WO2024003473A1

WO2024003473A1 - Method and device for controlling a driver-assistance system of a vehicle travelling in a road environment that includes an intersection

Info

Publication number: WO2024003473A1
Application number: PCT/FR2023/000100
Authority: WO
Inventors: Soumia NID BOUHOU; Zineb RAJI
Original assignee: Stellantis Auto Sas
Priority date: 2022-06-30
Filing date: 2023-05-12
Publication date: 2024-01-04
Also published as: FR3137352A1

Abstract

The present invention relates to a method and a device for controlling an ADAS system of a vehicle (10) travelling on a first portion of road (101) approaching an intersection with a second portion of road (102). To this end, mapping data for the road environment (1) are received, these mapping data including data on the intersection between the first and the second portion of road (101, 102). Intersection data are also obtained from data received from an on-board camera. Comparing the intersection data obtained from the mapping data with the camera data makes it possible to determine a confidence level for the mapping data. The ADAS system is controlled on the basis of the intersection data according to the confidence level.

Description

DESCRIPTION

Title: Method and device for controlling a driving assistance system for a vehicle traveling in a road environment including an intersection

5

Technical area

[0001] The present invention claims priority from French application 2206639 filed on June 30, 2022, the content of which (text, drawings and claims) is here incorporated by reference. The present invention relates to methods and devices for assisting with

10 driving a vehicle, in particular a motor vehicle. The present invention also relates to a method and a device for controlling a driving assistance system embedded in a vehicle. The present invention also relates to a method and a device for controlling a vehicle incorporating one or more driving assistance systems, in particular an autonomous vehicle.

15

Technology background

[0002] Road safety is one of the important issues facing our societies. With the increase in the number of vehicles circulating on road networks around the world, regardless of traffic conditions, the risk of accidents and

20 incidents caused by traffic conditions have never been so high.

[0003] To improve road safety, certain contemporary vehicles are equipped with driving assistance functions or systems, called ADAS (from the English “Advanced Driver-Assistance System” or in French “Driving Assistance System”). advance "). THE

25 ADAS systems, for example, implement processes based on knowledge of the road environment in which the vehicle is traveling. Knowledge of this environment is for example obtained from environmental mapping data and/or data obtained from one or more sensors embedded in the vehicle, such as cameras, radars, or even lidars. (from the English “Light Detection And Ranging”, or “Detection and estimation of distance by light” in French).

[0004] To ensure the proper functioning of the ADAS systems and the safety of the vehicle, it is then important that the environmental data on which the or

5 ADAS systems are reliable.

Summary of the present invention

[0006] An object of the present invention is to solve at least one of the problems of the technological background described above.

[000K] Another object of the present invention is to improve the operation of an ADAS system of a vehicle.

Another object of the present invention is to improve the reliability of data on the environment in which the vehicle is traveling.

[0009] According to a first aspect, the present invention relates to a method of controlling a

15 driving assistance system, called the ADAS system, of a vehicle, the vehicle traveling on a first portion of road in a road environment, the method comprising the following steps:

- reception of mapping data of the road environment, the mapping data comprising intersection data representative of an intersection

20 between the first portion of road and a second portion of road, the intersection being located in front of the vehicle in a direction of movement of the vehicle, the intersection data comprising first distance information between a current position of the vehicle and the intersection;

- reception of camera data representative of part of the road environment

25 located in front of the vehicle according to the direction of movement of the vehicle from a camera on board the vehicle;

- determination of second distance information between the current position of the vehicle and the intersection from the camera data;

- comparison of the first information and the second information; - determination of a confidence level associated with the intersection data from a result of the comparison;

- control of the ADAS system from intersection data according to the confidence level.

[001 Determining a confidence level associated with intersection data included in mapping data received by the vehicle from data obtained from a camera on board the vehicle makes it possible to ensure that the intersection data intersection are consistent with the environment data obtained from the camera.

[001H(] Controlling the ADAS system according to this reliability level makes it possible to adjust the operation of the ADAS system to the reliability level and to anticipate a possible problem when the data is judged to be unreliable or not reliable enough.

[0012] According to a variant, the confidence level corresponds to:

- a minimum confidence level when a difference between the first information 15 and the second information is greater than a first threshold value;

- an intermediate confidence level when the difference between the first information and the second information is between the first threshold value and a second threshold value, the first threshold value being greater than the second threshold value;

20 - a maximum confidence level when the difference between the first information and the second information is less than the second threshold value.

[0013] According to another variant, the first threshold value is equal to 5 meters and the second threshold value is equal to 7 meters.

[0014] According to an additional variant, the method further comprises the steps of:

25 - determination of third information representative of a type of road environment from mapping data;

- determination of fourth information representative of a type of road environment from the camera data;

- comparison of the third information and the fourth information, [0015] the associated confidence level also being a function of a result of the comparison of the third information and the fourth information.

[0016] According to yet another variant, the confidence level determined from the result of the comparison between the first information and the second information is

5 increased when the result of the comparison of the third information and the fourth information indicates that the third information and the fourth information are representative of the same type.

[0017] According to an additional variant, the determination of the second distance information comprises detection of the intersection from data representative of ground marking lines associated with the first portion of road and the second portion of road, the data representative of ground marking lines being obtained by processing camera data.

[0018] According to another variant, the ADAS system corresponds to a predictive speed regulation system, called the PCC system.

[001^ According to a second aspect, the present invention relates to a device for controlling a driving assistance system, called the ADAS system, of a vehicle, the device comprising a memory associated with a processor configured for implementing implementation of the steps of the method according to the first aspect of the present invention.

[0020] According to a third aspect, the present invention relates to a vehicle, for example of the automobile type, comprising a device as described above according to the second aspect of the present invention.

[0021] According to a fourth aspect, the present invention relates to a computer program which includes instructions adapted for the execution of the steps of the method according to the first aspect of the present invention, in particular when the program

25 computer is executed by at least one processor.

[0022] Such a computer program can use any programming language, and be in the form of a source code, an object code, or an intermediate code between a source code and an object code , such as in partially compiled form, or in any other desirable form. [0023] According to a fifth aspect, the present invention relates to a computer-readable recording medium on which is recorded a computer program comprising instructions for executing the steps of the method according to the first aspect of the present invention.

[0024^ On the one hand, the recording medium can be any entity or device capable of storing the program. For example, the medium may comprise a storage means, such as a ROM memory, a CD-ROM or a ROM memory of the microelectronic circuit type, or even a magnetic recording means or a hard disk.

[002fi] On the other hand, this recording medium can also be a transmissible medium such as an electrical or optical signal, such a signal being able to be conveyed via an electrical or optical cable, by conventional or terrestrial radio or by laser beam self-directed or by other means. The computer program according to the present invention can in particular be downloaded onto an Internet type network.

[002f^ Alternatively, the recording medium may be an integrated circuit in which the computer program is incorporated, the integrated circuit being adapted to execute or to be used in the execution of the method in question.

Brief description of the figures

[0027(] Other characteristics and advantages of the present invention will emerge from the description of the particular and non-limiting examples of embodiment of the present invention below, with reference to the appended Figures 1 to 3, in which:

[0028] [Fig. 1] schematically illustrates an environment 1 in which a vehicle operates, according to a particular and non-limiting exemplary embodiment of the present invention;

[002^ [Fig. 2] schematically illustrates a device configured to control a driving assistance system of the vehicle of FIG. 1, according to a particular and non-limiting embodiment of the present invention; [0030] [Fig. 3] illustrates a flowchart of the different stages of a method of controlling a driving assistance system of the vehicle of FIG. 1, according to a particular and non-limiting exemplary embodiment of the present invention.

[0031| Description of the implementation examples

[0032] A method and a device for controlling a driving assistance system, called the ADAS system, of a vehicle will now be described in what follows with reference jointly to Figures 1 to 3. The same elements are identified with the same reference signs throughout the description which follows.

[003fî] According to a particular and non-limiting example of embodiment of the present invention, the control of one or more ADAS systems of a vehicle traveling on a first portion of road approaching an intersection with one or more second portions of route includes the reception, at a determined time instant corresponding for example to a current instant, of environmental mapping data

15 road comprising the first road portion and the second road portion(s). These mapping data advantageously include data representative of one or more intersections between the first portion of road and the second portion(s) of road downstream of the first portion of road. The intersection data advantageously includes first information of

20 distance between the current position of the vehicle and each intersection. Camera data representative of the road environment located in front of the vehicle are received from a camera on board the vehicle, these camera data being processed to determine second distance information between the current position of the vehicle and each intersection. For a given intersection, the distance between the

25 current position of the vehicle and the intersection obtained from the mapping data is compared to the distance between the current position of the vehicle and this same intersection obtained from the camera data. The result of the comparison makes it possible to check whether the two distances are coherent and makes it possible to obtain a level of confidence (for example among a minimum level, an intermediate level and a

30 maximum level) associated with the intersection data of the mapping data. The ADAS system, for example a predictive cruise control system, called PCC system (from English “Predictive Cruise Control”) or P-ACC system from English “Predictive Adaptive Cruise Control” or in French “regulation system adaptive predictive speed"), is then controlled from the intersection data 5 according to the confidence level.

[0034] Figure 1 schematically illustrates an environment 1 in which a vehicle 10 operates, according to a particular and non-limiting exemplary embodiment of the present invention.

[003fi] The vehicle 10 corresponds for example to a motor vehicle operating in a road environment 1 comprising one or more roads or portions of road.

According to other examples, the vehicle 10 corresponds to a coach, a bus, a truck, a utility vehicle or a motorcycle, that is to say a vehicle of the motorized land vehicle type.

[0037 The vehicle 10 corresponds for example to a vehicle circulating in an autonomous or semi-autonomous mode. The vehicle travels for example according to a level of autonomy greater than or equal to 2, according to the scale defined by the American federal agency which has established 5 levels of autonomy ranging from 1 to 5, level 0 corresponding to a vehicle n having no autonomy, whose driving is under the total supervision of the driver, level 1 corresponding to a vehicle with a minimum level of autonomy, whose driving is under the supervision of the driver with minimal assistance from an ADAS system , and level 5 corresponding to a completely autonomous vehicle.

[0038] The vehicle 10 carries one or more driving assistance systems, called ADAS (from 25 English “Advanced Driver-Assistance System” or in French “Advanced Driving Assistance System”). Such ADAS systems are configured to assist, or even replace, the driver of the vehicle 10 to control the vehicle 10 on its route.

[0039] The vehicle 10 carries for example one or more of the following ADAS systems for this purpose: - adaptive cruise control system, known as ACC (from the English “Adaptive Cruise Control”); and or

- predictive cruise control, known as PCC system (from English “Predictive Cruise Control”); and or

5 - intelligent speed adaptation system, called ISA system (from the English

“Intelligent Speed Adaptation”); and or

- curve speed adaptation system, called CSA system (from English “Curve Speed Assist”); and or

- electronic stability control system, known as the ESC system (from the English

10 “Electronic Stability Control” or in French “Electronic Stability Control”),

DSC (from the English “Dynamic Stability Control” or in French “Controlle Dynamo de la Stability”) or even ESP (from the English “Electronic Stability Program” or in French “Electronics of Stability Program”); and or

- vehicle lane assistance system, known as the LKA system (from the English “Lane-Keeping Assist” or in French “Lane Keeping Assistant”) or LPA (from the English “Lane-Keeping Assist” Lane Positioning Assist” or in French “Lane positioning assistant”); and or

- semi-automatic lane change system (from the English SALC from the English “Semi Automatic Lane Change”).

[0040] The examples of ADAS systems in the list above are provided for illustrative purposes and are not limiting, this list is not exhaustive.

[0041] The ADAS systems on-board the vehicle 10 are for example powered by data obtained from one or more on-board sensors, such as for example radars, LIDARs and/or cameras, and/or data received from an infrastructure of

25 communications.

[0042] According to a particular embodiment, the vehicle 10 carries a communication system configured to communicate with one or more remote devices 111 via an infrastructure of a wireless communication network. The remote device 111 corresponds for example to a “cloud” server 100 (or “cloud” in French).

30 The wireless communication infrastructure comprises, for example, a set of communication devices 110 of the cellular network antenna type of LTE 4G or 5G type or of the UBR type (Roadside Unit).

[0043] The communication system of the vehicle 10 comprises for example one or more communication antennas connected to a telematics control unit, called TCU (of

5 the English “Telematic Control Unit”), itself connected to one or more computers of the on-board system of the vehicle 10, in particular one or more computers in charge of controlling the ADAS systems of the vehicle 10. The antenna(s), the TCU unit and the computer(s) form for example a multiplexed architecture for the production of different services useful for the proper functioning of the vehicle and to assist the

10 driver and/or passengers of the vehicle in control of the vehicle 10. The computer(s) and the TCU unit communicate and exchange data with each other via one or more computer buses, for example a communication bus CAN data bus type (from the English “Controller Area Network” or in French “Réseau de controlleres”), CAN FD (from the English “Controller Area

15 Network Flexible Data-Rate", FlexRay (according to ISO 17458) or Ethernet (according to ISO/IEC 802-3).

[0044] The wireless communication system allowing the exchange of data between the vehicle 10 and the remote device(s) 111 corresponds for example to:

20 - a vehicle-to-infrastructure V2I communication system, for example based on the 3GPP LTE-V or IEEE 802.11p standards of ITS G5; Or

- a cellular network type communication system, for example an LTE type network (from the English “Long-Term Evolution” or in French “Evolution à long

25 term"), LTE-Advanced (or in French LTE-advanced) LTE 4G or 5G; Or

- a WiFi type communication system according to IEEE 802.11, for example according to IEEE 802.11 n or IEEE 802.11 ac.

[0045] According to another particular example of embodiment, the vehicle 10 carries a receiver of a satellite geolocation system of the GPS type (from the English “Global

30 Positioning System” or in French “World Positioning System”) or the Galileo system for example in communication with a computer of the on-board system of vehicle 10.

[0046] The road environment 1 comprises for example a first portion of road 101 on which the vehicle 10 travels at a current time, the first portion of road 101

5 leading to a set of second road portions 102, 103 and 104. The first road portion 101 thus forms an intersection with each of the second road portions 102, 103, 104.

[0047] A process for controlling an ADAS system of the vehicle 10 is advantageously implemented by the autonomous vehicle 10, for example by one or more processors 10 of one or more computers on-board in the vehicle 10.

[0048] In a first operation, map data of the environment 1 are received, for example via a wireless link from the server 111 via the wireless communication infrastructure and the wireless communication system of the vehicle 10.

[0049] These data are for example received as the vehicle 15 10 moves according to a V2X communication mode (from the English “Vehicle-to-Everything” or in French “Véhicule vers tout”), for example according to an I2V mode (from the English “Infrastructure-to-Vehicle” or in French “Infrastructure towards vehicle”).

[0050] The mapping data received advantageously includes data representative of upcoming intersections within a determined horizon (for example for each intersection included in a distance interval equal to 1000, 1500, 2000 or 2500 m starting from the position of the vehicle 10 at the time of reception of the mapping data).

[0051] The intersection data includes for example for each intersection:

- distance information between the current position of the vehicle 10 (at the time of reception of the mapping data) and the intersection;

- angle information between the first portion of road 101 and the second portion of road 102, 103, 104 leading to the first portion of road or onto which the first portion of road leads to form the intersection;

- identification information (ID) of the type of the second portion of the road. The intersection data are for example received in the form of a table such as table 1 below.

[0053] [Table 1]

[007^ Portion ‘n’ is not shown in Figure 1 and is shown as an example.

[0079] The mapping data including the intersection data are for example received automatically at regular intervals (for example every 100, 200, 500 ms) or upon request from the vehicle 10. The mapping data are for example received after transmission by the vehicle 10 to the server 111 of data 10 of current position of the vehicle 10, for example obtained from the geolocation system on board the vehicle 10.

[0080] The distance between the vehicle 10 and the intersection(s) is for example updated by the vehicle 10 between two consecutive receptions of the intersection data, from the dynamic data of the vehicle 10 (for example the speed which makes it possible to 15 determine the distance traveled by measuring the elapsed time) and/or from the position data of the vehicle 10 obtained from the geolocation system on board the vehicle 10.

[0081] In a second operation, data, called camera data, representative of a part of the road environment located in front of the vehicle 10 according to the direction of movement of the vehicle 10 are received from a camera on board the vehicle 10.

[0082] The camera is for example arranged in the vehicle 10 so as to have in its field of vision the road surface on which the vehicle 10 is traveling. [0083] The on-board camera corresponds for example to a front camera (also called a windshield camera) and/or to a camera of a ground marking detection system.

[0084] In a third operation, second distance information between the position

5 current of the vehicle 10 and each intersection from the camera data received in the second operation.

[0085] The camera data are for example processed according to any method known to those skilled in the art to detect the presence of an intersection between the first road portion 101 and one or more second road portions 102, 103, 104 and 10 the distance between the vehicle 10 and this or these detected intersections.

[0086] An example of such a method is described in document WO2013176268A1 published on November 28, 2013. Document WO2013176268A1 describes an intersection detection system based on image data of the environment acquired by a camera on board in the vehicle, according to markers present in

15 the environment and detected by processing image data. Such a system is further configured to determine the distance between the vehicle and the detected intersection.

[0087] The marker corresponds for example to the marking lines on the ground delimiting the road portions.

[0088] For example, the vehicle 10 has a ground marking detection system. Such a system is for example coupled to the SALC system or integrated into the SALC system. Such a ground marking detection system receives data from one or more cameras on board the vehicle 10 and configured for the acquisition of images of the traffic lane taken by the vehicle 10, for example the portion of road

25 located at the front and/or on the sides of the vehicle 10. The ground marking detection system is thus configured to detect ground markings in the environment of the vehicle 10. Image processing is applied to the images obtained from the camera(s) of the ground marking detection system to determine the presence of lines on the ground and to classify these lines into different

30 categories, for example to determine if lines on the ground correspond to lines edge or center lines for example. An example of image processing to detect lines on the ground is for example described in the document WO2017194890A1.

[0089] The ground marking lines 1001 and 1002 laterally delimiting the first traffic lane 101 are thus detected as well as the ground marking lines 1003 and

5 1004 laterally delimiting the second portion of road 103 and the second portion of road 102 respectively.

[0090] An intersection between two road portions is for example detected by determining whether an intersection between the road marking lines exists.

[0091] For this purpose, each ground marking line 1001, 1002, 1003 and 1004 is for example

10 represented by a polynomial of degree 3 in the form P(x) = Co + Ci * x + C2 * x ² + C3

* x ³ , with Co, Ci, C2 and C3 the coefficients of the polynomial.

[0092] The coefficients Co, Ci, C2 and C3 come from the on-board camera(s) of the vehicle 10 or from the ground marking detection system using images from this or these cameras.

[009f^ The coefficient Co represents for example a distance between the center of the vehicle 10 and each boundary considered. The coefficient Ci represents an angle between the trajectory of the vehicle 10 and a tangent to the traffic lane (the heading). The coefficient C2 represents a radius of curvature and the coefficient C3 represents a derivative of this radius of curvature.

[0094] The intersection between two ground marking lines is then identified from the polynomial representations of each of the ground marking lines and the distance from each detected intersection is obtained.

[0095] According to a variant, only an intersection is searched for, for example following the triggering by the vehicle 10 of indicators on one side indicating that the vehicle 10 25 is going to take the second portion of the road on the side of the indicators.

[0096] According to this variant, only the intersection with the second portion of road located on the side of the activated indicators is searched.

[0097] The first, second and third operations are for example implemented in parallel, concomitantly. [0098] In a fourth operation, for a given intersection or for each intersection, the distance obtained from the intersection data (and represented by the first information) is compared to the distance obtained by processing the camera data (and represented by the second information).

[009^ The result of the comparison corresponds for example to a difference between the two distance values and is for example stored in a memory accessible by the computer(s) implementing the process.

[0100] In a fifth operation, a confidence level associated with the intersection data and/or the mapping data received is determined from the

10 result of the comparison of the fourth operation.

[0101] The confidence level is for example determined by comparing the result of the comparison obtained in the fourth operation with one or more threshold values, which are for example stored as parameters in the memory.

[0102] Thus, the confidence level corresponds for example to:

15 - a first level or minimum confidence level when a difference between the first information and the second information is greater than a first threshold value; the first threshold value is for example equal to 7 m; according to other examples, the first threshold value is equal to 6, 8 or 10 m;

- a second level or intermediate level of confidence when the difference between

20 the first information and the second information is between the first threshold value and a second threshold value, the first threshold value being greater than the second threshold value; the second threshold value is for example equal to 5 m; according to other examples, the second threshold value is equal to 4 or 6 m; And

- a third level or maximum level of confidence when the difference between the

25 first information and the second information is less than the second threshold value.

[0103] Of course, the number of confidence levels is not limited to 3 but is for example equal to 2, 4 or 5 levels, the number of threshold values varying according to the number of levels (1 threshold value for 2 levels , 3 threshold values for 4 levels and 4 values

30 thresholds for 5 levels). [0104] According to a particular and non-limiting embodiment, the confidence level is further determined as a function of a result of the comparison between third pieces of information representative of a type of road environment 1 obtained from data from mapping received in the first operation and fourth information 5 representative of a type of road environment 1 obtained from the camera data.

[0105] The type of road environment 1 corresponds for example to the urban type or the non-urban type.

[0106] Such third information is for example coded on 1 bit in all of the

10 mapping data (for example with the bit at ‘0’ to indicate the urban type and the bit at ‘T’ to indicate the non-urban type).

[0107] The fourth information is for example obtained by analyzing the camera data, that is to say the images obtained from the camera. For example, the urban type is determined when:

15 - a sign indicating entry into a town is detected without detecting the sign indicating exit from the town; and or

- constructions such as houses or buildings are detected in the images received; and or

- street furniture is detected in the images received; and or

20 - urban road infrastructures (for example traffic lights) are detected in the images received.

[0108] For example, the level of confidence obtained as a function of the result of the comparison of the first and second information is increased (or maintained at the highest level) when the third and fourth information indicates the same type of environment (for example urban example).

[0109] Conversely, the level of confidence obtained as a function of the result of the comparison of the first and second information is lowered, for example reduced by one level or maintained at the lowest level, when the third and fourth information indicates types different, that is to say urban and non-urban. [0110] Such a level of confidence makes it possible to evaluate the robustness of the intersection data received.

[0111] Thus, when the confidence level is at its highest level, this means that there is coherence and consistency between the intersection data received with the 5 mapping data and the intersection data obtained. by analysis of the camera data and that the intersection data obtained with the mapping data are reliable, this data can then be used safely as input data by the ADAS system.

[0112] On the contrary, when the confidence level is at its lowest level, this means that there is an inconsistency or inconsistency between the intersection data received with the mapping data and the intersection data obtained. by analysis of the camera data and the intersection data obtained with the mapping data are unreliable or unreliable, this data cannot then be used safely as input data by the ADAS system.

[011f^ Indeed the mapping data received from server 111 (and thus the intersection data) are likely to be erroneous for several reasons, for example:

- following an error or lack of updating of data on the server;

- following a hardware or software fault on the server or in the network infrastructure leading to data loss, transmission delays, etc. ; and or

20 - following a problem with the accuracy of the data and/or the location of the vehicle 10.

[0114] In a sixth operation, the ADAS system is controlled from the intersection data obtained from the mapping data according to the confidence level.

[0115] For example, when the confidence level is greater than a threshold (for example equal to the highest level when there are 3 levels or greater than or equal to level 3 when there are 5 levels), then the ADAS system is controlled based on the intersection data received.

[0116] Otherwise, when the confidence level is lower than the threshold, the ADAS system is for example controlled according to the intersection data obtained from the camera data or the ADAS system is shut down by informing the driver of the vehicle 10 that he must regain control of the vehicle 10 for the functions provided by this ADAS system.

[0117] For example, in the case of a PCC system, the speed and/or acceleration of the vehicle 5 10 are regulated automatically when an intersection is detected, as a function of the distance between the vehicle 10 and the intersection. and/or depending on the angle associated with the intersection. For example, the speed of the vehicle 10 is reduced automatically as the vehicle 10 approaches the intersection, with for example stopping or passing under driver control when the associated angle is greater than 10 threshold.

[0118] Figure 2 schematically illustrates a device 2 configured for controlling one or more ADAS systems of a vehicle, for example the vehicle 10, according to a particular and non-limiting embodiment of the present invention. Device 2

15 corresponds for example to a device on board the vehicle 10, for example a computer.

[0119] The device 2 is for example configured for the implementation of the operations described with regard to Figure 1 and/or the steps of the method described with regard to Figure 3. Examples of such a device 2 include, without be limited to it, on-board electronic equipment such as an on-board computer of a vehicle, an electronic computer such as an ECU (“Electronic Control Unit”), a smartphone, a tablet, a laptop. The elements of device 2, individually or in combination, can be integrated into a single integrated circuit, into several integrated circuits, and/or into discrete components. The device 2 can be produced in the form of electronic circuits or software (or computer) modules or even a combination of electronic circuits and software modules.

[0120] The device 2 comprises one (or more) processor(s) 20 configured to execute instructions for carrying out the steps of the method and/or for the execution of the 30 instructions of the software(s) embedded in the device 2. The device 2 processor 20 can include integrated memory, an input/output interface, and various circuits known to those skilled in the art. The device 2 further comprises at least one memory 21 corresponding for example to a volatile and/or non-volatile memory and/or comprises a memory storage device which may comprise volatile memory and/or

5 non-volatile, such as EEPROM, ROM, PROM, RAM, DRAM, SRAM, flash, magnetic or optical disk.

[0121] The computer code of the embedded software(s) comprising the instructions to be loaded and executed by the processor is for example stored on the memory 21.

[0122] According to different particular and non-limiting examples of embodiment, the device 2 is coupled in communication with other similar devices or systems and/or with communication devices, for example a TCU (from the English "Telematic Control"). Unit” or in French “Telematic Control Unit”), for example via a communications bus or through dedicated input/output ports.

[012f^ According to a particular and non-limiting embodiment, the device 2 comprises a block 22 of interface elements for communicating with external devices, for example a remote server or the “cloud”, other nodes in the network ad hoc. The interface elements of block 22 include one or more of the following interfaces:

20 - RF radio frequency interface, for example of the Wi-Fi® type (according to IEEE 802.11), for example in the 2.4 or 5 GHz frequency bands, or of the Bluetooth® type (according to IEEE 802.15.1), in the frequency band at 2.4 GHz, or Sigfox type using UBN (Ultra Narrow Band) radio technology, or LoRa in the 868 MHz frequency band, LTE (English " Long term

25 Evolution” or in French “Long-term Evolution”), LTE-Advanced (or in French LTE-advanced);

- USB interface (from the English “Universal Serial Bus” or “Bus Universel en Série” in French);

- HDMI interface (from the English “High Definition Multimedia Interface”, or “Interface 30 Multimedia Haute Definition” in French); - LIN interface (from the English “Local Interconnect Network”, or in French “Local Interconnected Network”).

[0124] Data are for example loaded to the device 2 via the interface of the block 22 using a Wi-Fi® network such as according to IEEE 802.11, an ITS G5 network based on

5 IEEE 802.11 p or a mobile network such as a 4G (or 5G) network based on the LTE (Long Term Evolution) standard defined by the 3GPP consortium, in particular an LTE-V2X network.

[0125] According to another particular and non-limiting example of embodiment, the device 2 comprises a communication interface 23 which makes it possible to establish communication with

10 other devices (such as other computers of the on-board system) via a communication channel 230. The communication interface 23 corresponds for example to a transmitter configured to transmit and receive information and/or data via the channel communication interface 230. The communication interface 23 corresponds for example to a CAN type wired network (from the English “Controller Area Network” or in

15 French “Réseau de controllers”), CAN FD (from the English “Controller Area Network Flexible Data-Rate” or in French “Réseau de controllers à rate flexible data”), FlexRay (standardized by the ISO 17458 standard) or Ethernet (standardized by ISO/IEC 802-3).

[0126] According to a particular and non-limiting embodiment, the device 2 can provide

20 output signals to one or more external devices, such as a display screen, touch or not, speaker(s) and/or other peripherals (projection system) via respective output interfaces. According to one variant, one or the other of the external devices is integrated into device 2.

[0127^ Figure 3 illustrates a flowchart of the different stages of a method of controlling one or more ADAS systems of a vehicle, for example the vehicle 10, according to a particular and non-limiting embodiment of the present invention. The method is for example implemented by a device on board the vehicle 10, for example by one or more processors of one or more computers, or by the device 2 of Figure 2. [0128] In a first step 31, mapping data of the road environment are received, the mapping data comprising intersection data representative of an intersection between the first portion of road and a second portion of road, the intersection being located in front of the vehicle in a direction of

5 movement of the vehicle, the intersection data comprising first distance information between a current position of the vehicle and the intersection.

[0129] In a second step 32, camera data representative of a part of the road environment located in front of the vehicle according to the direction of movement of the vehicle are received from a camera on board the vehicle.

[013C] In a third step 33, second distance information between the current position of the vehicle and the intersection is determined from the camera data.

[0131] In a fourth step 34, the first information is compared to the second information.

[0132] In a fifth step 35, a confidence level associated with the intersection data 15 is determined from a result of the comparison of the fourth step.

[0133] In a sixth step 36, the ADAS system is controlled from the intersection data according to the confidence level.

[0134] According to a variant, the variants and examples of the operations described in relation to Figure 1 apply to the steps of the method of Figure 3.

[0135] Of course, the present invention is not limited to the exemplary embodiments described above but extends to a method for controlling the activation state of one or more ADAS systems of a vehicle in operation. of the confidence level and/or a method of controlling a vehicle as a function of the activation state of the ADAS system(s) which would include secondary steps without thereby departing from the scope of the present invention. The same would apply to a device configured for implementing such a process. [0136] The present invention also relates to an ADAS system comprising device 2 of Figure 2.

[0137] The present invention also relates to a vehicle, for example an automobile or more generally an autonomous vehicle with a land engine, comprising the device 2 of the

5 figure 2 or the ADAS system above.

Claims

1. Method for controlling a driving assistance system, called the ADAS system, of a vehicle (10), said vehicle (10) traveling on a first portion of road (101) of a road environment (1), said method comprising the following steps:

- reception (31) of mapping data of said road environment (1), said mapping data comprising intersection data representative of an intersection between said first portion of road (101) and a second portion of road (102, 103 , 104), said intersection being located in front of said vehicle (10) in a direction of movement of said vehicle (10), said intersection data comprising first distance information between a current position of said vehicle (10) and said intersection;

- reception (32) of camera data representative of a part of said road environment (1) located in front of said vehicle (10) in said direction of movement of said vehicle from a camera on board said vehicle (10);

- determination (33) of second distance information between said current position of said vehicle (10) and said intersection from said camera data;

- comparison (34) of said first information and said second information;

- determination (35) of a confidence level associated with said intersection data from a result of said comparison;

- control (36) of said ADAS system from said intersection data according to said confidence level.

2. Method according to claim 1, for which said confidence level corresponds to:

- a minimum confidence level when a difference between said first information and said second information is greater than a first threshold value;

- an intermediate confidence level when said difference between said first information and said second information is between said first threshold value and a second threshold value, said first threshold value being greater than said second threshold value;

- a maximum confidence level when said difference between said first information and said second information is less than said second threshold value.

3. Method according to claim 2, for which said first threshold value is equal to 7 meters and said second threshold value is equal to 5 meters.

4. Method according to one of claims 1 to 3, further comprising the steps of:

- determination of third information representative of a type of said road environment (1) from said mapping data;

- determination of fourth information representative of a type of said road environment (1) from said camera data;

- comparison of said third information and said fourth information, said associated confidence level further being a function of a result of said comparison of said third information and said fourth information.

5. Method according to claim 4, for which said confidence level determined from the result of the comparison between said first information and said second information is increased when the result of the comparison of said third information and said fourth information indicates that said third information and said fourth information is representative of the same type.

6. Method according to one of claims 1 to 5, for which said determination of the second distance information comprises a detection of said intersection from data representative of ground marking lines (1001, 1002, 1003, 1004) associated with said first portion of road (101) and said second portion of road (102, 103, 104), said data representative of ground marking lines being obtained by processing said camera data.

7. Method according to one of claims 1 to 6, for which said ADAS system corresponds to a predictive speed control system, called PCC system.

8. Computer program comprising instructions for implementing the method according to any one of the preceding claims, when these instructions are executed by a processor.

9. Device (2) for controlling a driving assistance system, called the ADAS system, of a vehicle, said device (2) comprising a memory (21) associated with at least one processor (20) configured to the implementation of the steps of the method according to any one of claims 1 to 7.

10. Vehicle (10) comprising the device (2) according to claim 9.