Method and device for controlling a system for determining the position of a vehicle on a road

Abstract

The present invention relates to a method and a device for controlling a system for determining a position of an autonomous vehicle (10) traveling on a first portion of road (1001) of a road environment (1), called a road positioning system. Indeed, the method comprises the transmission of first data representative of first geographical positions of the vehicle and the reception of second data representative of parameters associated with a second portion of road (1002), the parameters being representative of a contribution of at least two methods for determining the position of the vehicle on the road. The second portion of road is determined according to the first data and is located downstream of the first portion of road. The road positioning system is controlled according to the second data, the road positioning system being configured to implement the at least two methods. Figure for abstract: Figure 1

Description

Title of the invention: Method and device for controlling a system for determining the position of a vehicle on a road Technical field

[0001] The present invention relates to methods and devices for controlling a system for determining a position of a vehicle on a road, in particular a motor vehicle. The present invention also relates to a method and a device for controlling a driving assistance system on board a vehicle. The present invention also relates to a method and a device for controlling a vehicle carrying one or more driving assistance systems, in particular an autonomous vehicle. Technological background

[0002] Road safety is one of the major challenges facing our societies. With the increasing number of vehicles on the world's road networks, regardless of traffic conditions, the risk of accidents and incidents caused by traffic conditions has never been greater.

[0003] To improve road safety, some contemporary vehicles are equipped with driver assistance functions or systems, known as AD AS (Advanced Driver-Assistance System). AD AS systems, for example, implement methods 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 received, for example, from a remote server-type device via a wireless communication network, as the vehicle moves.

[0004] Knowledge of this environment is also obtained from data from sensors embedded in the vehicle, for example from cameras, radars and / or lidars. However, depending on the environment of the vehicle, some of these sensors or methods for determining the position of the vehicle in its environment, in particular on a road, are not all equally reliable. Indeed, a camera may encounter difficulties in reading the lines of a road when the road surface is worn or when weather conditions make the surfaces illegible, for example when it is snowing. Similarly, a radar may encounter operating problems affecting the accuracy of its measurements, for example in an environment where there is a lot of electromagnetic interference or echoes due to the presence of obstacles, in particular reflecting waves emitted by the radar, or in difficult weather conditions, for example in the presence of fog.

[0005] Thus, the performance of the different sensors and methods for determining the position of the vehicle on the road are different depending on the road environment in which the vehicle is moving, thus the determined position of a vehicle on the road is more or less precise depending on the method used and the road environment.

[0006] An imprecise determination of a vehicle position can lead to risks of malfunction of an on-board driving assistance system, also called AD AS, with a safety risk for the vehicle and its passengers. Summary of the present invention

[0007] An object of the present invention is to solve at least one of the problems of the technological background described above.

[0008] Another object of the present invention is to improve the operation of a vehicle driving assistance system using data representative of a location of the vehicle on the road.

[0009] Another object of the present invention is to improve the control of an autonomous vehicle.

[0010] According to a first aspect, the present invention relates to a method for controlling a system for determining a position of an autonomous vehicle on a road, called a road positioning system, the autonomous vehicle traveling on a first portion of road in a road environment, the method comprising the following steps: - transmission of first data representative of the first geographical positions of the autonomous vehicle; - reception of second data representative of parameters associated with a second portion of road, the parameters being representative of a contribution from at least two methods of determining the position of the vehicle on the road, the second portion of road being determined based on the first data and located downstream of the first portion of road in a direction of movement of the autonomous vehicle; - controlling the road positioning system based on the second data, the road positioning system being configured to implement the at least two methods.

[0011] According to a variant of the method, the at least two methods belong to a set of methods comprising: - a method of recognizing lines on the ground or road signs using a camera and / or lidar; - a satellite positioning method associated with mapping; and - a simultaneous localization and mapping method, called SLAM.

[0012] According to another variant of the method, the parameters associated with the second portion of road are learned during a learning phase from third data obtained from a set of vehicles traveling on the second portion of road, the third data being representative of an efficiency of each of the at least two methods.

[0013] According to another variant of the method, the parameters associated with a second portion of road are received from a remote device connected in wireless communication with the autonomous vehicle or from a storage device on board the autonomous vehicle.

[0014] According to an additional variant, the method comprises a step of receiving a second geographical position of the next portion of road, the control step being implemented when a third geographical position of the autonomous vehicle corresponds to the second geographical position.

[0015] According to another variant of the method, the second portion of road is determined from mapping data of the road environment.

[0016] According to a second aspect, the present invention relates to a device for controlling a system for determining a position of an autonomous vehicle on a road, the device comprising a memory associated with a processor configured for implementing the steps of the method according to the first aspect of the present invention.

[0017] According to a third aspect, the present invention relates to an autonomous vehicle, for example of the automobile type, comprising a device as described above according to the second aspect of the present invention.

[0018] According to a fourth aspect, the present invention relates to a computer program which comprises instructions adapted for executing the steps of the method according to the first aspect of the present invention, in particular when the computer program is executed by at least one processor.

[0019] Such a computer program may use any programming language, and be in the form of source code, object code, or intermediate code between source code and object code, such as in a partially compiled form, or in any other desirable form.

[0020] 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 carrying out the steps of the method according to the first aspect of the present invention.

[0021] On the one hand, the recording medium may be any entity or device capable of storing the program. For example, the medium may comprise a means of storage, such as ROM, CD-ROM, or microelectronic circuit-type ROM, or magnetic recording media or a hard disk.

[0022] Furthermore, this recording medium may 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 hertzian radio or by self-directed laser beam or by other means. The computer program according to the present invention may in particular be downloaded from an Internet-type network.

[0023] Alternatively, the recording medium may be an integrated circuit in which the computer program is incorporated, the integrated circuit being adapted to perform or to be used in performing the method in question. Brief description of the figures

[0024] Other characteristics and advantages of the present invention will emerge from the description of the particular and non-limiting exemplary embodiments of the present invention below, with reference to the appended figures 1 to 3, in which:

[0025] [Fig.l] schematically illustrates a road environment 1 in which an autonomous vehicle operates, according to a particular and non-limiting exemplary embodiment of the present invention;

[0026] [Fig.2] schematically illustrates a device configured to control a system for determining a position of the autonomous vehicle of [Fig.l] on a road, according to a particular and non-limiting exemplary embodiment of the present invention;

[0027] [Fig.3] illustrates a flowchart of the different steps of a method for controlling a system for determining a position of the autonomous vehicle of [Fig.l] on a road, according to a particular and non-limiting exemplary embodiment of the present invention. Description of examples of implementation

[0028] A method and a device for controlling a system for determining a position of an autonomous vehicle on a road will now be described in what follows with joint reference to Figures 1 to 3. The same elements are identified with the same reference signs throughout the description which follows.

[0029] The terms "first(s)", "second(s)" (or "first(s)", "second(s)"), etc. are used in this document by arbitrary convention to enable different elements (such as operations, means, etc.) implemented in the embodiments described below to be identified and distinguished. Such elements may be distinct or correspond to a single element, depending on the embodiment.

[0030] According to a particular and non-limiting example of embodiment of the present invention, the control of a system for determining the position of an autonomous vehicle on a road, called a road positioning system, for example by a computer of the autonomous vehicle, the autonomous vehicle traveling on a first portion of road in a road environment.

[0031] Indeed, the method comprises the transmission of first data representative of first geographical positions of the vehicle and the reception of second data representative of parameters associated with a second portion of road, the parameters being representative of a contribution of at least two methods for determining the position of the vehicle on the road. The second portion of road is determined as a function of the first data and is located downstream of the first portion of road.

[0032] The road positioning system is then controlled based on the second data, the road positioning system being configured to implement the at least two methods.

[0033] Thus, the road positioning system has parameters allowing its performance to be improved by using these parameters to define a weight or a contribution attributed to each method of determining the position of the vehicle on the road. The precision of this system is then optimal when the autonomous vehicle is traveling on the second portion of road.

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

[0035] The autonomous vehicle 10 corresponds for example to a motor vehicle moving in a road environment 1 comprising several roads each comprising at least one portion of road.

[0036] The autonomous vehicle 10 corresponds for example to a vehicle with a thermal engine, with electric motor(s) or even a hybrid vehicle with a thermal engine and one or more electric motors. The autonomous vehicle 10 thus corresponds for example to a coach, a bus, a truck, a utility vehicle or a motorcycle, that is to say to a vehicle of the motorized land vehicle type.

[0037] The autonomous vehicle 10 corresponds for example to a vehicle traveling 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 having no autonomy, the driving of which is under the total supervision of the driver, level 1 corresponding to a vehicle with a minimal level of autonomy, the driving of which is under the supervision of the driver with minimal assistance from an AD AS system, and level 5 corresponding to a completely autonomous vehicle.

[0038] The 5 levels of autonomy of the classification of the federal agency responsible for road safety are: - level 0: no automation, the vehicle driver has full control over the main functions of the vehicle (engine, accelerator, steering, brakes); - level 1: driver assistance, automation is active for certain vehicle functions, the driver retaining overall control over the vehicle's driving; cruise control is part of this level, as are other aids such as ABS (anti-lock braking system) or ESP (electro-stabilizer programmed); - level 2: automation of combined functions, the control of at least two main functions is combined in the automation to replace the driver in certain situations; for example, adaptive cruise control combined with lane centering allows a vehicle to be classified as level 2, as does automatic parking assistance (from the English “Park assist”); - level 3: limited autonomous driving, the driver can hand over complete control of the vehicle to the automated system which will then be responsible for critical safety functions; autonomous driving can however only take place in certain specific environmental and traffic conditions (only on motorways for example); - level 4: fully autonomous driving under certain conditions, the vehicle is designed to ensure all critical safety functions on its own over a complete journey; the driver provides a destination or navigation instructions but is not required to make himself available to take back control of the vehicle; - level 5: completely autonomous driving without driver assistance in all circumstances.

[0039] The autonomous vehicle 10 incorporates one or more driving assistance systems, called AD AS (from the English “Advanced Driver-Assistance System” or in French “Advanced Driving Assistance System”). Such AD AS systems are configured to assist, or even replace, the driver of the autonomous vehicle 10 to control the autonomous vehicle 10 on its route.

[0040] The autonomous vehicle 10 for example incorporates one or more of the following AD AS systems for this purpose: - adaptive cruise control system, known as ACC (from the English “Adaptive Cruise Control”), - predictive cruise control, known as the P-ACC system (from the English "Predictive-Adaptive Cruise Control"), - intelligent speed adaptation system, known as ISA system (from English “Intelligent Speed ​​Adaptation”), - curve speed adaptation system, known as CSA (Curve Speed ​​Assist) system, - electronic stability control system, known as ESC (Electronic Stability Control), DSC (Dynamic Stability Control) or ESP (Electronic Stability Program), - lane keeping assistance system, known as LKA (Lane-Keeping Assist) or LPA (Lane Positioning Assist), - semi-automatic lane change system, known as SALC (Semi Automatic Lane Change), and / or - object detection system, called ODS system (from the English “Object Detection System”).

[0041] The examples of AD AS systems in the list above are provided for illustrative purposes and are not limiting, this list not being exhaustive.

[0042] The AD AS systems embedded in the autonomous vehicle 10 are for example supplied with data obtained from one or more embedded sensors, such as for example radars, LIDARs and / or cameras, and / or data received from a communication infrastructure.

[0043] According to a particular exemplary embodiment, the autonomous vehicle 10 carries a communication system configured to communicate with one or more remote devices 120 via an infrastructure of a wireless communication network. The remote device 120 corresponds for example to a server of the “cloud” 100 (or “cloud” in French). The wireless communication infrastructure comprises for example a set of communication devices 110 of the cellular network antenna type of the LTE 4G or 5G type or of the UBR (Roadside Unit) type.

[0044] The communication system of the autonomous vehicle 10 comprises, for example, one or more communication antennas connected to a telematic control unit, called TCU (from the English “Telematic Control Unit”), itself connected to one or more computers of the on-board system of the autonomous vehicle 10, in particular one or more computers in charge of controlling the AD AS systems of the autonomous vehicle 10. The antenna(s), the TCU unit and the computer(s) form, for example, a multiplexed architecture for the realization of different services useful for the proper functioning of the vehicle and for assisting the driver and / or the passengers of the vehicle in the control of the autonomous vehicle 10. The computer(s) and the TCU unit communicate and exchange data between them via of one or more computer buses, for example a communication bus of the data bus type CAN (from the English "Controller Area Network" or in French "Network of controllers"), CAN FD (from the English "Controller Area Network Flexible Data-Rate" or in French "Flexible Data Rate Controller Network"), FlexRay (according to the ISO 17458 standard) or Ethernet (according to the ISO / IEC 802-3 standard).

[0045] The wireless communication system allowing the exchange of data between the autonomous vehicle 10 and the remote device(s) 120 corresponds for example to: - a vehicle-to-infrastructure V2I communication system (from the English “vehicle-to-infrastructure”), for example based on the 3GPP LTE-V or IEEE 802.1 Ip standards of ITS G5; or - a cellular network type communication system, for example an LTE (Long-Term Evolution), LTE-Advanced, LTE 4G or 5G type network; or - a Wi-Fi type communication system according to IEEE 802.11, for example according to IEEE 802.1 In or IEEE 802.1 lac.

[0046] The autonomous vehicle 10 also carries a receiver of a satellite geolocation system of the GPS type (from the English “Global Positioning System” or in French “Système mondial de positioning”) or the Galileo system for example in communication with a computer of the on-board system of the autonomous vehicle 10. The satellite geolocation system is, for example, associated with a map.

[0047] The mapping comprises, for example, a road map in which the roads constituting the road network on which the autonomous vehicle 10 travels are located and referenced. This road map comprises different sections or different portions of roads and connections between these different portions called intersections, as well as, for example, topological, geometric, regulatory information such as speed limits or priority and event regimes.

[0048] The road environment 1 comprises for example a first portion of road 1001 on which the autonomous vehicle 10 is traveling at a current time, the first portion of road 1001 leading to at least a second portion of road 1002, for example via an intersection or the second portion of road 1002 being distinct from the first portion of road 1001 in particular following a change in the type of road environment, external conditions or type of road. According to a first example, the second portion of road 1002 corresponds to a road in a built-up area while the first portion of road 1001 corresponds to a road in a rural environment. According to a second example, the second portion of road 1002 corresponds to a tunnel while the first portion of road 1001 corresponds to a clear road. According to a third example, the second portion of road 1002 corresponds to a motorway while the first portion of road 1001 corresponds to a country road. According to a fourth example, the second portion of road 1002 corresponds to a portion of road referenced in a first particular category in a map while the first portion of road 1001 corresponds to a portion of road referenced in a second particular category in this map.

[0049] The autonomous vehicle 10 traveling on the first portion of road 1001 can thus take the second portion of road 1002 at a later time instant, the autonomous vehicle 10 being located for example at a determined distance from the second portion of road 1002, for example 10m, 100m, 2km or more.

[0050] The autonomous vehicle 10 incorporates a system for determining a position of the autonomous vehicle 10 on a road, called a road positioning system. This system identifies, for example, the traffic lane on which the autonomous vehicle 10 is traveling, in particular when the road comprises several traffic lanes, as well as the relative position of the autonomous vehicle 10 in this traffic lane. For example, the relative position of the autonomous vehicle 10 is determined relative to a central axis of the traffic lane, relative to the position of at least one limit of the traffic lane, for example defined by one or more lines on the ground, by a border or by a road surface limit.Thus, the road positioning system is able to determine the relative position of the autonomous vehicle 10 with respect to the road and to a traffic lane, this position being for example communicated to computers of the various AD AS previously presented.

[0051] This position is notably determined using different sensors and different methods, the road positioning system implementing at least two methods belonging to a set of methods comprising: - a method of recognizing lines on the ground or road signs using a camera and / or lidar; - a satellite positioning method associated with mapping; and - a simultaneous location and mapping method, known as SLAM.

[0052] These methods notably use different technologies such as: - a GPS (from the English "Gobai Positioning System") which uses signals from satellites orbiting the Earth, the position of the vehicle being determined with relatively high accuracy. However, in dense urban environments or in areas where the GPS signal is obstructed, for example, under bridges or near tall buildings, GPS may be less reliable; - a LiDAR (Light Detection and Ranging) which uses sensors emitting laser beams to measure the distance between the vehicle and surrounding objects. By analyzing the reflections of the beams, the vehicle's positioning system can map its environment in three dimensions and determine its position based on this data. LiDAR sensors can help with precise location, even in varied and changing environments; - a camera installed on the autonomous vehicle 10, for example at the top of its windshield or on a grille, used for visual perception and which can be used for localization by identifying and locating characteristics of the environment. A computer vision algorithm makes it possible to analyze the images acquired by the camera to recognize specific elements such as road signs, lines or markings on the ground of the road, traffic lights, thus helping to determine the position of the vehicle; - an inertial or motion sensor, measuring acceleration, angular velocity and sometimes magnetism to track the movements of the autonomous vehicle 10, capable of estimating the position of the vehicle by calculating its movements and correlating this data with other location information; - High Definition mapping, corresponding to a detailed and precise map of the road environment 1 of the autonomous vehicle 10 in which it is located. These maps contain specific information on roads, speed limits or intersections, the road positioning system of the autonomous vehicle 10 using this map to compare what it perceives with the cartographic data and determine the position of the autonomous vehicle 10 on the road.

[0053] The different location data obtained from the different technologies are for example merged in order to improve the accuracy of the position determined by the road positioning system. This system uses for example a technique called sensor fusion, where data from several sensors (GPS, LiDAR, cameras, inertial sensors, etc.) are combined and analyzed by algorithms to estimate and improve the accuracy of the vehicle's position.

[0054] By combining these different methods and using sophisticated data processing algorithms, autonomous vehicles are able to determine their position with high accuracy and reliability, which is essential for safe and efficient autonomous driving.

[0055] It should be noted that the different technologies or methods implemented by the road positioning system of the autonomous vehicle 10 are more or less efficient depending on the road environment in which the autonomous vehicle 10 is moving. Thus, a first method for determining the position of the vehicle on the road is more efficient than a second method for determining the position of the vehicle on the road in a first road environment, while the second method for determining the position of the vehicle on the road is more efficient than the first method for determining the position of the vehicle on the road in a second road environment.

[0056] Thus, parameters are associated with the different methods, these parameters allowing a weight or contribution to be assigned to each of the methods for determining the position of the autonomous vehicle 10 on the road in order to favor, for example, the most precise method or the fastest method.

[0057] A process for controlling a system for determining a position of the autonomous vehicle 10 on a road, called a road positioning system, is implemented, for example, by one or more processors of one or more computers embedded in the autonomous vehicle 10.

[0058] In a first operation, first data representative of first geographical positions of the autonomous vehicle 10 are transmitted, for example to a remote device 120 or to a computer on board the autonomous vehicle 10.

[0059] These geographical positions correspond, for example, to the current position of the autonomous vehicle 10 moving on the first portion of road 1001 and to a previous geographical position of the autonomous vehicle 10. From these two positions it is then possible to determine the current geographical position of the autonomous vehicle 10 and its direction of movement.

[0060] The remote device 120 or the computer then determines a second portion of road 1002 based on the first data. The second portion of road 1002 is in particular located downstream of the first portion of road 1001 in a direction of travel of the autonomous vehicle 10. The first data makes it possible, for example, to locate the autonomous vehicle 10 on a map and to identify the first portion of road 1001 on which it is traveling. Based on the previous geographical positions of the autonomous vehicle 10, a direction of travel on this first portion of road 1001 is then determined. At least one second portion of road 1002 is then identified as being a portion of road that the autonomous vehicle 10 will most likely take, for example within a given duration or at a given distance from the autonomous vehicle 10. Thus, according to this example, the second portion of road 1002 is determined from mapping data of the road environment 1..

[0061] According to a variant, the second portion of road 1002 is not determined from a map but from a database or correspondence tables, a second portion of road 1002 being associated with the first portion of road 1001 and with the direction of travel of the autonomous vehicle 10.

[0062] In a second operation, second data representative of parameters associated with the second portion of road 1002 are received. The parameters are representative of a contribution of at least two methods for determining the position of the vehicle on the road, i.e. of respective weights or contributions of different methods for determining the position of the autonomous vehicle 10 on the road or on the traffic lane.

[0063] These parameters are for example associated with a map comprising the second portion of road 1002 and are received, for example via a wireless link from the remote device 120 via the wireless communication infrastructure and the wireless communication system of the autonomous vehicle 10.

[0064] These data are for example received according to a V2X communication mode (from the English “Vehicle-to-Everything”), for example according to an I2V mode (from the English “Infrastructure-to-Vehicle”).

[0065] According to another particular exemplary embodiment, the parameters associated with a second portion of road 1002 are received from a storage device on board the autonomous vehicle 10, a map having for example been downloaded into this on-board storage device, for example downloaded at a time prior to the start of the autonomous vehicle 10 via a wireless link as previously described.

[0066] According to a particular embodiment, the parameters associated with the second portion of road 1002 and integrated into the mapping or into a database are learned during a learning phase from third data obtained from a set of vehicles traveling on the second portion of road 1002, the third data being representative of an efficiency of each of the at least two methods. In other words, the vehicles of the set of vehicles contributed to an experiment plan by traveling on the second portion of road 1002. During these vehicle circulation phases, these circulation phases being associated with a phase of learning optimal parameters, the performance of the different methods for determining the position of each vehicle on the road was monitored.That is to say that a performance index, associated with the reliability and / or the speed of determining the position of the vehicle on the road, has for example been attributed to the different methods of determining the position of the vehicle on the road. Parameters have then been determined from these performance indices so as to favor the most efficient or effective methods. Thus, these parameters define the weight attributed to each of the methods of determining the position of a vehicle on the road associated with the second portion of road 1002 so as to take more consideration of the most efficient methods on this portion of road 1002.

[0067] Thus, the parameters received by the calculator of the road positioning system make it possible to define the contribution of each of the methods implemented by this system to determine the position of the autonomous vehicle 10 on the second portion of road 1002, thus improving the performance of this system.

[0068] Optionally, in a third operation, a second geographic position of the next portion of road 1002 is received. It corresponds for example to the geographical position of the point of passage of the first vehicle from the first portion of road 1001 to the second portion of road 1002 if these two portions of road 1001, 1002 are adjacent or consecutive. According to a variant, the second geographical position corresponds to a position upstream of the second portion of road 1002 corresponding to the position at which the computer integrates the received parameters, the road positioning system having for example an update delay.

[0069] In a fourth operation, the road positioning system is controlled as a function of the second data, i.e. as a function of the received parameters. Thus, the computer in charge of the process takes into consideration the received parameters which it applies for determining the position of the autonomous vehicle 10 on the road.

[0070] These parameters thus make it possible to improve the performance of the road positioning system, favoring one or more methods of determining the position of the autonomous vehicle 10 on the road previously qualified as efficient.

[0071] According to a particular exemplary embodiment, the fourth operation is implemented when a third geographical position of the autonomous vehicle 10 corresponds to the second geographical position. The correspondence of these two geographical positions is for example determined when the distance separating the second geographical position from the third geographical position is less than a threshold distance, for example 1m, 10m or 100m.

[0072] According to another particular exemplary embodiment, the fourth operation is implemented when a duration has elapsed since the reception of the second data. This duration is for example determined from a speed of the vehicle and a distance separating the first geographical position from the second geographical position.

[0073] According to yet another particular exemplary embodiment, the fourth operation is implemented upon receipt of the second data, for example if the autonomous vehicle 10 is at a distance from the second portion of road 1002 less than the threshold distance at the time of receipt of the second data, or if the portions of road correspond to very small portions of roads regularly distributed geographically, for example if each portion of road has a length of between 10m and 1km and the road on which the autonomous vehicle 10 is traveling comprises portions of contiguous roads.

[0074] Thus, the process described above makes it possible to improve the performance of a road positioning system embedded in the autonomous vehicle 10, driving assistance systems embedded in this autonomous vehicle then receive reliable and precise input data. The efficiency of these AD AS is then guaranteed, thus improving the safety of the occupants of the autonomous vehicle 10.

[0075] [Fig.2] schematically illustrates a device 2 configured to control a system for determining a position of an autonomous vehicle on a road, for example the autonomous vehicle 10, according to a particular and non-limiting exemplary embodiment of the present invention. The device 2 corresponds for example to a device embedded in the autonomous vehicle 10, for example a computer.

[0076] The device 2 is for example configured for the implementation of the operations described with regard to [Fig.l] and / or the steps of the method 3 described with regard to [Fig.3]. Examples of such a device 2 include, but are not limited to, on-board electronic equipment such as an on-board computer of a vehicle, an electronic calculator such as an ECU (“Electronic Control Unit”), a smartphone, a tablet, a laptop. The elements of the 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.

[0077] The device 2 comprises one (or more) processor(s) 20 configured to execute instructions for carrying out the steps of the process of [Fig.l] and / or the method 3 of [Fig.3] and / or for executing the instructions of the software(s) embedded in the device 2. The processor 20 may 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 and / or non-volatile memory, such as EEPROM, ROM, PROM, RAM, DRAM, SRAM, flash, magnetic or optical disk.

[0078] The computer code of the embedded software(s) comprising the instructions to be loaded and executed by the processor is for example stored in the memory 21.

[0079] According to various particular and non-limiting embodiments, 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 communication bus or through dedicated input / output ports.

[0080] According to a particular and non-limiting exemplary 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 of the ad hoc network. The interface elements of the block 22 comprise one or more of the following interfaces: - RF radio frequency interface, for example Wi-Fi® type (according to IEEE 802.11), for example in the 2.4 or 5 GHz frequency bands, or Bluetooth® type (according to IEEE 802.15.1), in the 2.4 GHz frequency band, or Sigfox type using UBN (Ultra Narrow Band) radio technology, or LoRa in the 868 MHz frequency band, LTE (Long-Term Evolution), LTE-Advanced; - USB interface (from the English “Universal Serial Bus” or “Universal Serial Bus” in French); - HDMI interface (from the English “High Definition Multimedia Interface” or “High Definition Multimedia Interface” in French); - LIN interface (from the English “Local Interconnect Network”).

[0081] Data is 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 IEEE 802.1 Ip 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.

[0082] According to another particular and non-limiting exemplary embodiment, the device 2 comprises a communication interface 23 which makes it possible to establish communication with 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 communication channel 230. The communication interface 23 corresponds for example to a wired network of the CAN (from the English "Controller Area Network" or in French "Réseau de contrôles") type, CAN FD (from the English "Controller Area Network Flexible Data-Rate" or in French "Réseau de contrôles à débit de données flexible"), FlexRay (standardized by the ISO 17458 standard) or Ethernet (standardized by the ISO / IEC 802-3 standard).

[0083] According to a particular and non-limiting exemplary embodiment, the device 2 can provide output signals to one or more external devices, such as a display screen, touch-sensitive or not, one or more speakers and / or other peripherals (projection system) via respective output interfaces. According to a variant, one or other of the external devices is integrated into the device 2.

[0084] [Fig. 3] illustrates a flowchart of the different steps of a method 3 for controlling a system for determining a position of an autonomous vehicle on a road, for example of the autonomous vehicle 10, according to a particular and non-limiting exemplary embodiment of the present invention. The method is for example implemented by a device embedded in the autonomous vehicle 10 or by the device 2 of [Fig.2].

[0085] In a first step 31, first data representative of first geographical positions of the autonomous vehicle 10 are transmitted.

[0086] In a second step 32, second data representative of parameters associated with a second portion of road 1002 are received. The parameters are representative of a contribution from at least two methods for determining the position of the vehicle on the road. The second portion of road 1002 is determined as a function of the first data and located downstream of the first portion of road 1001 in a direction of movement of the autonomous vehicle 10.

[0087] In a third step 33, the road positioning system is controlled according to the second data, the road positioning system being configured to implement the at least two methods.

[0088] According to a variant, the variants and examples of the operations described in relation to [Fig.l] apply to the steps of the method of [Fig.3].

[0089] Of course, the present invention is not limited to the exemplary embodiments described above but extends to a method for controlling a driving assistance system on board a vehicle using location data of the vehicle on a road 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 method.

[0090] The present invention also relates to an autonomous vehicle, for example an automobile or more generally an autonomous vehicle with a land motor, comprising the device 2 of [Fig.2].

Claims

Claims

1. Method for controlling a system for determining a position of an autonomous vehicle (10) on a road, called a road positioning system, said autonomous vehicle (10) traveling on a first portion of road (1001) of a road environment (1), said method comprising the following steps: - transmission (31) of first data representative of first geographical positions of the autonomous vehicle (10); - reception (32) of second data representative of parameters associated with a second portion of road (1002), said parameters being representative of a contribution of at least two methods for determining the position of the vehicle on the road, said second portion of road (1002) being determined as a function of said first data and located downstream of the first portion of road (1001) in a direction of movement of the autonomous vehicle (10);- control (33) of the road positioning system as a function of said second data, said road positioning system being configured to implement said at least two methods.;

2. Method according to claim 1, for which said at least two methods belong to a set of methods comprising: - a method of recognizing lines on the ground or road signs by a camera and / or a lidar; - a method of positioning by satellites associated with mapping; and - a method of simultaneous localization and mapping, called SLAM.

3. Method according to claim 1 or 2, for which said parameters associated with the second portion of road (1002) are learned during a learning phase from third data obtained from a set of vehicles traveling on the second portion of road (1002), said third data being representative of an efficiency of each of the at least two methods.

4. Method according to one of claims 1 to 3, for which said parameters associated with a second portion of road (1002) are received from a remote device (120) connected in wireless communication with the autonomous vehicle (10) or from a storage device on board the autonomous vehicle (10).

5. Method according to one of claims 1 to 4, comprising a step of receiving a second geographic position of said next portion of road (1002), the control step (33) being implemented when a third geographic position of the autonomous vehicle (10) corresponds to said second geographic position.

6. Method according to one of claims 1 to 5, for which said second portion of road (1002) is determined from mapping data of said road environment (1).

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

8. A computer-readable recording medium on which is recorded a computer program comprising instructions for carrying out the steps of the method according to one of claims 1 to 6.

9. Device (2) for controlling a system for determining a position of a vehicle on a road, said device (2) comprising a memory (21) associated with at least one processor (20) configured for implementing the steps of the method according to any one of claims 1 to 6

10. 1 d U. Autonomous vehicle (10) comprising the device (2) according to claim 9.

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