CN113650616B - Vehicle behavior prediction method and system based on collected data - Google Patents
Vehicle behavior prediction method and system based on collected data Download PDFInfo
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- CN113650616B CN113650616B CN202110822812.4A CN202110822812A CN113650616B CN 113650616 B CN113650616 B CN 113650616B CN 202110822812 A CN202110822812 A CN 202110822812A CN 113650616 B CN113650616 B CN 113650616B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
- B60W60/001—Planning or execution of driving tasks
- B60W60/0015—Planning or execution of driving tasks specially adapted for safety
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
Abstract
The invention relates to a vehicle behavior prediction method and a system based on collected data, wherein the method comprises the following steps: acquiring forward vehicle data and backward vehicle data acquired by a vehicle; extracting the current position and speed of a target vehicle from forward vehicle data acquired by a current vehicle; determining the initial position and the initial time of a target vehicle according to historical data of backward vehicles acquired by the vehicle; and predicting the motion track of the target vehicle before the target vehicle is found by the vehicle according to the current position and speed of the target vehicle and the initial position and initial time of the target vehicle. According to the invention, the behavior prediction of the vehicle beyond the measurement range of the sensor is realized by comparing the vehicle data acquired by the vehicle with the historical data and combining with the kinematics model.
Description
Technical Field
The invention belongs to the technical field of automatic driving and measurement of vehicles, and particularly relates to a vehicle behavior prediction method and system based on collected data.
Background
When extracting a certain scene of vehicle data collection, can find that the sensor has the measurement blind area: that is, when the object is beyond the recognition range of the sensor, the sensor cannot recognize the relevant information of the object (including the vehicle), and the information of the vehicle or other objects can be acquired only when the vehicle enters the range of the sensor. Therefore, when the scene imported by the collected data is restored or the virtual scene is simulated, a part of vehicles or other targets can flash.
Disclosure of Invention
In order to solve the problem mentioned in the background art that the historical track of the flash vehicle cannot be predicted, in a first aspect of the present invention, a vehicle behavior prediction method based on collected data is provided, including: acquiring forward vehicle data and backward vehicle data acquired by a vehicle; extracting the current position and speed of a target vehicle from forward vehicle data acquired by a current vehicle; determining the initial position and the initial time of a target vehicle according to historical data of backward vehicles acquired by the vehicle; and predicting the motion track of the target vehicle before the target vehicle is found by the vehicle according to the current position and speed of the target vehicle and the initial position and initial time of the target vehicle.
In some embodiments, the determining the initial position and the initial time of the target vehicle according to the historical data of the backward vehicle collected by the own vehicle includes the following steps: identifying one or more target vehicles in the current forward vehicle data, and recording the target vehicles as a first type of target vehicle; screening a first type of target vehicle with the occurrence frequency lower than a threshold value from historical data of backward vehicles acquired by the vehicle according to a preset time interval or preset displacement of the vehicle, and marking the first type of target vehicle as a second type of target vehicle; and determining the initial position and the initial time of the second type of target vehicle according to the accuracy and the range of the vehicle distance sensor.
In some embodiments, the predicting of the motion trajectory of the target vehicle before the target vehicle is discovered by the self vehicle according to the current position and speed of the target vehicle and the initial position and initial time of the target vehicle comprises the following steps: acquiring the current position and speed of a target vehicle, and the initial position and initial time of the target vehicle; and predicting the motion track of the target vehicle from the initial position to the position before the target vehicle is found by the own vehicle according to the vehicle kinematic model.
Further, the vehicle kinematics model includes at least one of a constant velocity model, a constant acceleration model, a constant turn, and a constant velocity model.
In the above embodiment, the method further includes: if the motion track of the target vehicle before being found by the vehicle is overlapped with the tracks of other vehicles, predicting again; otherwise, the motion track of the target vehicle before being discovered by the vehicle is led into a simulation scene.
In a second aspect of the present invention, a method for optimizing a vehicle simulation scenario is provided, which includes the method for predicting vehicle behavior based on collected data provided in the first aspect of the present invention.
The invention provides a vehicle behavior prediction system based on collected data, which comprises an acquisition module, an extraction module, a determination module and a prediction module, wherein the acquisition module is used for acquiring forward vehicle data and backward vehicle data collected by a vehicle; the extraction module is used for extracting the current position and speed of the target vehicle from the forward vehicle data acquired by the current vehicle; the determining module is used for determining the initial position and the initial time of the target vehicle according to the historical data of the backward vehicle acquired by the vehicle; and the prediction module is used for predicting the motion trail of the target vehicle before the target vehicle is found by the vehicle according to the current position and speed of the target vehicle and the initial position and initial time of the target vehicle.
Further, the determining module comprises an identifying unit, a screening unit and a determining unit, wherein the identifying unit is used for identifying one or more target vehicles in the current forward vehicle data and marking the target vehicles as the first type of target vehicles; the screening unit is used for screening a first type of target vehicle with the occurrence frequency lower than a threshold value from historical data of backward vehicles acquired by the vehicle according to a preset time interval or preset displacement of the vehicle, and marking the first type of target vehicle as a second type of target vehicle; and the determining unit is used for determining the initial position and the initial time of the second type of target vehicle according to the accuracy and the range of the vehicle distance sensor.
In a fourth aspect of the present invention, there is provided an electronic apparatus comprising: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the collected data-based vehicle behavior prediction method provided by the first aspect of the invention.
In a fifth aspect of the present invention, a computer-readable medium is provided, on which a computer program is stored, wherein the computer program, when being executed by a processor, implements the collected data based vehicle behavior prediction method provided by the first aspect of the present invention.
The beneficial effects of the invention are:
1. the invention can predict the vehicle behavior beyond the range of the sensor and the unrecognized object behavior which appears suddenly;
2. according to the invention, vehicle data acquired by the vehicle is compared with historical data, and the vehicle behavior prediction beyond the measurement range of the sensor is realized by combining a kinematic model;
3. the phenomenon that the vehicle flashes (from the measuring blind area to the detected vehicle within a very short time) can occur when the simulation scene is automatically converted, and after the method is improved, the phenomenon that the vehicle flashes in the simulation scene can be eliminated can be realized, so that the simulation scene restoration degree is higher.
Drawings
FIG. 1 is a schematic basic flow diagram of a method for vehicle behavior prediction based on collected data in some implementations of the invention;
FIG. 2a is a schematic view of a target vehicle out of a measurement range of the subject vehicle in some implementations of the invention;
FIG. 2b is a schematic illustration of a target vehicle being discovered by an own vehicle in some implementations of the invention;
FIG. 3 is a method of optimizing a vehicle simulation scenario in some implementations of the invention;
FIG. 4 is a schematic diagram of a vehicle behavior prediction system based on collected data in some implementations of the invention;
fig. 5 is a schematic diagram of an electronic device in some implementations of the invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, in a first aspect of the invention there is provided a vehicle behaviour prediction method based on collected data, comprising: s100, acquiring forward vehicle data and backward vehicle data acquired by a vehicle; s200, extracting the current position and speed of a target vehicle from forward vehicle data acquired by the current vehicle; s300, determining an initial position and initial time of a target vehicle according to historical data of backward vehicles acquired by the vehicle; s400, predicting the motion track of the target vehicle before the target vehicle is found by the vehicle according to the current position and speed of the target vehicle and the initial position and initial time of the target vehicle.
It is understood that the initial position and the initial time refer to a time (time) at which the target vehicle is first found by the backward sensor of the own vehicle when the own vehicle makes a scene change or a change in motion state (e.g., lane change, acceleration, or deceleration) occurs. The prediction method provided by the invention can be used for simultaneous prediction of one own vehicle for one or more target vehicles, due to different sensor numbers and measurement ranges of the own vehicles.
Referring to fig. 2a and 2b, in step S300 in some embodiments, the determining the initial position and the initial time of the target vehicle according to the historical data of the backward vehicle collected by the own vehicle includes the following steps: identifying one or more target vehicles in the current forward vehicle data, and recording the target vehicles as a first type of target vehicle; screening a first type of target vehicle with the occurrence frequency lower than a threshold value from historical data of backward vehicles acquired by the vehicle according to a preset time interval or preset displacement of the vehicle, and marking the first type of target vehicle as a second type of target vehicle; and determining the initial position and the initial time of the second type of target vehicle according to the accuracy and the range of the vehicle distance sensor.
Specifically, in a lane change scene, when the lane change of the vehicle is completed (from the state in fig. 2a to the state in fig. 2 a), a vehicle object is found at a position 200 m ahead, and the time is 5s relative to the scene; after the target vehicle is located in the measuring blind area of the vehicle for 5S, a vehicle object (target vehicle) is found 200 m in front, and the motion track or state of the front 5S of the vehicle cannot be predicted. It can be understood that the above-mentioned scene is an example of lane change, but the above-mentioned phenomenon does not affect other scenes when the motion state of the own vehicle changes, for example: cornering, hard braking, sudden acceleration, etc.
In step S400 of some embodiments, the predicting the motion trajectory of the target vehicle before being discovered by the self vehicle according to the current position and speed of the target vehicle and the initial position and initial time of the target vehicle includes the following steps: acquiring the current position and speed of a target vehicle, and the initial position and initial time of the target vehicle; and predicting the motion track of the target vehicle from the initial position to the position before the target vehicle is found by the own vehicle according to the vehicle kinematic model.
Specifically, assuming that the starting position of the vehicle is (0, 0) speed E _ V, and the vehicle C is found to be ahead by longitudinal distance C _ x and lateral distance C _ y after traveling N seconds, and the speed of the vehicle C is C _ V at this time, then 0-N seconds are now unknown, and the behavior of the vehicle between 0-N seconds is predicted by:
longitudinal position: ((EjuVxN) + CjuxVxN; transverse position: (0C _ Y); by comparing the longitudinal and lateral positions of other vehicles to determine if there is coincidence with the other vehicle position, a new prediction can be made by adjusting the predicted vehicle speed and lane change behavior. For example, if the predicted vehicle speed is C _ V, the position of each second can be predicted again by increasing the speed of C _ V and compared.
Further, the vehicle kinematic model includes at least one of a Constant Velocity model (CV), a Constant Acceleration model (CA), a Constant Turn Rate and Velocity model (CTRV), or a Constant Curvature and Acceleration (CCA).
In the above embodiment, the method further includes: if the motion track of the target vehicle before being found by the vehicle is overlapped with the tracks of other vehicles, predicting again; otherwise, the motion track of the target vehicle before being discovered by the vehicle is led into a simulation scene.
Referring to fig. 3, in a second aspect of the invention, there is provided a method for optimizing a vehicle simulation scenario, including the method for predicting vehicle behavior based on collected data according to the first aspect. The method comprises the following specific steps:
step 2, acquiring the initial appearance time, position and speed of each automobile object;
and 3, screening the vehicles overtaking from the rear by using the initial time and the initial position of the vehicles. Calculating the moving distance of the vehicle according to the speed when the vehicle is found and the time when the vehicle object is found relative to the scene by the starting speed and the starting time, calculating the position, the transverse distance and the longitudinal distance information of the vehicle in each second in the scene, comparing the information with other vehicle moving behavior tracks, judging whether the vehicle with the predicted behavior is overlapped with the positions of other vehicles or not, and if so, predicting again;
step 4, comparing the positions of the vehicle objects in each second (each frame), judging whether the vehicle objects are overlapped, if not, carrying out the step 5, and if so, carrying out the step 4;
and 5, converting and simulating the scene, converting the data into format information which can be used by simulation software, and importing the format information into the simulation scene.
Example 2
In a third aspect of the present invention, a vehicle behavior prediction system 1 based on collected data is provided, including an obtaining module 11, an extracting module 12, a determining module 13, and a predicting module 14, where the obtaining module 11 is configured to obtain forward vehicle data and backward vehicle data collected by a vehicle; the extraction module 12 is configured to extract a current position and a current speed of the target vehicle from forward vehicle data acquired by the current vehicle; the determining module 13 is configured to determine an initial position and an initial time of the target vehicle according to historical data of a backward vehicle acquired by the vehicle; the prediction module 14 is configured to predict a motion trajectory of the target vehicle before the target vehicle is found by the vehicle according to the current position and speed of the target vehicle, and the initial position and initial time of the target vehicle.
Further, the determining module 13 includes an identifying unit, a screening unit, and a determining unit, where the identifying unit is configured to identify one or more target vehicles in the current forward vehicle data, and mark them as a first type of target vehicle; the screening unit is used for screening a first type of target vehicle with the occurrence frequency lower than a threshold value from historical data of backward vehicles acquired by the vehicle according to a preset time interval or preset displacement of the vehicle, and marking the first type of target vehicle as a second type of target vehicle; and the determining unit is used for determining the initial position and the initial time of the second type of target vehicle according to the accuracy and the measuring range of the own vehicle distance sensor.
Example 3
In a fourth aspect of the present invention, there is provided an electronic apparatus comprising: one or more processors; a storage device configured to store one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the collected data-based vehicle behavior prediction method provided by the first aspect of the present invention.
Referring to fig. 5, an electronic device 500 may include a processing means (e.g., central processing unit, graphics processor, etc.) 501 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM502, and the RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
The following devices may be connected to the I/O interface 505 in general: input devices 506 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; a storage device 508 including, for example, a hard disk; and a communication device 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 5 illustrates an electronic device 500 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 5 may represent one device or may represent multiple devices as desired.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. The computer program, when executed by the processing device 501, performs the above-described functions defined in the methods of embodiments of the present disclosure. It should be noted that the computer readable medium described in the embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may be separate and not incorporated into the electronic device. The computer readable medium carries one or more computer programs which, when executed by the electronic device, cause the electronic device to:
computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + +, python, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (8)
1. A vehicle behavior prediction method based on collected data is applied to scene simulation and is characterized by comprising the following steps:
acquiring forward vehicle data and backward vehicle data acquired by a vehicle;
extracting the current position and speed of a target vehicle from forward vehicle data acquired by a current vehicle;
determining the initial position and the initial time of a target vehicle according to historical data of backward vehicles acquired by the vehicle: identifying one or more target vehicles in the current forward vehicle data, and recording the target vehicles as a first type of target vehicle; screening a first type of target vehicle with the occurrence frequency lower than a threshold value from historical data of backward vehicles acquired by the vehicle according to a preset time interval or preset displacement of the vehicle, and marking the first type of target vehicle as a second type of target vehicle; determining the initial position and the initial time of the second type of target vehicle according to the accuracy and the measuring range of the self vehicle distance sensor; the initial position and the initial time refer to the position and the time of a target vehicle which are firstly discovered by a backward sensor of the vehicle when the vehicle carries out scene switching or the motion state of the vehicle is changed;
and predicting the motion track of the target vehicle before the target vehicle is found by the vehicle according to the current position and speed of the target vehicle and the initial position and initial time of the target vehicle.
2. The vehicle behavior prediction method based on the collected data as claimed in claim 1, wherein the step of predicting the movement locus of the target vehicle before the target vehicle is found by the own vehicle based on the current position and speed of the target vehicle and the initial position and initial time of the target vehicle comprises the steps of:
acquiring the current position and speed of a target vehicle, and the initial position and initial time of the target vehicle;
and predicting the motion track of the target vehicle from the initial position to the position before the target vehicle is found by the own vehicle according to the vehicle kinematic model.
3. The collected data-based vehicle behavior prediction method of claim 2, wherein the vehicle kinematics model comprises at least one of a constant velocity model, a constant acceleration model, a constant turn, and a constant velocity model.
4. The collected data-based vehicle behavior prediction method according to any one of claims 1-3, further comprising:
if the motion track of the target vehicle before being found by the vehicle is coincident with the tracks of other vehicles, predicting again; otherwise, the motion track of the target vehicle before being discovered by the vehicle is led into a simulation scene.
5. A vehicle simulation scenario optimization method, comprising the vehicle behavior prediction method based on collected data of any one of claims 1 to 4.
6. A vehicle behavior prediction system based on collected data is characterized by comprising an acquisition module, an extraction module, a determination module and a prediction module,
the acquisition module is used for acquiring the forward vehicle data and the backward vehicle data acquired by the vehicle;
the extraction module is used for extracting the current position and speed of the target vehicle from the forward vehicle data acquired by the current vehicle;
the determining module is used for determining the initial position and the initial time of the target vehicle according to the historical data of the backward vehicle collected by the vehicle: determining the initial position and the initial time of a target vehicle according to historical data of backward vehicles acquired by the vehicle: identifying one or more target vehicles in the current forward vehicle data, and recording the target vehicles as a first type of target vehicle; screening a first type of target vehicle with the occurrence frequency lower than a threshold value from historical data of backward vehicles acquired by the vehicle according to a preset time interval or preset displacement of the vehicle, and marking the first type of target vehicle as a second type of target vehicle; determining the initial position and the initial time of the second type of target vehicle according to the accuracy and the measuring range of the self vehicle distance sensor; the initial position and the initial time refer to the position and the time of a target vehicle which are firstly discovered by a backward sensor of the vehicle when the vehicle carries out scene switching or the motion state of the vehicle is changed;
and the prediction module is used for predicting the motion track of the target vehicle before the target vehicle is found by the vehicle according to the current position and speed of the target vehicle and the initial position and initial time of the target vehicle.
7. An electronic device, comprising: one or more processors; storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the collected data-based vehicle behavior prediction method of any one of claims 1-4.
8. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out a method of vehicle behaviour prediction based on collected data according to any one of claims 1 to 4.
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