autonomous or semi-autonomous mode using information about other vehicles determined using various embodiments.

[0048] FIG1C is a component block diagram illustrating a communication system 100 suitable for implementing various embodiments of components and support systems. Referring to FIGS. 1A to 1C, vehicle 101 may include a control unit 140, which may include various circuits and devices for controlling the operation of vehicle 101. In the example shown in FIG1D, control unit 140 includes processor 140a, memory 140b, input module 140c, output module 140d, and radio module 140e. Control unit 140 may be coupled to drive control component 172a, navigation component 172b, and one or more sensors 172c of vehicle 101 and may be configured to control drive control component 172a, navigation component 172b, and one or more sensors 172c. Processor 140a may be configured to have processor-executable instructions to control the handling, navigation, and / or other operations of vehicle 101, including operations of various embodiments. Processor 140a may be coupled to memory 140b.

[0049] Radio module 140e can be configured for wireless communication. Radio module 140e can exchange signals (e.g., command signals for control operation, signals from navigation facilities, etc.) with a network transceiver (e.g., base station 110) via communication link 122, and can provide signals to processor 140a and / or navigation unit 172b. In some embodiments, radio module 140e can enable vehicle 101 to communicate with wireless communication device 120 via wireless communication link 124. As described, wireless communication link 124 can be a bidirectional or unidirectional communication link, and can use one or more communication protocols.

[0050] Input module 140c can receive sensor data from one or more vehicle sensors 172c, as well as electronic signals from other components, including drive control unit 172a and navigation unit 172b. Output module 140d can communicate with or activate various components of vehicle 101, including drive control unit 172a, navigation unit 172b and sensors(s)172c.

[0051] The control unit 140 may be coupled to the drive control component 172a to control physical components of the vehicle 101 related to vehicle handling and navigation, such as the engine, electric motor, throttle, steering components, flight control components, braking or deceleration components, etc. The drive control component 172a may also include components that control other equipment of the vehicle, including environmental controls (e.g., air conditioning and heating), exterior and / or interior lighting, interior and / or exterior information displays (which may include display screens or other devices for displaying information), safety devices (e.g., haptic devices, audible alarms, etc.) and other similar devices.

[0052] Control unit 140 may be coupled to navigation component 172b, and may receive data from navigation component 172b and may be configured to use this data to determine the current position and orientation of vehicle 101, as well as the appropriate route toward the destination. Navigation component 172b may include or be coupled to a Global Navigation Satellite System (GNSS) receiver system (e.g., one or more Global Positioning System (GPS) receivers), enabling vehicle 101 to use GNSS signals to determine its current position. Alternatively, or additionally, navigation component 172b may include a radio navigation receiver for receiving navigation beacons or other signals from radio nodes such as Wi-Fi access points, cellular network sites, radio stations, remote computing devices, other vehicles, etc. Through control of drive control component 172a, processor 140a may control vehicle 101 for navigation and maneuvering. The processor 140a and / or navigation component 172b can be configured to communicate with network components (such as servers) via wireless communication links 122, 126 in a communication network (e.g., core network 132) to receive commands for control and manipulation, receive data useful for navigation, provide real-time location reports, and evaluate other data.

[0053] The control unit 140 may be coupled to one or more sensors 172c. The sensors 172c may include sensors 144-170 as described, and may be configured to provide various data to the processor 140a.

[0054] Although the control unit 140 is described as comprising separate components, in some embodiments, some or all of the components (e.g., processor 140a, memory 140b, input module 140c, output module 140d, and radio module 140e) may be integrated into a single device or module, such as a system-on-a-chip (SOC) processing device. This SOC processing device may be configured for use in a vehicle and is configured to have processor-executable instructions, such as those executed in processor 140a, to perform navigation and collision avoidance operations using Local Dynamic Map (LDM) data when installed in a vehicle.

[0055] Figure 1D illustrates a portion of a V2X system 103, comprising three vehicles, 12, 14, and 16. In the illustrated example, each vehicle 12, 14, and 16 includes V2X onboard units 102, 104, and 106, respectively, configured to periodically broadcast basic safety messages 30, 40, and 50 for reception and processing by the onboard units of the other vehicles (e.g., 102, 104, and 106). By sharing vehicle positioning, speed, direction, braking, and other information, the vehicles can maintain a safe distance and identify and avoid potential collisions. For example, a following vehicle 12 receiving basic safety message 40 from the leading vehicle 16 can determine the speed and positioning of vehicle 16, thereby enabling vehicle 12 to match its speed and maintain a safe distance 20. Upon receiving basic safety information 40 when the leading vehicle 16 brakes, the V2X device 102 in the following vehicle 12 can simultaneously apply its brakes to maintain a safe distance 20 even if the leading vehicle 16 comes to a sudden stop. As another example, the V2X device 104 in the truck 14 can receive basic safety information 30 and 50 from both vehicles 12 and 16, and thus know that the truck 14 should stop at the intersection to avoid a collision. Each of the vehicle V2X onboard devices 102, 104, and 106 can communicate with each other using any of the various short-range communication protocols. Additionally, vehicles may be able to send data and information regarding detected basic safety information and detected misconduct reports to the original equipment manufacturer (OEM) (70, 72) and / or a remote misconduct management agency 74 via communication links 60 and 62 through communication network 18 (e.g., cellular, WiFi, etc.). The MBR can be sent directly to the misconduct management agency 74 (e.g., via communication links 64 and 66). In other embodiments, the MBR may first be sent to an MBR preprocessing unit, such as OEM servers 70, 72, via communication links 64, 66 for preprocessing. Subsequently, the preprocessed MBR may be sent from the MBR preprocessing servers 70, 72 to the misconduct management agency 74 via communication links 64, 66. In other embodiments, the MBR may be received from a vehicle (such as vehicle 16) at a remote misconduct management agency 74. The remote misconduct management agency 74 may relay the received MBR from vehicle 16 to OEM servers 70, 72 via communication links 64, 66. Additionally, OEM servers 70, 72 may provide an acknowledgment report to the remote misconduct management agency 74 via communication links 64, 66.

[0056] FIG2A is a block diagram illustrating the components of an example misbehavior management system 200. The vehicle management system 200 may include various subsystems, communication components, computing components, computing devices, or units that may be utilized within the vehicle 101. Referring to FIGS. 1A to 2A, the various computing components, computing devices, or units within the misbehavior management system 200 may be implemented within a system of interconnected computing devices (i.e., subsystems) that communicate data and commands to each other (e.g., as indicated by the arrows in FIG2A). In some implementations, the various computing components, computing devices, or units within the misbehavior management system 200 may be implemented within a single computing device, such as separate threads, programs, algorithms, or computing components. Thus, each subsystem / computing component shown in FIG2A is generally referred to herein as a "layer" within a computing "stack" constituting the misbehavior management system 200. However, the use of the terms layer and stack in describing various embodiments is not intended to imply or require that the corresponding functionality be implemented within a single autonomous (or semi-autonomous) vehicle management system computing device, although this is a potential implementation embodiment. Conversely, the term "layer" is intended to encompass subsystems with independent processors, computational components (e.g., threads, algorithms, subroutines, etc.) that execute in one or more computing devices, and combinations of subsystems and computational components.

[0057] The misbehavior management system stack may include a radar perception layer 202, a camera perception layer 204, a localization engine layer 206, a map fusion and arbitration layer 208, a route planning layer 210, a sensor fusion and road world model (RWM) management layer 212, a motion planning and control layer 214, and a behavior planning and prediction layer 216. Layers 202-216 are merely examples of some layers in one instance configuration of the misbehavior management system stack 200. In other configurations, other layers may be included, such as additional layers for other perception sensors (e.g., a LiDAR perception layer), additional layers for planning and / or control, additional layers for modeling, etc., and / or some layers of layers 202-216 may be excluded from the misbehavior management system stack 200. Each of layers 202-216 can exchange data, calculation results, and commands, as indicated by the arrows in Figure 2A. Furthermore, the misbehavior management system stack 200 can receive and process data from sensors (such as radar, lidar, cameras, inertial measurement units (MUs), etc.), navigation systems (e.g., GPS receivers, IMUs, etc.), vehicle networks (e.g., Controller Area Network (CAN) buses), and databases in memory (e.g., digital map data). The misbehavior management system stack 200 can output vehicle control commands or signals to the drive-by-wire (DBW) system / control unit 220, which is a system, subsystem, or computing device that directly interfaces with the vehicle's steering, throttle, and brake control interfaces. The configurations of the misbehavior management system stack 200 and the DBW system / control unit 220 shown in Figure 2A are merely example configurations, and other configurations of the vehicle management system and other vehicle components can be used. As examples, the configurations of the misbehavior management system stack 200 and the DBW system / control unit 220 shown in Figure 2A can be used in vehicles configured for autonomous or semi-autonomous operation, while different configurations can be used in non-autonomous vehicles.

[0058] The radar perception layer 202 may receive data from one or more detection and ranging sensors (such as radar (e.g., 132) and / or lidar (e.g., 138)) and process the data to identify and determine the location of other vehicles and objects in the vicinity of vehicle 100. The radar perception layer 202 may include the use of neural network processing and artificial intelligence methods to identify objects and vehicles and pass this information to the sensor fusion and RWM management layer 212.

[0059] The camera perception layer 204 can receive data from one or more cameras, such as cameras (e.g., 158, 160), and process the data to identify and determine the location of other vehicles and objects near vehicle 100. The camera perception layer 204 may include the use of neural network processing and artificial intelligence methods to identify objects and vehicles, and pass this information to the sensor fusion and RWM management layer 212.

[0060] The positioning engine layer 206 can receive data from various sensors and process the data to determine the position of the vehicle 100. These various sensors may include, but are not limited to, GPS sensors, IMUs, and / or other sensors connected via a CAN bus. The positioning engine layer 206 may also utilize input from one or more cameras, such as cameras (e.g., 158, 160) and / or any other available sensors, such as radar, LiDAR, etc.

[0061] The misconduct management system 200 may include or be coupled to the vehicle wireless communication subsystem 230. The wireless communication subsystem 230 may be configured to communicate with other vehicle computing devices and highway communication systems, such as via vehicle-to-vehicle (V2V) communication links, and / or with remote information resources (such as cloud-based resources) via cellular wireless communication systems (such as 5G networks). In various embodiments, the wireless communication subsystem 230 may communicate with other V2X system participants via wireless communication links to receive V2X messages and sensor data that may support conclusions regarding the detection of misconduct.

[0062] The map fusion and arbitration layer 208 can access sensor data received from other V2X system participants and receive output received from the positioning engine layer 206, and process the data to further determine the position of vehicle 101 within the map, such as its position within a traffic lane, its position within a street map, etc. The sensor data can be stored in memory (e.g., memory 312). For example, the map fusion and arbitration layer 208 can convert latitude and longitude information from GPS into a position within a surface map of the road contained in the sensor data. GPS position fixes include errors, so the map fusion and arbitration layer 208 can use this to determine the best guessed position of the vehicle within the road based on arbitration between GPS coordinates and sensor data. For example, although GPS coordinates may place the vehicle near the middle of a two-lane road in the sensor data, the map fusion and arbitration layer 208 can determine from the direction of travel that the vehicle is most likely aligned with the lane traveling in the same direction of travel. The map fusion and arbitration layer 208 can pass map-based positioning information to the sensor fusion and RWM management layer 212.

[0063] Route planning layer 210 may utilize sensor data, as well as input from an operator or scheduler, to plan the route along the 101 vehicle to a particular destination. The route planning layer 210 can communicate map-based positioning information to the sensor fusion and RWM management 212 . However, other layers (such as sensor fusion and RWM management212, etc.) are not required to use previous maps. For example, other stacks can operate and / or control vehicles based solely on perceptual data, without the notion of provided maps, constructing lanes, borders, and local maps upon receiving perceptual data.

[0064] The sensor fusion and RWM management 212 may receive data and output generated by the radar perception layer 202 , the camera perception layer 204 , the map fusion and arbitration layer 208 , and the route planning layer 210 , and use some or all of these inputs to estimate or refine the positioning and state of the vehicle 101 in relation to the road, other vehicles on the road, and other objects in the vicinity of the vehicle 100 . For example, the sensor fusion and RWM management 212 may combine image data from the camera perception layer 204 with arbitrated map positioning information from the map fusion and arbitration layer 208 to refine the determined position of the vehicle within the traffic lane. As another instance, the sensor fusion and RWM management 212 may combine object recognition and imagery data from the camera perception layer 204 with object detection and ranging data from the radar perception layer 202 to determine and refine the relative positions of other vehicles and objects in the vicinity of the vehicle. As another example, sensor fusion and RWM management 212 can receive information about the location and direction of travel of other vehicles from vehicle-to-vehicle (V2V) communication (e.g. via CAN bus) and combine that information with information from the radar perception layer 202 and camera perception layer 204 to refine the positioning and motion of other vehicles. The sensor fusion and RWM management 212 may output refined positioning and status information of vehicle 100 and refined positioning and status information of other vehicles and objects in the vicinity of the vehicle to the motion planning and control layer 214 and / or the behavioral planning and prediction layer 216 .

[0065] As another example, sensor fusion and RWM management 212 may use dynamic traffic control commands that guide vehicle 101 to change speed, lane, direction of travel, or other navigation elements, and combine that information with other received information to determine refinement positioning and status information. Sensor fusion and RWM management 212 may output refined positioning and status information of vehicle 101, as well as refined positioning and status information of vehicle 101 in the vicinity of vehicle 100, via wireless communications (e.g. via C-V2X connections, other wireless connections, etc.), to motion planning and control layers 214 , behavior planning and prediction layers 216 , and / or equipment far away from vehicle 101 , such as data servers, other vehicles, etc.

[0066] As another example, the sensor fusion and RWM management layer 212 can monitor perception data from various sensors (such as perception data from radar perception layer 202, camera perception layer 204, other perception layers, etc., and / or data from one or more sensors themselves) to analyze the status in the vehicle sensor data. The sensor fusion and RWM management layer 212 can be configured to detect status in the sensor data, such as sensor measurements being at, above, or below thresholds, certain types of sensor measurements occurring, etc., and can output the sensor data as part of the refined positioning and status information of the vehicle 101 provided to the behavior planning and prediction layer 216 and / or devices remote to the vehicle 100 (such as data servers, other vehicles, etc.) via wireless communication (such as via C-V2X connection, other wireless connection, etc.).

[0067] Detailed location and status information may include vehicle descriptors associated with the vehicle and its owner and / or operator, such as: vehicle specifications (e.g., size, weight, color, onboard sensor type, etc.); vehicle position, speed, acceleration, direction of travel, attitude, orientation, destination, (multiple) fuel / power levels and other status information; vehicle emergency status (e.g., whether the vehicle is an emergency vehicle or a private individual in an emergency); vehicle constraints (e.g., heavy / wide load, turning constraints, high occupancy vehicle (HOV) authorization, etc.); vehicle capabilities (e.g., all-wheel drive, four-wheel drive, snow tires, snow chains, supported connectivity types, onboard sensor operating status, onboard sensor resolution level, etc.); equipment issues (e.g., low tire pressure, weak braking, sensor shutdown, etc.); owner / operator driving preferences (e.g., preferred lanes, roads, routes and / or destinations, preferences for avoiding tolls or highways, preferences for the fastest route, etc.); permission to provide sensor data to a data agent server (e.g., 184); and / or owner / operator identification information.

[0068] The behavior planning and prediction layer 216 of the autonomous vehicle system stack 200 can use refined positioning and state information of vehicle 101 and positioning and state information of other vehicles and objects output from sensor fusion and RWM management layer 212 to predict the future behavior of other vehicles and / or objects. For example, the behavior planning and prediction layer 216 can use this information to predict the future relative position of other vehicles near the vehicle based on its own vehicle position and speed and the positions and speeds of other vehicles. This prediction can take into account information from LDM data and route planning to predict changes in relative vehicle position as the master vehicle and other vehicles follow the road. The behavior planning and prediction layer 216 can output the behavior of other vehicles and objects, as well as positioning predictions, to the motion planning and control layer 214. In addition, the behavior planning and prediction layer 216 can use object behavior, combined with positioning predictions, to plan and generate control signals for controlling the movement of vehicle 101. For example, based on route planning information, detailed positioning in road information, and the relative positioning and movement of other vehicles, the behavior planning and prediction layer 216 can determine that vehicle 101 needs to change lanes and accelerate, such as to maintain or achieve a minimum distance from other vehicles, and / or prepare to turn or leave. As a result, the behavior planning and prediction layer 216 can calculate or otherwise determine the steering angle for the wheels and the change in throttle settings to be commanded to the motion planning and control layer 214 and the DBW system / control unit 220, along with various parameters required to accomplish this lane change and acceleration. One such parameter could be the calculated steering wheel command angle.

[0069] The motion planning and control layer 214 can receive data and information output from the sensor fusion and RWM management layer 212, as well as other vehicle and object behaviors and positioning predictions from the behavior planning and prediction layer 216, and use this information to plan and generate control signals for controlling the motion of the vehicle 101 and verify that such control signals meet the safety requirements for the vehicle 100. For example, based on route planning information, refined positioning in road information, and the relative positioning and motion of other vehicles, the motion planning and control layer 214 can verify various control commands or instructions and transmit them to the DBW system / control unit 220.

[0070] The DBW system / control unit 220 can receive commands or instructions from the motion planning and control layer 214 and convert this information into mechanical control signals for controlling the wheel angles, braking, and throttle of the vehicle 100. For example, the DBW system / control unit 220 can respond to the calculated steering wheel command angle by sending a corresponding control signal to the steering wheel controller.

[0071] In various embodiments, the wireless communication subsystem 230 can communicate with other V2X system participants via a wireless communication link to send sensor data, location data, vehicle data, and data about the vehicle's surrounding environment collected by onboard sensors. This information can be used by other V2X system participants to update stored sensor data for relay to other V2X system participants.

[0072] In various embodiments, the misconduct management system stack 200 may include the ability to perform safety checks or oversight of various commands, plans, or other decisions across various layers that may affect vehicle and occupant safety. This safety check or oversight function may be implemented within a dedicated layer or distributed across various layers and included as part of the functionality. In some embodiments, various safety parameters may be stored in memory, and the safety check or oversight function may compare determined values ​​(e.g., relative distance to nearby vehicles, distance to the road centerline, etc.) with corresponding safety parameters(s), and issue alarms or commands if safety parameters are violated or are about to be violated. For example, the safety or supervisory functions in the behavior planning and prediction layer 216 (or a separate layer) can determine the current or future distance between another vehicle (as defined by the sensor fusion and RWM management layer 212) and the vehicle (e.g., based on a world model refined by the sensor fusion and RWM management layer 212), compare this distance with a safe distance parameter stored in memory, and issue instructions to the motion planning and control layer 214 to accelerate, decelerate, or turn if the current or predicted distance violates the safe distance parameter. As another example, the safety or supervisory functions in the motion planning and control layer 214 (or a separate layer) can compare the determined or commanded steering wheel command angle with a safe wheel angle limit or parameter, and issue an overdrive command and / or alarm in response to the commanded angle exceeding the safe wheel angle limit.

[0073] Some safety parameters stored in memory can be static (i.e., do not change over time), such as maximum vehicle speed. Other safety parameters stored in memory can be dynamic because these parameters are continuously or periodically determined or updated based on vehicle status information and / or environmental conditions. Non-limiting examples of safety parameters include maximum safe speed, maximum braking pressure, maximum acceleration, and safe wheel angle limits, all of which can be functions of road and weather conditions.

[0074] Figure 2B illustrates an example of a subsystem, computing component, computing device, or unit within a vehicle management system 250, which may be utilized within a vehicle 101. Referring to Figures 1A through 2B, in some embodiments, layers 202, 204, 206, 208, 210, 212, and 216 of the misbehavior management system stack 200 may be similar to those layers described with reference to Figure 2A, and the misbehavior management system stack 250 may operate similarly to the misbehavior management system stack 200, except that the misbehavior management system stack 250 may transmit various data or instructions to the vehicle safety and collision avoidance system 252 instead of the DBW system / control unit 220. For example, the configuration of the misbehavior management system stack 250 and the configuration of the vehicle safety and collision avoidance system 252 shown in Figure 2B may be used in a non-autonomous vehicle.

[0075] In various embodiments, the behavior planning and prediction layer 216 and / or the sensor fusion and RWM management layer 212 may output data to the vehicle safety and collision avoidance system 252. For example, the sensor fusion and RWM management layer 212 may output sensor data as part of the refined location and status information of vehicle 101 provided to the vehicle safety and collision avoidance system 252. The vehicle safety and collision avoidance system 252 may use the refined location and status information of vehicle 101 to make safety decisions relative to the occupants of vehicle 101 and / or vehicle 100. As another example, the behavior planning and prediction layer 216 may output behavior models and / or predictions related to the motion of other vehicles to the vehicle safety and collision avoidance system 252. The vehicle safety and collision avoidance system 252 may use the behavior models and / or predictions related to the motion of other vehicles to make safety decisions relative to vehicle 101 and / or the occupants of vehicle 101.

[0076] In various embodiments, the vehicle safety and collision avoidance system 252 may include the ability to perform safety checks or oversight on various commands, plans, or other decisions at various levels that may affect the safety of the vehicle and its occupants. In some embodiments, various safety parameters may be stored in memory, and the vehicle safety and collision avoidance system 252 may compare determined values ​​(e.g., relative distance to nearby vehicles, distance to the centerline of the road, etc.) with corresponding safety parameters(s), and issue warnings or commands if safety parameters are violated or will be violated. For example, the vehicle safety and collision avoidance system 252 may determine the current or future distance between another vehicle (e.g., defined by the sensor fusion and RWM management layer 212) and another vehicle (e.g., based on a world model refined by the sensor fusion and RWM management layer 212), compare that distance with a safe distance parameter stored in memory, and issue an acceleration, deceleration, or turning command to the driver if the current or predicted distance violates the safe distance parameter. As another example, the vehicle safety and collision avoidance system 252 can compare changes in the steering wheel angle made by a human driver with safe wheel angle limits or parameters, and issue an over-control command and / or alarm in response to the steering wheel angle exceeding the safe wheel angle limit.

[0077] Figure 3 illustrates an example system-on-a-chip (SOC) architecture suitable for implementing various embodiments of a processing device SOC 300 in a vehicle. Referring to Figures 1A through 3, the processing device SOC 300 may include several heterogeneous processors, such as a digital signal processor (DSP) 303, a modem processor 304, an image and object recognition processor 306, a mobile display processor 307, an application processor 308, and a resource and power management (RPM) processor 317. The processing device SOC 300 may also include one or more auxiliary processors 310 (e.g., vector auxiliary processors) connected to one or more of the heterogeneous processors 303, 304, 306, 307, 308, and 317. Each processor may include one or more cores and an independent / internal clock. Each processor / core may operate independently of other processors / cores. For example, the processing device SOC 300 may include a processor executing a first type of operating system (e.g., FreeBSD, LINUX, OS X, etc.) and a processor executing a second type of operating system (e.g., Microsoft Windows). In some embodiments, the application processor 308 may be the main processor, central processing unit (CPU), microprocessor unit (MPU), arithmetic logic unit (ALU), etc. of the SOC 300. The graphics processor 306 may be a graphics processing unit (GPU).

[0078] The processing device SOC 300 may include analog circuitry and custom circuitry 314 for managing sensor data, analog-to-digital conversion, wireless data transmission, and for performing other specialized operations, such as processing encoded audio and video signals for rendering in a web browser. The processing device SOC 300 may further include system components and resources 316, such as voltage regulators, oscillators, phase-locked loops, peripheral bridges, data controllers, memory controllers, system controllers, access ports, timers, and other similar components for supporting processors and software clients (e.g., web browsers) running on a computing device.

[0079] The processing device SOC 300 also includes dedicated circuitry for camera drive and management (CAM) 305, which includes, provides, controls, and / or manages the operation of one or more cameras 158, 160 (e.g., a main camera, a network camera, a 3D camera, etc.), video display data from the camera firmware, image processing, video preprocessing, video front-end (VFE), online JPEG, high-definition video encoding and decoding, etc. CAM 305 may be a separate processing unit and / or include a separate or internal clock.

[0080] In some embodiments, the image and object recognition processor 306 may be configured with processor-executable instructions and / or dedicated hardware configured to perform the image processing and object recognition analysis involved in various embodiments. For example, the image and object recognition processor 306 may be configured to perform operations processing images received from cameras (e.g., 158, 160) via CAM 305 to identify and / or identify other vehicles, and otherwise perform the functions of the camera perception layer 204 as described. In some embodiments, the processor 306 may be configured to process radar or lidar data and perform the functions of the radar perception layer 202 as described.

[0081] System components and resources 316, analog and custom circuitry 314, and / or CAM 305 may include circuitry for interfacing with peripheral devices such as cameras 158, 160, radar 168, laser radar 170, electronic displays, wireless communication devices, external memory chips, etc. Processors 303, 304, 306, 307, and 308 may interconnect with one or more memory components 312, system components and resources 316, analog and custom circuitry 314, CAM 305, and RPM processor 317 via interconnect / bus modules 324. These interconnect / bus modules may include reconfigurable gate arrays and / or may implement bus architectures (e.g., CoreConnect, AMBA, etc.). Communication may be provided by advanced interconnects, such as high-performance on-chip networks (NoC).

[0082] The processing device SOC 300 may further include input / output modules (not shown) for communicating with resources external to the SOC, such as clock 318 and voltage regulator 320. The external resources (e.g., clock 318, voltage regulator 320) may be shared by two or more internal SOC processors / cores (e.g., DSP 303, modem processor 304, graphics processor 306, application processor 308, etc.).

[0083] In some embodiments, the processing device SOC 300 may be included in a control unit (e.g., 140) for use in a vehicle (e.g., 100). The control unit may include a communication link for communication with a telephone network (e.g., 180), the Internet, and / or a network server (e.g., 184) as described.

[0084] The processing device SOC 300 may also include additional hardware and / or software components suitable for collecting sensor data from sensors, including motion sensors (e.g., accelerometers and gyroscopes of an IMU), user interface components (e.g., input buttons, touch screen displays, etc.), microphone arrays, sensors for monitoring physical conditions (e.g., location, orientation, motion, direction, vibration, pressure, etc.), cameras, compasses, GPS receivers, communication circuitry (e.g., Bluetooth®, WLAN, WiFi, etc.), and other well-known components of modern electronic devices.

[0085] To minimize possible dimensionality values ​​for calculating potential location overlap misbehavior conditions, the V2X system participant processor can assign threshold vehicle model data to the second vehicle. However, if the dimensionality values ​​do not accurately represent the second vehicle, the detection of location overlap misbehavior conditions may be a false positive. Figure 4A illustrates several instance scenarios that may occur when an accurate threshold vehicle model is selected and an inaccurate threshold vehicle model is selected, and conclusions are drawn from such selections. In each scenario shown, the first vehicle (e.g., the reporting vehicle) is shown at the "top" position, and the second vehicle (e.g., the neighboring vehicle) is shown at the "bottom" position.

[0086] Referring to Figures 1A to 4A, in each of scenarios 350-357, the first vehicle may use hard-coded preset values ​​for the length and width of the first vehicle. In other embodiments, the first vehicle may be assigned the length and width based on a selected threshold vehicle model. In the example shown in scenario 350, the second vehicle is a "large" vehicle, which is assigned a "large" threshold vehicle model, and the reported positions of the first and second vehicles (at least one of which is inaccurate) result in positional overlap. Therefore, in scenario 350, an inappropriate positional overlap has occurred (unless the second vehicle is actually partially inside the trunk of the first vehicle, such as after a collision). The first and second vehicles are determined to be positioned too close to each other, and the first and second vehicles are assigned accurate dimensions from the selected threshold vehicle model set. Therefore, the detected inappropriate overlap can be a positive inappropriate behavior event with high confidence, where the dimensional boundaries of the two vehicles overlap. This scenario can be considered a true positive event. In other words, it is a confirmed event because the first and second vehicles have been detected as overlapping in position, and it is truly confirmed because there is indeed a positional overlap in the "real world / ground reality".

[0087] In the example shown in scenario 351, the second vehicle is a "small" vehicle, which is inaccurately assigned a "large" threshold vehicle model. Therefore, the second vehicle is assigned an inaccurate dimension from the selected set of threshold vehicle models. Thus, in scenario 351, a false positive location overlap misbehavior may have occurred. Although the first and second vehicles are properly positioned to avoid location overlap, the second vehicle is assigned an inaccurate dimension from the "large" threshold vehicle model. Therefore, a location overlap misbehavior has been detected, but the detected location overlap misbehavior is a false positive event. In other words, it is a positive event because the first and second vehicles have been detected as overlapping in location. However, it is a false positive event because in the "real world / ground reality," there is no location overlap.

[0088] In the example shown in scenario 352, the second vehicle is a "small" vehicle, which is accurately assigned a "small" threshold vehicle model. Therefore, the second vehicle is assigned an accurate dimension from the selected set of threshold vehicle models. Therefore, no positional overlap is detected in scenario 352. In scenario 352, the first and second vehicles are properly positioned to avoid positional overlap, and the second vehicle is assigned an accurate dimension from the "small" threshold vehicle model. Therefore, no positional overlap violation is detected, which is a true negative event. In other words, it is a negative event because the first and second vehicles have been detected as not overlapping in position, and it is a true negative event because there is indeed no positional overlap in the "real world / ground reality".

[0089] In the example shown in scenario 353, the second vehicle is a "large" vehicle, which is inaccurately assigned a "small" threshold vehicle model. Therefore, the second vehicle is assigned an inaccurate dimension from the selected threshold vehicle model set. Thus, in scenario 353, a false negative positional overlap misbehavior may have occurred. In scenario 353, the first and second vehicles are inaccurately positioned such that if the true dimension were applied to both vehicles, positional overlap would be detected. However, because the second vehicle is assigned an inaccurate dimension from the "small" threshold vehicle model, the positional overlap misbehavior is not detected, which is a false negative event. In other words, it is a negative event because the first and second vehicles have been detected as not overlapping in position. However, it is a false negative because positional overlap does exist in the "real world / ground reality."

[0090] In the example shown in scenario 354, the second vehicle is a "small" vehicle that has been inaccurately assigned a "large" threshold vehicle model. Therefore, the second vehicle is assigned an inaccurate dimension from the selected threshold vehicle model set. However, despite the incorrect boundary dimension of the second vehicle, the vehicles are positioned far enough away that there is no positional overlap. Therefore, in scenario 354, a true negative positional overlap condition has occurred. In scenario 354, the first and second vehicles are properly positioned to avoid positional overlap, even though the second vehicle is assigned an inaccurate dimension from the "large" threshold vehicle model. Although the inaccurate dimension is assigned to the "small" vehicle, no positional overlap misbehavior is detected, and the failure to detect a positional overlap condition is a true negative event. In other words, it is a negative event because the first and second vehicles have been detected as not overlapping in position, and it is a true negative event because there is indeed no positional overlap in the "real world / ground reality."

[0091] In the example shown in scenario 355, the second vehicle is a "small" vehicle, which is accurately assigned a "small" threshold vehicle model, and the second vehicle is assigned an accurate dimension from the selected threshold vehicle model set. Therefore, in scenario 355, a true negative positional overlap condition has occurred. In scenario 355, the first and second vehicles are properly positioned to avoid positional overlap, and the second vehicle is assigned an accurate dimension from the "small" threshold vehicle model. Therefore, no positional overlap misbehavior is detected, and the absence of a positional overlap condition is a true negative event. In other words, it is a negative event because the first and second vehicles have been detected as not overlapping in position, and it is a true negative event because there is indeed no positional overlap in the "real world / ground reality".

[0092] In the example shown in scenario 356, the second vehicle is a "small" vehicle, which is accurately assigned a "small" threshold vehicle model. Therefore, the second vehicle is assigned an accurate dimension from the selected set of threshold vehicle models. Therefore, in scenario 356, a true certainty of positional overlap may have occurred. In scenario 356, the first and second vehicles are improperly positioned relative to each other, resulting in positional overlap, and the second vehicle is assigned an accurate dimension from the "small" threshold vehicle model. Therefore, the improper positional overlap has been detected, and the detection of the positional overlap is a true certainty event. In other words, it is a certainty event because the first and second vehicles have been detected as positionally overlapping, and it is true certainty because positional overlap does indeed exist in the "real world / ground reality".

[0093] In the example shown in scenario 357, the second vehicle is a "small" vehicle, which is inaccurately assigned a "large" threshold vehicle model. However, despite being assigned an inaccurate and large dimension, the second vehicle is positioned too close to the first vehicle, resulting in an inappropriate positional overlap. Therefore, in scenario 357, a true positional overlap may have occurred. In scenario 357, the first and second vehicles are inappropriately positioned relative to each other, resulting in positional overlap, and the second vehicle is assigned an inaccurate dimension from the "large" threshold vehicle model. Despite the inaccurate dimension assignment, the inappropriate positional overlap has been detected, and the detection of the positional overlap is a true event. In other words, it is a certain event because the first and second vehicles have been detected as overlapping in position, and it is true because there is indeed positional overlap in the "real world / ground reality".

[0094] Figure 4B illustrates an additional instance of anomaly detection for inaccurate location overlap behavior that may occur when inaccurate dimensions are assigned to other V2X system participants (such as pedestrians). Referring to Figures 1A through 4B, in the instance shown in scenario 358, similar to scenario 354, the second vehicle is a "large" vehicle, which is precisely assigned a "large" threshold vehicle model. Therefore, the second vehicle is assigned an accurate dimension from the selected set of threshold vehicle models. In scenario 354, the first and second vehicles are properly positioned to avoid location overlap, and the second vehicle is assigned an accurate dimension from the "large" threshold vehicle model. Therefore, in scenario 358, a true negative location overlap situation may have occurred.

[0095] In scenarios 359 and 360, another V2X system participant can be a pedestrian carrying a mobile device capable of communicating via the V2X system. As a pedestrian, the entity can have a shorter length and narrower width than the actual dimension of the smallest car in the current geographic area. Nevertheless, the pedestrian (i.e., the second vehicle) can be considered a "small" vehicle, which is inaccurately assigned a "large" threshold vehicle model. Therefore, the second vehicle is assigned an inaccurate dimension from the selected set of threshold vehicle models. Therefore, in scenarios 359 and 360, a false positive location overlap condition can be detected. In scenarios 359 and 360, the first vehicle and the "second vehicle" are properly positioned to avoid location overlap. However, because the "second vehicle" is assigned an inaccurate dimension from the "large" threshold vehicle model, the V2X system participant processor will detect that an improper location overlap condition has occurred. Therefore, although the improper location overlap condition has been detected, the detection of the location overlap condition is a false positive event.

[0096] In scenario 361, where the vehicle is a pedestrian carrying a mobile device capable of communicating via the V2X system, another V2X system participant can be a pedestrian. As a pedestrian, the entity can have a shorter length and narrower width than the actual dimension of the smallest car in the current geographic area. In some cases, the pedestrian (i.e., the second vehicle) can be considered a "small vehicle" that is accurately assigned a "small" threshold vehicle model. Therefore, the second vehicle is assigned an accurate dimension from the selected set of threshold vehicle models. Thus, in scenario 361, a true negative location overlap condition can be detected. In scenario 361, the first vehicle and the "second vehicle" are properly positioned to avoid location overlap. Furthermore, since the "second vehicle" is assigned an accurate dimension from the "small" threshold vehicle model, the V2X system participant processor will not detect that an improper location overlap condition has occurred. Since no improper location overlap condition is detected, the absence of a detected location overlap condition is a true negative event.

[0097] The various scenario illustrations shown in Figures 4A and 4B accurately represent the dimensional requirements of vehicles in a V2X system. While a solution to accurately represent the dimensional requirements of vehicles in a V2X system could be to store the dimensional set for each individual vehicle model and variant known to the V2X system, this solution may not be efficient or permissible in resource-constrained V2X systems. Even in scenarios where "large" vehicles are inaccurately assigned dimensions from the threshold model of "small" vehicles, false negative location overlap events may occur, as illustrated in Figures 4A and 4B. False positive location overlap events may occur in scenarios where "small" vehicles are inaccurately assigned dimensions from the threshold model of "large" vehicles. The occurrence of these false positive and false negative events can lead to distrust of the V2X system and defeat the purpose of Intelligent Transportation Systems (ITS).

[0098] Figure 5 illustrates a more accurate vehicle threshold model for use with the various embodiments disclosed herein. The vehicle threshold model shown in Figure 5 can improve the accuracy of detecting inappropriate position overlap behavior. Additionally, the vehicle threshold model shown in Figure 5 can provide information to the V2X system to calculate the confidence level for detecting inappropriate position overlap behavior. Referring to Figures 1A through 5, a first vehicle (e.g., a reporting vehicle) is illustrated. Point 370 represents the reported position of a second vehicle having a "parallel" orientation relative to the first vehicle. This position and orientation (i.e., facing) data can be reported by the second vehicle in a V2X message based on sensor data contained in the second vehicle. Furthermore, this position and orientation data can be confirmed by sensor data of the first vehicle (such as radar and camera sensor data equipped on the first vehicle). For the purpose of performing position overlap checks, the first vehicle can assign dimensions to the second vehicle by selecting an appropriate vehicle threshold model.

[0099] Figure 5 illustrates a set of vehicle threshold models containing three different vehicle threshold models. The minimum vehicle threshold model 371 can assign the shortest length and narrowest width dimensions (i.e., the minimum dimension) to the second vehicle. The maximum vehicle threshold model 373 can assign the second vehicle length dimensions (i.e., the maximum vehicle length) and width dimensions (i.e., the maximum vehicle width) that are greater than the actual longest vehicle length (i.e., the maximum vehicle length) and greater than the actual widest vehicle width (i.e., the maximum vehicle width) (i.e., collectively referred to as the maximum dimension). Conversely, in other embodiments, the minimum vehicle threshold model 371 can assign the second vehicle length dimensions (i.e., the minimum vehicle length) and width dimensions (i.e., the minimum vehicle width) that are less than the actual shortest vehicle length (i.e., the minimum vehicle length) and narrowest vehicle width (i.e., the minimum vehicle width) (i.e., collectively referred to as the minimum dimension). The maximum vehicle threshold model 373 can assign the second vehicle length dimensions (i.e., the maximum vehicle length) and width dimensions (i.e., the maximum vehicle width) that are actually the longest vehicle length (i.e., the maximum vehicle length) and widest vehicle width (i.e., the maximum vehicle width) (i.e., collectively referred to as the maximum dimension). In doing so, the system can provide some "buffering" for dimensions even for the largest vehicle in the current geographic region. The vehicle threshold model 372 can assign a length shorter than the maximum length but longer than the minimum length. Additionally, the vehicle threshold model 372 can assign a width narrower than the maximum width but wider than the minimum width.

[0100] Further, each of the minimum vehicle threshold model 371, the maximum vehicle threshold model 373, and the vehicle threshold model 372 may include a "confidence value" associated with a dimension of each of the minimum vehicle threshold model 371, the maximum vehicle threshold model 373, and the vehicle threshold model 372. The confidence values ​​associated with the various assigned dimensions may be based on the vehicle distribution in the current geographic region. In some embodiments, the confidence value may be expressed as the total percentage of all vehicles minus the percentage of vehicles in the selected vehicle threshold model that are greater than the assigned dimension. In some embodiments, the confidence value may be expressed as the total percentage of all vehicles minus the percentage of vehicles in the selected vehicle threshold model that are less than the assigned dimension. For example, the confidence value may be between 0 and 1 (or 0% to 100%).

[0101] For example, in a geographic region (e.g., country, state, county, city, town, etc.) where there is a single vehicle model, more than one vehicle threshold model is not required in the vehicle threshold model set. A single vehicle threshold model can be implemented where all known possible vehicles share the same dimension assigned by the vehicle threshold model. Since the distribution of vehicles in the current geographic region is all of the same model, the confidence value of the maximum vehicle threshold model 373 can be 1 (i.e., the total percentage 1 (100%) of all vehicles in the current geographic region minus the percentage 0 of vehicles in the selected vehicle threshold model that are greater than the assigned dimension). The confidence value of the minimum vehicle threshold model 371 can be zero (0) or 0% (i.e., the total percentage 1 (100%) of all vehicles in the current geographic region minus the percentage 1 (100%) of vehicles in the selected vehicle threshold model that are greater than the assigned dimension, so 1-1=0).

[0102] The calculated confidence level for detecting improper location overlap behavior can be expressed as the percentage of vehicles smaller than the maximum dimension minus the confidence value of the selected vehicle threshold. In various embodiments, the calculated confidence level can be between zero (0) and one (1) (i.e., 0%-100%). Therefore, the confidence level for detected improper location overlap behavior calculated using the maximum vehicle threshold model 373 can be zero (0) or 0% (i.e., 1-1=0), because any improper location overlap behavior using a dimension larger than that of a single vehicle model will result in a false positive event. As noted above, this false positive may undermine the trust in the V2X system. However, the confidence level for detected improper location overlap behavior calculated using the minimum vehicle threshold model 371 can be 1 or 100% (i.e., 1-0=1), because any improper location overlap behavior using the minimum dimension (i.e., the dimension of a single vehicle model) will result in a true positive event. Since there is a single car model in the current geographic area, a positive detection using the minimum dimensional location overlap (assuming GPS sensor and orientation sensor data are accurate) cannot be wrong.

[0103] In another example, suppose there are more than one model vehicle (e.g., 2 models) in the current geographic area. In one example, the distribution of all cars in the current geographic area can be represented by a graph named "Distribution 1". In Distribution 1, half (50%) of all vehicles in the current geographic area are "large" vehicle models, and half (50%) of all vehicles in the current geographic area are "small" vehicle models. In this example, the vehicle threshold model set may include a maximum vehicle threshold model 373, which is assigned a dimension greater than that of the "large" vehicle models, and a minimum vehicle threshold model 371, which is assigned a dimension equal to that of the "small" vehicle models. Therefore, in this embodiment, the confidence value of the maximum vehicle threshold model 373 may be one (1) or 100% (i.e., 1-0=1), and the confidence value of the minimum vehicle threshold model 371 may be 0.5 or 50% (i.e., 1-0.5=0.5).

[0104] Therefore, in this example, the confidence level for detected misaligned location behavior calculated using the maximum vehicle threshold model 373 can be zero (0) or 0% (i.e., 1-1=0), because any misaligned location behavior using a dimension larger than that of a single vehicle model will result in a false positive event. As noted above, this false positive may undermine the trust in the V2X system. However, the confidence level for detected misaligned location behavior calculated using the minimum vehicle threshold model 371 can be 0.5 (50%) (i.e., 1-0.5=0.5), because for 50% of vehicles with a large model having a dimension larger than that assigned in the minimum vehicle threshold model 371, any misaligned location behavior using the minimum dimension (i.e., the dimension of a single vehicle model) will result in a true positive event.

[0105] Therefore, in this example, the confidence level for detected misaligned location behavior calculated using the maximum vehicle threshold model 373 can be zero (0) or 0% (i.e., 1-1=0), because any misaligned location behavior using a dimension larger than that of a single vehicle model would result in a false positive event. As noted above, this false positive could undermine the trust in the V2X system. However, the confidence level for detected misaligned location behavior calculated using the minimum vehicle threshold model 371 can be 0.5 (50%) (i.e., 1-0.5=0.5), because for 50% of vehicles that are large models with a dimension larger than that assigned in the minimum vehicle threshold model 371, any misaligned location behavior using the minimum dimension (i.e., the dimension of a single vehicle model) would result in a true positive event.

[0106] In some embodiments, vehicle threshold model 372 may also be included in the vehicle threshold model set. Vehicle threshold model 372 may be assigned a dimension smaller than that of the maximum vehicle threshold model 373, but larger than that of the minimum vehicle threshold model 371. In the vehicle distribution in the current geographic area, such as the distribution represented by distribution 1, the confidence value assigned to vehicle threshold model 372 may be 0.5. Since 50% (i.e., 0.5) of all vehicles in the current geographic area may be greater than the dimension assigned in vehicle threshold model 372. Similar to the minimum vehicle threshold model 371, the confidence level for the detected positional overlap misbehavior calculated using the vehicle threshold model 372 can also be 0.5 (50%) (i.e., 1-0.5=0.5), because for 50% of vehicles that are large models with dimensions larger than those assigned in the vehicle threshold model 372, any positional overlap misbehavior using the dimensions assigned in the vehicle threshold model 372 (i.e., smaller than the dimensions of the maximum vehicle threshold model 373, but larger than the dimensions of the minimum vehicle threshold model 371) will result in a true positive event.

[0107] Referring to “Distribution 2”, in the current geographic region, the percentage of “large” vehicle models in the total number of vehicles is 80%, while the percentage of “small” vehicle models in the total number of vehicles is 20%. In this example, the vehicle threshold model set may include a maximum vehicle threshold model 373, which is assigned a dimension greater than that of the “large” vehicle models, and a minimum vehicle threshold model 371, which is assigned a dimension equal to that of the “small” vehicle models. In addition, the confidence level of the maximum vehicle threshold model 373 may be one (1) or 100% (i.e., 1-0=1), and the confidence level of the minimum vehicle threshold model 371 may be 0.2 or 20% (i.e., 1-0.8=0.2).

[0108] Therefore, in this example, the confidence level for detected misaligned location behavior calculated using the maximum vehicle threshold model 373 can be zero (0%) (e.g., 1-1=0), because any misaligned location behavior using a dimension larger than that of a single vehicle model would result in a false positive event. As noted above, this false positive could undermine the trust in the V2X system. However, the confidence level for detected misaligned location behavior calculated using the minimum vehicle threshold model 371 can be 0.8 (80%) (i.e., 1-0.2=0.8), because for 80% of vehicles that are large models with a dimension larger than that assigned in the minimum vehicle threshold model 371, any misaligned location behavior using the minimum dimension (i.e., the dimension of a single vehicle model) would result in a true positive event.

[0109] As noted above, in some embodiments, vehicle threshold model 372 may also be included in the vehicle threshold model set. Vehicle threshold model 372 may be assigned a dimension smaller than that of the maximum vehicle threshold model 373, but larger than that of the minimum vehicle threshold model 371. In the vehicle distribution within the current geographic region, such as the distribution represented by distribution 1, the confidence value assigned to vehicle threshold model 372 could be 0.2. This is because 80% of all vehicles in the current geographic region (i.e., 0.8) may be greater than the dimension assigned to vehicle threshold model 372. Similar to the minimum vehicle threshold model 371, the confidence level for the detected positional overlap misbehavior calculated using vehicle threshold model 372 can also be 0.8 (80%) (i.e., 1-0.2=0.8), because for 80% of vehicles that are large models with dimensions larger than those assigned in vehicle threshold model 372, any positional overlap misbehavior using dimensions assigned in vehicle threshold model 372 (i.e., smaller than the dimensions of the maximum vehicle threshold model 373, but larger than the dimensions of the minimum vehicle threshold model 371) will result in a true positive event.

[0110] Although vehicle threshold model 372 may not be significantly different from the confidence values ​​and calculated confidence levels that can be obtained using the minimum vehicle threshold model 371 in a distribution with only two models, additional vehicle threshold models (e.g., vehicle threshold model 372) can provide confidence values ​​and calculated confidence levels different from the minimum vehicle threshold model 371 as the number of models with different dimensions increases. Therefore, an increase in the subtlety and accuracy of the overall V2X system can be provided.

[0111] In some embodiments, false negative events can be avoided by assigning a minimum dimension value smaller than the actual “large” and “small” vehicle dimensions. Thus, in such embodiments, the minimum vehicle threshold model 371 can assign a vehicle a length and width smaller than the actual vehicle dimension of a “small” vehicle. The maximum vehicle threshold model 373 can assign a dimension smaller than or equal to the “large” vehicle dimension. In this embodiment, the confidence value assigned to the vehicle threshold model can be the total percentage of vehicles in the current geographic area (i.e., -(1) or 100%) minus the percentage of vehicles in the selected vehicle threshold model that are smaller than the assigned dimension. Thus, in the above example for distribution 1, the confidence value for the minimum vehicle threshold model 371 can be -(1) or 100% (i.e., 1-0=1), while the confidence value for the maximum vehicle threshold model 373 can be 0.5 or 50% (i.e., 1-0.5=0.5). As discussed in the embodiments above, the calculated confidence level for detecting improper location overlap can be expressed as the percentage of vehicles with a smaller dimension minus the confidence value of the selected vehicle threshold. Therefore, in this embodiment, the confidence level for detected improper location overlap calculated using the minimum vehicle threshold model 371 can be zero (0) or 0% (i.e., 1-1=0), because any improper location overlap condition using a dimension smaller than that of a single vehicle model would result in a false negative event. As noted above, this false negative can undermine trust in the V2X system. However, the confidence level for detected improper location overlap calculated using the maximum vehicle threshold model 373 can be 0.5 or 50% (i.e., 1-0.5=0.5), because for 50% of vehicles with a smaller dimension than that assigned in the maximum vehicle threshold model 373, any improper location overlap condition using the maximum dimension (i.e., the dimension of a single vehicle model) would result in a true positive event.

[0112] FIG. 6A is a process flowchart illustrating the operation of a method 400 for effectively performing location overlap misbehavior detection consistent with the various embodiments disclosed herein. Referring to FIGS. 1 through 6A, the operation of method 400 may be performed by a processor of a V2X system participant (e.g., any of vehicles 12, 14, 16 in FIG. 1D, but for ease of discussion, vehicle 16 may be referred to as the first vehicle (also referred to as the reporting vehicle), and vehicle 12 may be referred to as the second vehicle (also referred to as the neighboring vehicle)). In block 402, the V2X system participant processor in the first vehicle (hereinafter referred to as the first vehicle) may determine the position and orientation of the first vehicle. The position and orientation of the first vehicle may be obtained and determined from a plurality of sensors of the first vehicle in relation to the manipulation, navigation, and / or other operations of the V2X system participant (e.g., vehicle 16). The sensors of the first vehicle may include any of the various sensors discussed above with respect to FIGS. 1A and 1B.

[0113] In block 404, the first vehicle dimension boundary can be determined based on the first vehicle position, the first vehicle orientation, the first vehicle length, and the first vehicle width. In some embodiments, the first vehicle dimension boundary can be determined using hard-coded or preset first vehicle length and width values, which are values ​​trusted by the first vehicle. In other embodiments, the first vehicle dimension boundary can be determined by selecting a vehicle threshold model from a set of vehicle threshold models and using the length and width dimensions of the first vehicle assigned therein.

[0114] In block 406, the first vehicle can receive V2X messages from the second vehicle. The V2X messages received by the first vehicle from the second vehicle (e.g., vehicle 12) may include sensor data providing the position and orientation of the second vehicle.

[0115] In block 408, the first vehicle can retrieve a vehicle threshold model set. In some embodiments, the vehicle threshold model set can be retrieved from a local storage device. In other embodiments, the vehicle threshold model set can be retrieved from a remote storage device. The vehicle threshold model set can be selected and retrieved based on the current geographic area where the first vehicle is located, as described in more detail below with respect to method 450 shown in FIG. 6B.

[0116] In block 410, the first vehicle can select a vehicle threshold model (e.g., vehicle threshold model 372) representing the second vehicle. By selecting a specific vehicle threshold model from the set of vehicle threshold models, the second vehicle can be assigned the length and width of the selected vehicle threshold model. Additionally, confidence values ​​contained in the selected vehicle threshold model can also be retrieved.

[0117] In block 412, the second vehicle dimension boundary can be determined based on the second vehicle position and second vehicle orientation data received in the V2X message, as well as the length and width dimensions that can be assigned to the second vehicle via a selected vehicle threshold model.

[0118] In decision block 414, the first vehicle can determine whether any portion of the first vehicle dimension boundary overlaps with any portion of the second vehicle dimension boundary. In response to the first vehicle determining that no portion of the first vehicle dimension boundary overlaps with any portion of the second vehicle dimension boundary (i.e., decision 414 = no), the first vehicle can return to the sensors monitoring the first vehicle to determine the position and orientation of the first vehicle in block 402. In response to the first vehicle determining that a portion of the first vehicle dimension boundary overlaps with any portion of the second vehicle dimension boundary (i.e., decision 414 = yes), in block 416, the first vehicle can identify that an improper position overlap condition has occurred.

[0119] In some embodiments, in block 418, the first vehicle can calculate a confidence level for identifying an improper location overlap behavior condition. As discussed above with reference to FIG5, the calculation of the confidence level for identifying an improper location overlap behavior condition can be based on a selected vehicle threshold model confidence value, such as (the percentage of vehicles greater than the minimum dimension or the percentage of vehicles less than the maximum dimension) minus (the selected vehicle threshold model confidence value).

[0120] In some embodiments, in decision block 420, the first vehicle may determine whether the confidence level for identifying the calculated position overlap misbehavior exceeds a confidence threshold. In some embodiments, the V2X system may attempt to limit the number of generated MBRs to cases where the confidence level for the calculated identified misbehavior is high. In this way, the V2X system can limit the generation and transmission of MBRs to cases where there is a higher probability that the misbehavior has occurred. In response to the determination that the calculated confidence level does not exceed the confidence threshold (i.e., decision block 420 = No), the first vehicle may return to the sensors monitoring the first vehicle to determine the position and orientation of the first vehicle in block 402. In response to the determination that the calculated confidence level exceeds the confidence threshold (i.e., decision block 420 = Yes), the first vehicle may generate an MBR that identifies the position overlap misbehavior and includes selected vehicle threshold model data used to conclude that the position overlap misbehavior has occurred, block 422.

[0121] In block 424, the first vehicle can send the generated MBR to the misconduct management agency 74 for further analysis and confirmation that a position overlap misconduct condition has occurred. Additionally, the misconduct management agency 74 (sometimes combined with sensor OEM servers 70, 72) determines that the position overlap misconduct condition may have been identified due to erroneous sensor data. In this embodiment, the misconduct management agency 74 (sometimes combined with sensor OEM servers 70, 72) can provide the first vehicle with instructions to remedy the sensors (e.g., replacement, repair, new software update, etc.).

[0122] In some embodiments, the calculation of the confidence level (block 418) and the determination of whether the calculated confidence level exceeds a threshold (decision block 420) can occur after the first vehicle generates the MBR (block 422) but before the operation of sending the MBR to the misconduct management agency 74 (block 424). In this way, the calculated confidence level can be used to save transmission resources (e.g., bandwidth, transmission power). In this embodiment, the MBR generated in block 422 can be stored locally until the first vehicle is able to provide the stored MBR to the misconduct management agency 74, such as via a wired download connection.

[0123] Figure 6B is a process flowchart illustrating the operation of a method 450 for effectively performing location overlap misbehavior detection consistent with the various embodiments disclosed herein. Different geographic regions may have different vehicle size distributions. For example, the size of vehicle models may vary by country. Furthermore, trucks and SUVs may be more widely distributed in rural geographic regions compared to urban geographic regions, while in urban geographic regions, traffic and parking issues may limit vehicle size, and smaller vehicles may be more widely distributed. In fact, the distribution of vehicle sizes may vary by state, county, city, etc. Therefore, in order to accurately determine dimensional boundaries from the vehicle threshold model set, an appropriate vehicle threshold model set should be retrieved and / or selected based on the current geographic region where the first vehicle is currently located. Thus, the first vehicle may periodically check its current location to determine whether the first vehicle has changed its geographic region. If the geographic region has changed, the first vehicle may retrieve a vehicle threshold model set that accurately reflects the vehicle distribution for the current geographic region. As discussed above, the vehicle threshold model set may contain vehicle dimensions and confidence values ​​for each vehicle threshold model. Therefore, in some embodiments, the method 450 shown in FIG6B can be executed before block 408 in FIG6A in order to determine which current geographic region the vehicle threshold model set can be retrieved for.

[0124] Referring to Figures 1A to 6B, in block 401, the V2X system participant processor (i.e., for reference, the first vehicle) can store a preset geographical area in memory (local or remote).

[0125] As discussed above, in block 402, the first vehicle can determine its position and orientation. The position and orientation of the first vehicle can be obtained and determined from a plurality of sensors of the first vehicle in relation to the manipulation, navigation, and / or other operations of a participant in the V2X system (e.g., vehicle 16). For example, the GPS sensor of the first vehicle can provide global positioning.

[0126] In block 403, the first vehicle can compare the determined location with the stored geographic area map and geographic area boundary to determine the current geographic area (e.g., region, country, state, province, county, city, town, etc.) based on the determined location of the first vehicle.

[0127] In block 405, the determined geographical area where the first vehicle is located can be set as the current geographical area.

[0128] In decision block 407, the first vehicle can determine whether the set current geographic region is different from the geographic region stored in memory (e.g., preset memory). In response to the determination that the current geographic region is not different from the geographic region stored in memory (i.e., decision block 407 = No), the first vehicle can loop back to block 402 and continue to determine the position and orientation of the first vehicle. It may not be necessary to change the vehicle threshold model set. In response to the determination that the current geographic region is different from the geographic region stored in memory (i.e., decision block 407 = Yes), in block 409, the first vehicle can store the set current geographic region in memory to replace the previously stored geographic region (e.g., preset geographic region), and then proceed to step 408 described with reference to FIG. 6A to retrieve a vehicle threshold model set suitable for the current geographic region where the first vehicle is located. In this way, the first vehicle can assign accurate dimensions to the second vehicle based on the distribution of vehicle models in the current geographic region. Additionally, the vehicle threshold model can include more accurate confidence values ​​for the assigned dimensions.

[0129] Various embodiments (including, but not limited to, those described above with reference to Figures 1A to 6B) can be implemented in a wide variety of computing systems, including in-vehicle devices and mobile computing devices, examples of which are suitable for use with the various embodiments shown in Figure 7. Referring to Figures 1A to 7, the mobile computing device 700 may include a processor 702 coupled to a touchscreen controller 704 and internal memory 706. The processor 702 may be one or more multi-core integrated circuits designed for general-purpose or special-purpose processing tasks. The internal memory 706 may be volatile or non-volatile memory, and may also be secure and / or encrypted memory, or insecure and / or unencrypted memory, or any combination thereof. Examples of memory types that can be utilized include, but are not limited to, DDR, LPDDR, GDDR, Wide 100, RAM, SRAM, DRAM, P-RAM, R-RAM, M-RAM, STT-RAM, and embedded DRAM. The touchscreen controller 704 and processor 702 can also be coupled to the touchscreen panel 712, such as a resistive touchscreen, a capacitive touchscreen, or an infrared touchscreen. Furthermore, the display of the mobile computing device 700 does not need to have touchscreen capability.

[0130] The mobile computing device 700 may have one or more radio transceivers 708 (e.g., Peanut, Bluetooth, ZigBee, Wi-Fi, RF radio) and antennas 710, which are used for transmitting and receiving communications and are coupled to each other and / or coupled to the processor 702. The transceivers 708 and antennas 710 may be used with the circuitry mentioned above to implement various wireless transmission protocol stacks and interfaces. The mobile computing device 700 may include a cellular wireless modem chip 716 that enables communication via a cellular network and is coupled to the processor.

[0131] The mobile computing device 700 may include a peripheral device connection interface 718 coupled to the processor 702. The peripheral device connection interface 718 may be configured to accept only one type of connection, or it may be configured to accept various types of physical and communication connections, common or proprietary, such as Universal Serial Bus (USB), FireWire, Thunderbolt, or PCIe. The peripheral device connection interface 718 may also be coupled to a peripheral device port (not shown) configured similarly.

[0132] The mobile computing device 700 may also include a speaker 714 for providing audio output. The mobile computing device 700 may also include a housing 720 made of plastic, metal, or a combination of materials to house all or some of the components described herein. Those skilled in the art will recognize that the housing 720 may be a vehicle dashboard console in an in-vehicle embodiment. The mobile computing device 700 may include a power source 722 coupled to the processor 702, such as a disposable or rechargeable battery. The rechargeable battery may also be coupled to a peripheral device port to receive charging current from a source external to the mobile computing device 700. The mobile computing device 700 may also include a physical button 724 for receiving user input. The mobile computing device 700 may also include a power button 726 for turning the mobile computing device 700 on and off.

[0133] Various embodiments (including, but not limited to, those described above with reference to Figures 1A to 6B) can be implemented in a wide variety of computing systems, including a laptop computer 800 (examples of which are shown in Figure 8). Many laptop computers include a touchpad touch surface 817 that serves as a pointing device for the computer and can therefore receive drag, scroll, and tap gestures, similar to those implemented in the computing devices described above and equipped with a touchscreen display. The laptop computer 800 will typically include a processor 802 coupled to volatile memory 812 and a large-capacity non-volatile memory, such as a disk drive 813 containing flash memory. Furthermore, the computer 800 may have one or more antennas 808 for transmitting and receiving electromagnetic radiation, which may be connected to a wireless data link and / or a cellular telephone transceiver 816 coupled to the processor 802. The computer 800 may also include a floppy disk drive 814 and a compact optical disc (CD) drive 815 coupled to the processor 802. In a laptop configuration, the computer casing includes a touchpad 817, a keyboard 818, and a display 819, all of which are coupled to a processor 802. Other configurations of the computing device may include a computer mouse or trackball coupled to the processor (e.g., via USB input), which is well known and can be used in various embodiments.

[0134] Various embodiments (including, but not limited to, those described above with reference to Figures 1A to 6B) can be implemented by various vehicle computing systems (examples of which are shown in Figure 9). A vehicle computing system 900 typically includes one or more multi-core processor components 901 coupled to volatile memory 902 and large-capacity non-volatile memory (such as a disk drive 904). As shown in Figure 9, the vehicle computing system 900 can be added by inserting the multi-core processor component 901 into a rack of components. The vehicle computing system 900 may also include a communication port 907 coupled to the multi-core processor component 901 for exchanging data and commands with a radio module (not shown), such as a local area network, internet, public switched telephone network, and / or cellular data network (e.g., CDMA, TDMA, GSM, PCS, 3G, 4G, 5G, LTE, or any other type of cellular data network) coupled to other broadcast system computers and servers.

[0135] Several different cellular and mobile communication services and standards are available or envisioned in the future, all of which can be implemented and benefit from various embodiments. Such services and standards include, for example, the 3rd Generation Partnership Project (3GPP), Long Term Evolution (LTE) systems, 3rd generation (3G), 4th generation (4G), 5th generation (5G), Global System for Mobile Communications (GSM), Universal Mobile Telecommunications System (UMTS), 3GSM, Universal Packet Radio Service (GPRS), Code Division Multiple Access (CDMA) systems (e.g., cdmaOne, CDMA1020TM), Enhanced Data Rate Evolution of GSM (EDGE), Advanced Mobile Telephone Systems (AMPS), Digital AMPS (IS-136 / TDMA), Evolutionary Data Optimization (EV-DO), Digital Enhanced Wireless Telecommunications (DECT), Global Interoperability Microwave Access (WiMAX), Wireless Local Area Networks (WLAN), Wi-Fi Protected Access I & II (WPA, WPA2), and Integrated Digital Enhanced Network (iDEN). For example, each of these technologies involves the transmission and reception of voice, data, signal delivery, and / or content messages. It should be understood that any reference to terms and / or technical details relating to individual telecommunications standards or technologies is for illustrative purposes only and is not intended to limit the scope of the request to a particular communication system or technology, unless specifically enumerated in the language of the request.

[0136] The following paragraphs describe implementation examples. Although some of the following implementation examples are described in accordance with example methods, further example implementations may include: the example methods discussed in the following paragraphs are implemented by a V2X system processor, which may be an on-board unit, mobile device unit, mobile computing unit, or fixed roadside unit that includes a processor configured to have processor-executable instructions to perform the operations of the methods of the following implementation examples; the example methods discussed in the following paragraphs are implemented by a V2X system that includes components for performing the functions of the methods of the following implementation examples; and the example methods discussed in the following paragraphs may be implemented as a non-transitory processor-readable storage medium having processor-executable instructions stored thereon, which are configured to cause the V2X system processor to perform the operations of the methods of the following implementation examples.

[0137] Example 1. A method for performing position overlap checking in a V2X system, comprising: determining a first vehicle position and a first vehicle orientation of a first vehicle; determining a first vehicle dimension boundary, wherein the first vehicle dimension boundary is based on a first vehicle length, a first vehicle width, the first vehicle position, and the first vehicle orientation; receiving a V2X message from a second vehicle, wherein the V2X message includes a second vehicle position and a second vehicle orientation; selecting a vehicle threshold model for the second vehicle from a set of vehicle threshold models, wherein the selected vehicle threshold model includes a selected vehicle threshold model length, a selected vehicle threshold model width, and a selected vehicle... The system determines the vehicle threshold model confidence value; determines the second vehicle dimension boundary, wherein the second vehicle dimension boundary is based on the second vehicle location, the second vehicle orientation, the selected vehicle threshold model length, and the selected vehicle threshold model width received in the V2X message; determines whether any part of the first vehicle dimension boundary overlaps with any part of the second vehicle dimension boundary; identifies a location overlap misconduct situation in response to the determination that any part of the first vehicle dimension boundary overlaps with any part of the second vehicle dimension boundary; generates a misconduct report identifying the location overlap misconduct situation; and sends the misconduct report to the misconduct management agency.

[0138] Example 2. According to the method of Example 1, it further includes: in response to determining that the first vehicle has left the first geographic area and entered the current geographic area, selecting the vehicle threshold model from the vehicle threshold model set for the current geographic area, wherein the vehicle threshold model set for the current geographic area is different from the vehicle threshold model set for the first geographic area.

[0139] Example 3. The method according to Example 1 or 2 further includes: in response to identifying the overlapping misconduct condition, calculating a confidence level for the identification that the misconduct condition has occurred, wherein the confidence level for the identification that the misconduct condition has occurred is based on a selected vehicle threshold model confidence value.

[0140] Example 4. According to the method of Example 3, the length and width are assigned to the second vehicle in the vehicle threshold model based on the distribution of vehicle length and vehicle width in the current geographic region.

[0141] Example 5. According to the method of request item 4, it further includes the operation of: assigning the first vehicle length and the first vehicle width according to the values ​​contained in the selected vehicle threshold model.

[0142] Example 6. According to the method of Example 4, wherein the vehicle threshold model set includes: a minimum vehicle threshold model, which includes a minimum dimension and a minimum vehicle confidence value for the minimum vehicle size in the current geographic region, wherein the minimum dimension includes the minimum vehicle length and the minimum vehicle width; and a maximum vehicle threshold model, which includes a maximum dimension and a maximum vehicle confidence value for the maximum vehicle size in the current geographic region, wherein the maximum dimension includes the maximum vehicle length which is greater than the actual longest vehicle length and the maximum vehicle width which is greater than the actual widest vehicle width.

[0143] Example 7. According to the method of Example 6, wherein the selected vehicle threshold model includes the selected threshold vehicle length, the selected threshold vehicle width, and the selected threshold vehicle confidence value of the threshold vehicle in the current geographic region, wherein the selected threshold vehicle confidence value of the threshold vehicle is the percentage of all vehicles in the current geographic region minus the percentage of vehicles in the current geographic region that have an actual length greater than the length of the selected vehicle threshold model and an actual width greater than the width of the selected vehicle threshold model, further wherein the calculated confidence level is equal to the percentage of vehicles smaller than the minimum dimension minus the selected vehicle threshold model confidence value of the threshold vehicle in the current geographic region.

[0144] Example 8. According to the method of Example 4, wherein the vehicle threshold model set includes: a minimum vehicle threshold model, which includes a minimum dimension and a minimum vehicle confidence value for the minimum vehicle size in the current geographic region, wherein the minimum dimension includes a minimum vehicle length less than the actual shortest vehicle length and a minimum vehicle width less than the actual narrowest vehicle width; and a maximum vehicle threshold model, which includes a maximum dimension and a maximum vehicle confidence value for the maximum vehicle size in the current geographic region, wherein the maximum dimension includes the maximum vehicle length and the maximum vehicle width.

[0145] Example 9. According to the method of Example 8, wherein the selected vehicle threshold model includes the selected threshold vehicle length, the selected threshold vehicle width, and the selected threshold vehicle confidence value of the threshold vehicle in the current geographic region, wherein the selected threshold vehicle confidence value of the threshold vehicle is the percentage of all vehicles in the current geographic region minus the percentage of vehicles in the current geographic region that have an actual length smaller than the length of the selected vehicle threshold model and an actual width smaller than the width of the selected vehicle threshold model, further wherein: the calculated confidence level is equal to the percentage of vehicles larger than the maximum dimension minus the selected vehicle threshold model confidence value of the threshold vehicle in the geographic region.

[0146] The various embodiments shown and described are provided merely as examples to illustrate various features of the claimed item. However, the features shown and described with respect to any given embodiment are not necessarily limited to the associated embodiments, and other embodiments shown and described, or combinations thereof, may be used. Furthermore, the claimed item is not intended to be limited to any particular example embodiment.

[0147] The foregoing method description and processing flowcharts are provided merely as illustrative examples and are not intended to require or imply that the operations of the various embodiments must be performed in the presented order. As will be understood by those skilled in the art to which this invention pertains, the order of operations in the foregoing embodiments can be performed in any order. Words such as "afterwards," "following," and "next" are not intended to limit the order of operations; such words are used to guide the reader through the description of the method. Furthermore, any singular claim element mentioned, such as terms using "a," "an," or "the," should not be construed as limiting the element to the singular.

[0148] The various illustrative logic blocks, modules, components, circuits, and algorithmic operations described in conjunction with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and operations have been generally described above in terms of their functionality. Whether this functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art to which this invention pertains can implement the described functionality in varying ways for each specific application; however, such implementation decisions should not be construed as causing a departure from the scope of the claims.

[0149] The hardware used to implement the various illustrative logics, logic blocks, modules, and circuits described in conjunction with the embodiments disclosed herein may be implemented or performed by a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, individual gate or transistor logic, individual hardware components, or any combination thereof designed to perform the functions described herein. The general-purpose processor may be a microprocessor, but alternatively, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of receiver intelligent objects, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors combined with a DSP core, or any other such configuration. Alternatively, some operations or methods may be performed by circuitry specific to a given function.

[0150] In one or more embodiments, the described functionality can be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functionality can be stored as one or more instructions or code on a non-transitory computer-readable storage medium or a non-transitory processor-readable storage medium. The operation of the methods or algorithms disclosed herein can be embodied in a processor-executable software module or processor-executable instructions, which can reside on a non-transitory computer-readable or processor-readable storage medium. A non-transitory computer-readable or processor-readable storage medium can be any storage medium accessible by a computer or processor. By way of example, and not limitation, this non-transitory computer-readable or processor-readable storage medium can include RAM, ROM, EEPROM, flash memory, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage intelligent objects, or any other medium that can be used to store desired program code in the form of instructions or data structures and is accessible by a computer. As used herein, magnetic disks and optical disks include CDs, laser discs, optical discs, DVDs, floppy disks, and Blu-ray discs, where magnetic disks typically reproduce data magnetically, while optical disks reproduce data optically using lasers. Combinations of these are also included within the scope of non-transitory computer-readable and processor-readable media. Furthermore, the operation of methods or algorithms may reside as a set of codes and / or instructions, or any combination of codes and / or instructions, on non-transitory processor-readable and / or computer-readable storage media, and may be incorporated into computer program products.

[0151] The foregoing description of the disclosed embodiments is provided to enable anyone skilled in the art to implement or use the claimed items. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be applied to other embodiments without departing from the scope of the claimed items. Therefore, this application is not intended to be limited to the embodiments shown herein, but rather to be consistent with the widest scope of the following claims and the principles and novel features disclosed herein. [Simplified Explanation of the Diagram]

[0015] The accompanying drawings, which are incorporated herein and constitute a part of this specification, illustrate exemplary embodiments of the requested item and, together with the general description provided above and the detailed description provided below, serve to explain the features of the requested item.

[0016] Figures 1A and 1B are block diagrams illustrating components suitable for implementing various embodiments of a vehicle.

[0017] FIG1C is a component block diagram showing the components of a vehicle suitable for implementing various embodiments.

[0018] Figure 1D is a schematic block diagram illustrating a subset of V2X communication systems suitable for implementing various embodiments.

[0019] Figure 2A is a component block diagram illustrating the components of an example V2X system participant management system according to various embodiments.

[0020] Figure 2B is a component block diagram illustrating components of a V2X system participant management system according to another example of various embodiments.

[0021] Figure 3 is a block diagram illustrating components of an on-chip system for use in a V2X system participant according to various embodiments.

[0022] Figure 4A illustrates several possible scenarios when an accurate threshold vehicle model is selected and an inaccurate threshold vehicle model is selected.

[0023] Figure 4B illustrates an additional instance of location overlap misbehavior detection anomalies that may occur when inaccurate dimensions are assigned to other V2X system participants (such as pedestrians).

[0024] Figure 5 illustrates a more accurate vehicle threshold model for use with the various embodiment methods disclosed herein.

[0025] Figure 6A is a process flowchart illustrating the operation of a method for effectively performing the detection of improper positional overlap behavior consistent with the various embodiments disclosed herein.

[0026] Figure 6B is a process flowchart illustrating the operation of another method for effectively performing the detection of improper positional overlap behavior consistent with the various embodiments disclosed herein.

[0027] Figure 7 is a block diagram of components for use with various embodiments of an example mobile computing device.

[0028] Figure 8 is a block diagram of components for use with various embodiments of an example mobile computing device.

[0029] Figure 9 is a block diagram of components for use with various embodiments of an example server. [Biomaterial Storage]

[0153] Domestic storage information (please note in order of storage institution, date, and number): None. International storage information (please note in order of storage country, institution, date, and number): None.

Claims

1. A method for performing a position overlap check using vehicle threshold model data in a vehicle-to-everything (V2X) system by a first vehicle, comprising the following steps: performing an autonomous or semi-autonomous driving action by the first vehicle; determining a first vehicle position and a first vehicle orientation based on sensor data generated by a sensor of the first vehicle; determining a first vehicle size boundary, wherein the first vehicle size boundary is based on a first vehicle length, a first vehicle width, the first vehicle position, and the first vehicle orientation; receiving a V2X message from a second vehicle, wherein the V2X message includes a second vehicle position and a second vehicle orientation; A vehicle threshold model is selected for the second vehicle from a set of vehicle threshold models for the first vehicle in a current geographic area, wherein the set of vehicle threshold models for the current geographic area includes vehicle threshold models determined based on a group of vehicles with multiple sizes in the current geographic area, and wherein the selected vehicle threshold model includes a selected vehicle threshold model length between a maximum vehicle length and a minimum vehicle length, a selected vehicle threshold model width between a maximum vehicle width and a minimum vehicle width, and a vehicle threshold model confidence value configured to indicate the number of vehicles within a vehicle size range in the group of vehicles in the current geographic area of ​​the first vehicle, one end of the vehicle size range being defined by the selected vehicle threshold model, and the vehicle threshold model confidence value being selected by the V2X system based on a distribution of vehicles in the current geographic area of ​​the first vehicle; a second vehicle size boundary is determined, wherein the second vehicle size boundary is based on the second vehicle location, the second vehicle orientation, the selected vehicle threshold model length, and the selected vehicle threshold model width received in the V2X message; The system determines whether any portion of the first vehicle size boundary overlaps with any portion of the second vehicle size boundary; in response to the determination that any portion of the first vehicle size boundary overlaps with any portion of the second vehicle size boundary, it identifies a position overlap misconduct; based on the vehicle threshold model confidence value, it calculates a confidence level associated with the identification of the position overlap misconduct; based on the calculated confidence level being greater than a confidence threshold, it generates a misconduct report identifying the position overlap misconduct; it sends the misconduct report to a misconduct management agency; and based on a response from the misconduct management agency, it updates the software associated with the sensor.

2. According to the method of request item 1, wherein: In response to the determination that the first vehicle has left a first geographic region and entered a current geographic region, the system performs the selection of a vehicle threshold model for the second vehicle from the vehicle threshold model set for the current geographic region for the first vehicle, wherein the vehicle threshold model set for the current geographic region is different from the vehicle threshold model set for the first geographic region.

3. According to the method of request item 1, a length and a width are assigned to the second vehicle in the vehicle threshold model based on a distribution of vehicle length and vehicle width in the current geographic region.

4. The method according to claim 3 further includes the following steps: assigning the first vehicle length and the first vehicle width according to values ​​contained in the selected vehicle threshold model.

5. The method according to claim 3, wherein the vehicle threshold model set includes the following steps: a minimum vehicle threshold model, the minimum vehicle threshold model including a minimum dimension and a minimum vehicle confidence value for a minimum vehicle size in the current geographic region, wherein the minimum dimension includes the minimum vehicle length and the minimum vehicle width; and a maximum vehicle threshold model, the maximum vehicle threshold model including a maximum dimension and a maximum vehicle confidence value for a maximum vehicle size in the current geographic region, wherein the maximum dimension includes the maximum vehicle length which is greater than an actual longest vehicle length and the maximum vehicle width which is greater than an actual widest vehicle width.

6. According to the method of claim 5, wherein the selected vehicle threshold model includes a selected threshold vehicle length, a selected threshold vehicle width, and a selected threshold vehicle confidence value for a threshold vehicle in the current geographic region, wherein the selected threshold vehicle confidence value for the threshold vehicle is a percentage of all vehicles in the current geographic region minus a percentage of vehicles in the current geographic region having an actual length greater than the length of the selected vehicle threshold model and an actual width greater than the width of the selected vehicle threshold model, further wherein the calculated confidence level is equal to the percentage of vehicles smaller than the minimum dimension minus the vehicle threshold model confidence value of the threshold vehicle in the current geographic region.

7. The method according to claim 3, wherein the vehicle threshold model set includes the following steps: a minimum vehicle threshold model, the minimum vehicle threshold model including a minimum dimension and a minimum vehicle confidence value for a minimum vehicle size in the current geographic region, wherein the minimum dimension includes the minimum vehicle length which is less than an actual shortest vehicle length and the minimum vehicle width which is less than an actual narrowest vehicle width; and a maximum vehicle threshold model, the maximum vehicle threshold model including a maximum dimension and a maximum vehicle confidence value for the maximum vehicle size in the current geographic region, wherein the maximum dimension includes the maximum vehicle length and the maximum vehicle width.

8. According to the method of claim 7, wherein the selected vehicle threshold model includes a selected threshold vehicle length, a selected threshold vehicle width, and a selected threshold vehicle confidence value for a threshold vehicle in the current geographic region, wherein the selected threshold vehicle confidence value is a percentage of all vehicles in the current geographic region minus a percentage of vehicles in the current geographic region having an actual length smaller than the length of the selected vehicle threshold model and an actual width smaller than the width of the selected vehicle threshold model, further wherein: The calculated confidence level is equal to the percentage of vehicles larger than the maximum dimension minus the vehicle threshold model confidence value of the threshold vehicles in the current geographic region.

9. A vehicle-to-everything (V2X) system, comprising: One memory; One transceiver; and a processor coupled to the memory and the transceiver, wherein the processor is configured to have processor-executable instructions to: perform an autonomous or semi-autonomous driving action by a first vehicle; determine a first vehicle position and a first vehicle orientation based on sensor data generated by a sensor of the first vehicle; determine a first vehicle size boundary, wherein the first vehicle size boundary is based on a first vehicle length, a first vehicle width, the first vehicle position, and the first vehicle orientation; and receive a V2X message from a second vehicle, wherein the V2X message includes a second vehicle position and a second vehicle orientation. A vehicle threshold model is selected for the second vehicle from a set of vehicle threshold models for the first vehicle in a current geographic area, wherein the set of vehicle threshold models for the current geographic area includes vehicle threshold models determined based on a group of vehicles with multiple sizes in the current geographic area, and wherein the selected vehicle threshold model includes a selected vehicle threshold model length between a maximum vehicle length and a minimum vehicle length, a selected vehicle threshold model width between a maximum vehicle width and a minimum vehicle width, and a vehicle threshold model confidence value configured to indicate the number of vehicles within a vehicle size range in the group of vehicles in the current geographic area of ​​the first vehicle, one end of the vehicle size range being defined by the selected vehicle threshold model, and the vehicle threshold model confidence value being selected by the V2X system based on a distribution of vehicles in the current geographic area of ​​the first vehicle; a second vehicle size boundary is determined, wherein the second vehicle size boundary is based on the second vehicle location, the second vehicle orientation, the selected vehicle threshold model length, and the selected vehicle threshold model width received in the V2X message; The system determines whether any portion of the first vehicle size boundary overlaps with any portion of the second vehicle size boundary; in response to the determination that any portion of the first vehicle size boundary overlaps with any portion of the second vehicle size boundary, it identifies a position overlap misconduct; based on the vehicle threshold model confidence value, it calculates a confidence level associated with the identification of the position overlap misconduct; based on the calculated confidence level being greater than a confidence threshold, it generates a misconduct report identifying the position overlap misconduct; it sends the misconduct report to a misconduct management agency; and based on a response from the misconduct management agency, it updates the software associated with the sensor.

10. The V2X system according to claim 9, wherein the processor is further configured to have processor-executable instructions to: in response to a determination that the first vehicle has left a first geographic region and entered a current geographic region, perform the selection of a vehicle threshold model for the second vehicle from a set of vehicle threshold models for the current geographic region for the first vehicle, wherein the set of vehicle threshold models for the current geographic region is different from a set of vehicle threshold models for the first geographic region.

11. According to request item 9 of the V2X system, a length and a width are assigned to the second vehicle in the vehicle threshold model based on a distribution of vehicle lengths and widths in the current geographic region.

12. According to request item 11, the V2X system, wherein the vehicle threshold model set includes: A minimum vehicle threshold model, comprising a minimum dimension and a minimum vehicle confidence value for a minimum vehicle size in the current geographic region, wherein the minimum dimension includes the minimum vehicle length and the minimum vehicle width; and a maximum vehicle threshold model, comprising a maximum dimension and a maximum vehicle confidence value for a maximum vehicle size in the current geographic region, wherein the maximum dimension includes the maximum vehicle length which is greater than an actual longest vehicle length and the maximum vehicle width which is greater than an actual widest vehicle width.

13. The V2X system according to request 12, wherein the selected vehicle threshold model includes a selected threshold vehicle length, a selected threshold vehicle width, and a selected threshold vehicle confidence value for a threshold vehicle in the current geographic region, wherein the selected threshold vehicle confidence value for the threshold vehicle is a percentage of all vehicles in the current geographic region minus a percentage of vehicles in the current geographic region having an actual length greater than the length of the selected vehicle threshold model and an actual width greater than the width of the selected vehicle threshold model, further wherein the calculated confidence level is equal to the percentage of vehicles smaller than the minimum dimension minus the vehicle threshold model confidence value of the threshold vehicle in the current geographic region.

14. According to request item 11, the V2X system, wherein the vehicle threshold model set includes: A minimum vehicle threshold model, comprising a minimum dimension for a minimum vehicle size in the current geographic region and a minimum vehicle confidence value, wherein the minimum dimension comprises a minimum vehicle length less than an actual shortest vehicle length and a minimum vehicle width less than an actual narrowest vehicle width; and a maximum vehicle threshold model, comprising a maximum dimension for a maximum vehicle size in the current geographic region and a maximum vehicle confidence value, wherein the maximum dimension comprises the maximum vehicle length and the maximum vehicle width.

15. The V2X system according to request 14, wherein the selected vehicle threshold model includes a selected threshold vehicle length, a selected threshold vehicle width, and a selected threshold vehicle confidence value for a threshold vehicle in the current geographic region, wherein the selected threshold vehicle confidence value is a percentage of all vehicles in the current geographic region minus a percentage of vehicles in the current geographic region having an actual length smaller than the length of the selected vehicle threshold model and an actual width smaller than the width of the selected vehicle threshold model, further wherein: The calculated confidence level is equal to the percentage of vehicles larger than the maximum dimension minus the vehicle threshold model confidence value of the threshold vehicles in the current geographic region.

16. A vehicle-to-everything (V2X) system, comprising: Components for performing an autonomous or semi-autonomous driving action by a first vehicle; components for determining a first vehicle position and a first vehicle orientation based on sensor data generated by a sensor of the first vehicle; components for determining a first vehicle size boundary, wherein the first vehicle size boundary is based on a first vehicle length, a first vehicle width, the first vehicle position, and the first vehicle orientation; and components for receiving a V2X message from a second vehicle, wherein the V2X message includes a second vehicle position and a second vehicle orientation. Components for selecting a vehicle threshold model for the second vehicle from a set of vehicle threshold models for the first vehicle in a current geographic area, wherein the set of vehicle threshold models for the current geographic area includes vehicle threshold models determined based on a group of vehicles with multiple sizes in the current geographic area, and wherein the selected vehicle threshold model includes a selected vehicle threshold model length between a maximum vehicle length and a minimum vehicle length, a selected vehicle threshold model width between a maximum vehicle width and a minimum vehicle width, and a vehicle threshold model confidence value configured to indicate the number of vehicles within a vehicle size range in the group of vehicles in the current geographic area of ​​the first vehicle, one end of the vehicle size range being defined by the selected vehicle threshold model, and the vehicle threshold model confidence value being selected by the V2X system based on a distribution of vehicles in the current geographic area of ​​the first vehicle; and components for determining a second vehicle size boundary, wherein the second vehicle size boundary is based on the second vehicle location, the second vehicle orientation, the selected vehicle threshold model length, and the selected vehicle threshold model width received in the V2X message; The components include: a component for determining whether any portion of the first vehicle size boundary overlaps with any portion of the second vehicle size boundary; a component for identifying a positional overlap misconduct in response to the determination that any portion of the first vehicle size boundary overlaps with any portion of the second vehicle size boundary; a component for calculating a confidence level associated with the identification of the positional overlap misconduct based on the vehicle threshold model confidence value; a component for generating a misconduct report identifying the positional overlap misconduct based on the calculated confidence level being greater than a confidence threshold; a component for sending the misconduct report to a misconduct management agency; and a component for updating the software associated with the sensor based on a response from the misconduct management agency.

17. The V2X system according to request item 16 further includes: Components used to determine whether the first vehicle has left a first geographic area and entered the current geographic area; And a component for selecting a vehicle threshold model for the second vehicle from the vehicle threshold model set for the current geographic region, wherein the vehicle threshold model set for the current geographic region is different from a vehicle threshold model set for the first geographic region.

18. The V2X system according to request item 16, wherein a length and a width are assigned to the second vehicle in the vehicle threshold model based on a distribution of vehicle lengths and vehicle widths in the current geographic region.

19. The V2X system according to request item 18, wherein the vehicle threshold model set includes: A minimum vehicle threshold model, comprising a minimum dimension and a minimum vehicle confidence value for a minimum vehicle size in the current geographic region, wherein the minimum dimension includes the minimum vehicle length and the minimum vehicle width; and a maximum vehicle threshold model, comprising a maximum dimension and a maximum vehicle confidence value for a maximum vehicle size in the current geographic region, wherein the maximum dimension includes the maximum vehicle length which is greater than an actual longest vehicle length and the maximum vehicle width which is greater than an actual widest vehicle width.

20. The V2X system according to request 19, wherein the selected vehicle threshold model includes a selected threshold vehicle length, a selected threshold vehicle width, and a selected threshold vehicle confidence value for a threshold vehicle in the current geographic region, wherein the selected threshold vehicle confidence value for the threshold vehicle is a percentage of all vehicles in the current geographic region minus a percentage of vehicles in the current geographic region having an actual length greater than the length of the selected vehicle threshold model and an actual width greater than the width of the selected vehicle threshold model, further wherein the calculated confidence level is equal to the percentage of vehicles smaller than the minimum dimension minus the vehicle threshold model confidence value for the threshold vehicle in the current geographic region.

21. According to request item 18, the V2X system, wherein the vehicle threshold model set includes: A minimum vehicle threshold model, comprising a minimum dimension for a minimum vehicle size in the current geographic region and a minimum vehicle confidence value, wherein the minimum dimension comprises a minimum vehicle length less than an actual shortest vehicle length and a minimum vehicle width less than an actual narrowest vehicle width; and a maximum vehicle threshold model, comprising a maximum dimension for a maximum vehicle size in the current geographic region and a maximum vehicle confidence value, wherein the maximum dimension comprises the maximum vehicle length and the maximum vehicle width.

22. The V2X system according to request 21, wherein the selected vehicle threshold model includes a selected threshold vehicle length, a selected threshold vehicle width, and a selected threshold vehicle confidence value for a threshold vehicle in the current geographic region, wherein the selected threshold vehicle confidence value for the threshold vehicle is a percentage of all vehicles in the current geographic region minus a percentage of vehicles in the current geographic region having an actual length smaller than the length of the selected vehicle threshold model and an actual width smaller than the width of the selected vehicle threshold model, further wherein the calculated confidence level is equal to the percentage of vehicles larger than the maximum dimension minus the vehicle threshold model confidence value of the threshold vehicle in the current geographic region.

23. A non-transitory processor-readable medium having processor-executable instructions stored thereon, the processor-executable instructions being configured to cause a processor of a first vehicle of a vehicle-to-everything (V2X) system to perform operations, the operations including: A first vehicle performs an autonomous or semi-autonomous driving action; Based on sensor data generated by a sensor of the first vehicle, a first vehicle position and a first vehicle orientation are determined; a first vehicle size boundary is determined, wherein the first vehicle size boundary is based on a first vehicle length, a first vehicle width, the first vehicle position, and the first vehicle orientation; and a V2X message is received from a second vehicle, wherein the V2X message includes a second vehicle position and a second vehicle orientation. A vehicle threshold model is selected for the second vehicle from a set of vehicle threshold models for the first vehicle in a current geographic area, wherein the set of vehicle threshold models for the current geographic area includes vehicle threshold models determined based on a group of vehicles with multiple sizes in the current geographic area, and wherein the selected vehicle threshold model includes a selected vehicle threshold model length between a maximum vehicle length and a minimum vehicle length, a selected vehicle threshold model width between a maximum vehicle width and a minimum vehicle width, and a vehicle threshold model confidence value configured to indicate the number of vehicles within a vehicle size range in the group of vehicles in the current geographic area of ​​the first vehicle, one end of the vehicle size range being defined by the selected vehicle threshold model, and the vehicle threshold model confidence value being selected by the V2X system based on a distribution of vehicles in the current geographic area of ​​the first vehicle; a second vehicle size boundary is determined, wherein the second vehicle size boundary is based on the second vehicle location, the second vehicle orientation, the selected vehicle threshold model length, and the selected vehicle threshold model width received in the V2X message; The system determines whether any portion of the first vehicle size boundary overlaps with any portion of the second vehicle size boundary; in response to the determination that any portion of the first vehicle size boundary overlaps with any portion of the second vehicle size boundary, it identifies a position overlap misconduct; based on the vehicle threshold model confidence value, it calculates a confidence level associated with the identification of the position overlap misconduct; based on the calculated confidence level being greater than a confidence threshold, it generates a misconduct report identifying the position overlap misconduct; it sends the misconduct report to a misconduct management agency; and based on a response from the misconduct management agency, it updates the software associated with the sensor.

24. The non-transitory processor-readable medium according to claim 23, wherein the processor-executable instructions stored therein are further configured to cause the processor of the V2X system to perform operations, such operations including: In response to the identification of the overlapping misconduct situation, a confidence level is calculated for the identification that a misconduct situation has occurred, wherein the confidence level for the identification that a misconduct situation has occurred is based on the confidence value of the vehicle threshold model.

25. The non-transitory processor-readable medium according to request item 24, wherein the stored processor-executable instructions are further configured to cause the processor of the V2X system to perform operations such that a length and a width are assigned to the second vehicle in the vehicle threshold model based on a distribution of vehicle lengths and vehicle widths in the current geographic region.

26. The non-transitory processor-readable medium according to request item 25, wherein the processor-executable instructions stored therein are further configured to cause the processor of the V2X system to perform operations, such that the vehicle threshold model set includes: A minimum vehicle threshold model, comprising a minimum dimension and a minimum vehicle confidence value for a minimum vehicle size in the current geographic region, wherein the minimum dimension includes the minimum vehicle length and the minimum vehicle width; and a maximum vehicle threshold model, comprising a maximum dimension and a maximum vehicle confidence value for a maximum vehicle size in the current geographic region, wherein the maximum dimension includes the maximum vehicle length which is greater than an actual longest vehicle length and the maximum vehicle width which is greater than an actual widest vehicle width.

27. The non-transitory processor-readable medium according to claim 26, wherein the stored processor-executable instructions are further configured to cause the processor of the V2X system to perform operations such that a selected vehicle threshold model includes a selected threshold vehicle length, a selected threshold vehicle width, and a selected threshold vehicle confidence value for threshold vehicles in the current geographic region, wherein the selected threshold vehicle confidence value for the threshold vehicle is a percentage of all vehicles in the current geographic region minus a percentage of vehicles in the current geographic region having an actual length greater than the length of the selected vehicle threshold model and an actual width greater than the width of the selected vehicle threshold model; and the calculated confidence level is equal to the percentage of vehicles smaller than the minimum dimension minus the vehicle threshold model confidence value for the threshold vehicles in the current geographic region.

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