FR3107114A1 - Method and device for validating mapping data of a vehicle road environment - Google Patents

Abstract

The invention relates to a method and a device a method for validating mapping data of a road environment (1) of a first vehicle (11). To this end, map data is obtained from a navigation system and includes first information representative of direction and/or direction associated with the traffic lanes (101 to 103) of the road environment (1). A set of data associated with a detection of at least one second vehicle (12, 13) in the road environment (1) is obtained from one or more sensors of the first vehicle (11). Second information representative of the trajectory of the at least one second vehicle (12, 13) is determined from the set of data and used to obtain third information representative of direction and/or direction associated with the at least one lane of circulation (101 to 103). The third information is used to validate or invalidate the accuracy of the first information. Figure for abstract: Figure 1

Description

Method and device for validating mapping data of a vehicle road environment

The invention relates to methods and devices for validating vehicle road environment mapping data, in particular automotive and for example for an autonomous vehicle. The invention also relates to a method and a device for determining the direction and/or the direction associated with one or more traffic lanes in the environment of a vehicle.

Technology background

To plan a journey to be made with a vehicle, it is known to use a navigation system, such a system being for example embedded in the vehicle or implemented in a mobile device, for example a mobile telephone, itself embedded in the vehicle. Such a system uses map data such as road maps indicating the various traffic lanes that the vehicle can take to go from point A to point B, with in particular the direction of traffic and/or the direction associated with each of these pathways.

This data is for example used by certain functions or driving assistance systems, known as ADAS (from the English "Advanced Driver-Assistance System" or in French "Advanced Driving Assistance System"), to guide or control the vehicle in its environment to reach its destination. The most advanced driving assistance systems provide control of the vehicle, which becomes a so-called autonomous vehicle, i.e. a vehicle capable of driving in the road environment without driver intervention.

However, the mapping data is not always up to date or there is uncertainty as to the accuracy of this data, which is particularly problematic when a vehicle relies on this data to circulate in its environment.

An object of the present invention is to make the mapping data of a road environment of a vehicle more reliable.

Another object of the present invention is to improve road safety.

According to a first aspect, the invention relates to a method for validating mapping data of a road environment of a first vehicle, the data being obtained from a navigation system and comprising first information representative of direction and/or direction associated with at least one traffic lane of the road environment, the method being implemented by the first vehicle, the method comprising the following steps:

- reception of a set of data associated with the detection of at least one second vehicle in the road environment;

- determining second information representative of the trajectory of the at least one second vehicle from the set of data;

- determination of third information representative of direction and/or direction associated with the at least one traffic lane from the second information;

- validation of the first information from the third information.

Alternatively, the data set includes:

- first data representative of actuation of at least one indicator by the at least one second vehicle; and or

- second data representative of a movement of the at least one second vehicle in the road environment.

According to another variant, the method further comprises the steps of:

- detection of at least one traffic lane;

- association between the at least one second vehicle and the at least one traffic lane as a function of third positioning data of the at least one second vehicle, the third data belonging to the set of data.

According to an additional variant, the first information comprises a type of traffic lane associated with the at least one traffic lane, the type of traffic lane belonging to a set of types of traffic lane comprising:

- right lane according to the direction of travel of the first vehicle;

- left lane according to the direction of travel of the first vehicle;

- left-turn lane; And

- right-turn lane.

According to yet another variant, the method further comprises a step of trajectory control of the first vehicle according to a result of the validation.

According to an additional variant, the first vehicle is an autonomous vehicle.

According to an additional variant, the road environment corresponds to an intersection between several traffic lanes.

According to a second aspect, the invention relates to a device for validating map data of a road environment of a first vehicle, the device comprising a memory associated with a processor configured for the implementation of the steps of the method according to the first aspect of the invention.

According to a third aspect, the invention relates to a vehicle, for example of the automotive type, comprising a device as described above according to the second aspect of the invention.

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

Such a computer program can 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.

According to a fifth aspect, the invention relates to a computer-readable recording medium on which is recorded a computer program comprising instructions for the execution of the steps of the method according to the first aspect of the invention.

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.

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 hertzian radio or by self-directed laser beam or by other ways. The computer program according to the invention can in particular be downloaded from an Internet-type network.

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 figures

Other characteristics and advantages of the invention will emerge from the description of the non-limiting embodiments of the invention below, with reference to the appended figures 1 to 4, in which:

schematically illustrates a road environment for a vehicle, according to a particular embodiment of the present invention;

schematically illustrates a process for validating road environment mapping data of FIG. 1, according to a particular embodiment of the present invention;

schematically illustrates a device configured to validate map data of the road environment of FIG. 1, according to a particular embodiment of the present invention;

illustrates a flowchart of the different steps of a method for validating road environment mapping data of FIG. 1, according to a particular embodiment of the present invention.

A method and a device for validating map data of the road environment of a vehicle will now be described in what will follow with reference jointly to FIGS. 1 to 4. The same elements are identified with the same reference signs throughout the following description.

According to a particular and non-limiting embodiment of the invention, a method for validating mapping data of a road environment of a first vehicle, for which the mapping data is obtained from a navigation system and comprises first information representative of direction and/or direction associated with the traffic lanes of the road environment, comprises the reception, for example by a computer of the first vehicle, of a set of data associated with a detection of at least a second vehicle in the road environment, this set of data being obtained from one or more object detection sensors on board the first vehicle. Second information representing or indicating the trajectory of the at least one second vehicle is determined from the set of data. Third information representing direction and/or direction associated with the at least one traffic lane is determined from the second information. Finally, the third information is used to validate or invalidate the accuracy of the first information, for example by comparing the second information to the first information.

The determination of trajectory information of one or more second vehicles located in the environment of the first vehicle on the basis of data resulting from the detection of the second vehicles by the first vehicle makes it possible to reliably determine the direction and/or the traffic direction associated with the traffic lanes on which the second vehicles are located. This information is particularly useful for validating the information received from a navigation system on the direction and/or direction of travel of the traffic lanes of the road environment of the first vehicle, thus making it possible to correct the information from the navigation system if necessary. navigation. In the end, the information relating to the traffic lanes of the environment of the first vehicle is more reliable, also improving the safety of vehicles operating in this road environment.

schematically illustrates a road environment 1 for a vehicle, according to a particular and non-limiting embodiment of the present invention.

FIG. 1 illustrates a road environment 1 comprising for example a portion of road with several traffic lanes 101, 102, 103, for example a first traffic lane 101 according to a first traffic direction, a second traffic lane 102 of the turns type -to the right in the same direction of traffic as the first traffic lane 101 and a third traffic lane 103 of the left lane type whose traffic direction is opposite to the traffic direction of the first traffic lane. The direction associated with the first traffic lane 101 is for example "straight on" and is materialized by an arrow painted on the first traffic lane indicating that this lane is dedicated to vehicles wanting to go straight ahead. The direction associated with the second traffic lane 102 is for example "to the right" and is materialized by an arrow painted on the first traffic lane indicating that this lane is dedicated to vehicles wanting to go right at the crossroads. According to the particular example of figure 1, the road environment 1 corresponds to an intersection (or a crossroads) between several roads or several traffic lanes.

Of course, the road environment 1 is not limited to the example above but extends to any type of road environment, for example a fast lane of the motorway type, an urban road environment, a road environment comprising roads with a single traffic lane in each direction of traffic.

The information relating to the number of traffic lanes, to the direction of traffic associated with each lane and/or to the direction associated with each lane 101 to 103, called first information, advantageously forms part of a set of mapping data comprising data road map(s) indicating the roads of the road environment 1, these data being obtained through a cartography system or a navigation system.

The mapping data is for example received from a mapping system. The mapping data is for example obtained from a navigation application based on a satellite navigation system, for example of the Galileo or GPS type (from the English “Global Positioning System”, or “Global Positioning System” in French). The mapping data is for example stored in the memory of the navigation system, the geographical position of the system being obtained for example via the satellite positioning system, for example GPS. According to another example, the map data is received from a remote storage space (for example from the “cloud”) via a wireless connection used in a mobile network such as a 4G network (or LTE Advanced according to 3GPP release 10 – version 10) or 5G. According to this variant, the map data is loaded and for example displayed on a screen of the navigation system as the vehicle carrying the navigation system moves.

The road environment 1 corresponds for example to the road environment of a first vehicle 11 traveling on the first traffic lane 101 according to the direction of traffic associated with this first traffic lane 101.

The first vehicle 11 advantageously embeds a navigation system configured for the management and/or display of map data. The navigation system corresponds for example to a system embedded in the first vehicle 11 or to a system installed on a communication device such as a smart telephone (from the English “smartphone”) connected to the first vehicle 11 via a wireless link. wire (for example of the Bluetooth® or Wifi® type) or wired (for example of the USB type (from the English “Universal Serial Bus” or in French “Universal Serial Bus”)).

The first vehicle 11 advantageously embeds a set of sensors configured to obtain data on the environment of the first vehicle 11. The first vehicle 11 embeds for example The first vehicle 11 advantageously embeds one or more sensors of one or more detection systems of object in the environment of the first vehicle 11, the data obtained from this or these sensors making it possible, for example, to determine the position, the speed and/or the trajectory of objects detected in the environment 1 of the first vehicle 11, these objects corresponding for example to one or more second vehicles 12, 13. This or these object detection systems are for example associated with, or included in, one or more driving assistance systems, called ADAS system(s) ( English “Advanced Driver-Assistance System” or French “Advanced Driving Assistance System”).

The sensor(s) associated with these object detection systems correspond, for example, to one or more of the following sensors:

- one or more millimeter wave radars arranged on the vehicle, e.g. front, rear, each front/rear corner of the vehicle; each radar is adapted to emit electromagnetic waves and to receive the echoes of these waves sent back by one or more objects, with the aim of detecting obstacles and their distances vis-à-vis the vehicle; and or

- one or more LIDAR(s) (from the English "Light Detection And Ranging", or "Detection and estimation of the distance by light" in French), a LIDAR sensor corresponding to an optoelectronic system composed of a transmitter device laser, a receiver device comprising a light collector (to collect the part of the light radiation emitted by the emitter and reflected by any object located on the path of the light rays emitted by the emitter) and a photodetector which transforms the light collected as an electrical signal; a LIDAR sensor thus makes it possible to detect the presence of objects located in the emitted light beam and to measure the distance between the sensor and each detected object; and or

- one or more cameras (associated or not with a depth sensor) for acquiring one or more images of the environment around the vehicle located in the field of vision of the camera(s).

The data obtained from this or these sensors varies according to the type of sensor. When it is a radar or a LIDAR, the first data correspond for example to distance data between points of the detected object and the sensor. Each detected object is thus represented by a cloud of points (each point corresponding to a point of the object receiving the radiation emitted by the sensor and reflecting at least part of this radiation), the cloud of points representing the envelope (or a part of the envelope) of the detected object as seen by the sensor and ultimately by the vehicle carrying the sensor. In the case of a video camera, the first data correspond to data associated with each pixel of the acquired image or images, for example gray level values coded on for example 8, 10, 12 or more bits for each color channel, for example RGB (from English “Red, Green, Blue” or in French “Rouge, vert, bleu”).

The processing of these data obtained from the on-board sensors allows the first vehicle 11 to obtain information, called second information, on the respective trajectories of the second vehicles 12, 13 located in the environment 1 of the first vehicle 11.

For example, when the second vehicles 12, 13 are in motion, the analysis of the succession of positions taken by these second vehicles 12, 13 over time makes it possible to obtain second data representing the movement of each of these second vehicles 12, 13 over time, thus providing information on their respective trajectory. For example, the detection of the successive positions of the second vehicle 13 (in a determined time interval, the position of the second vehicle 13 being for example determined every 10, 20, 50 or 100 ms for a few seconds or as long as the second vehicle 13 is detected by the first vehicle 11) indicates that the second vehicle 13 is traveling in a direction of travel opposite to the direction of travel of the first vehicle 11.

According to another example, when for example one or more of these second vehicles 12, 13 is stationary, for example the second vehicle 12 positioned in the second traffic lane 102, an analysis of the image(s) taken by the the cameras on board the first vehicle 11 make it possible to determine whether the second vehicle 12 has activated one or more of its indicators 121 (also called direction change indicators). Such a determination is for example obtained by applying an image processing based on the color and applied to a sequence of several successive images, the color associated with the indicators in operation being orange with an alternation between two on and off states of the lights. According to a variant, the analysis of the images to detect the activation of one or more indicators corresponds for example to a machine learning method implementing an artificial intelligence with a network neurons, for example. The analysis of the images thus makes it possible to obtain first data representative of the actuation of one or more indicators by the second vehicle 12, providing an indication of the trajectory of this second vehicle 12. Indeed, the second vehicle 12 having activated at least one of its right flashing lights, this indicates that the second vehicle 12 intends to turn to the right, providing an indication of its trajectory in the next few seconds.

According to an optional embodiment variant, the first vehicle 11 also incorporates a ground marking detection system. Such a system comprises one or more cameras for acquiring images of the traffic lanes 101 to 103, for example the portion of road located in front and/or on the sides of the first vehicle 11. The detection system ground marking is configured to detect the ground markings in the environment of the first vehicle 11, that is to say the ground markings of the environment 1. The ground markings are also called horizontal signaling and correspond to, for example, a set of lines 110, 111, 112, 113 drawn on the ground. Image processing is applied to the images obtained from the camera(s) of the floor marking detection system to determine the presence of lines on the ground and to classify these lines into different categories, for example to determine whether the lines on the ground correspond to shore lines or center lines for example. An example of image processing to detect lines on the ground is for example described in the document WO2017194890A1. The floor marking detection system identifies, for example, the lines in solid lines 112 and 113 or in dotted lines 110 and 111.

Such detection of traffic lanes 101 to 103 allows the first vehicle 11 to associate the second vehicles 12, 13 detected with the traffic lanes 102, 103 on which they are traveling, the second vehicle 12 being associated with the second traffic lane 102 (indicating that the second vehicle 12 is traveling or is positioned on the second traffic lane 102) and the third vehicle 13 being associated with the third traffic lane 103 (indicating that the second vehicle 13 is traveling or is positioned on the third traffic lane 103).

The data obtained from the marking detection system are for example compared with the mapping data from the navigation system to confirm the correct detection of traffic lanes 101 to 103.

The first vehicle 11 corresponds for example to an autonomous vehicle with a determined level of autonomy, for example a level of autonomy higher than level 2 or level 3. The level of autonomy of the first vehicle 11 corresponds for example to one of the 5 levels of the classification according to the American federal agency in charge of road safety or one of the 6 levels of the classification of the international organization of automobile manufacturers.

The 5 levels of the classification of the federal agency responsible for road safety are:

- level 0: no automation, the driver of the vehicle fully controls the main functions of the vehicle (engine, accelerator, steering, brakes);

- level 1: driver assistance, the automation is active for certain vehicle functions, the driver retaining overall control over the driving of the vehicle; cruise control is part of this level, like other aids such as ABS (anti-lock braking system) or ESP (programmed electro-stabilizer);

- 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;

- level 3: limited autonomous driving, the driver can hand over complete control of the vehicle to the automated system which will then be in charge of critical safety functions; however, autonomous driving can only take place under certain environmental and traffic conditions (only on the motorway, for example);

- level 4: complete autonomous driving under conditions, the vehicle is designed to perform all the 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 regain control of the vehicle;

- level 5: completely autonomous driving without driver assistance in all circumstances.

The second information representative of the trajectories of the second vehicles 12, 13 obtained from the detection data of these second vehicles 12, 13 by the first vehicle 11 are used to determine third information representative of the direction of movement and/or of the direction associated with each traffic lanes 102, 103 on which second vehicles 12, 13 travel. This third information is then advantageously used to validate the first information relating to the direction and/or the direction associated with each traffic lane 101 to 103, these first information being obtained from map data via the navigation system.

The validation comprises for example the comparison of the third information with the first information. For example, the third direction information and/or the direction associated with each traffic lane 101 to 103 and determined via the detection data of the second vehicles 12, 13 are each compared to the corresponding first information. For example, the second traffic lane 102 has been detected as being a right-turn lane according to the direction of travel of the first vehicle 11 and of the first traffic lane 101 on which the first vehicle 11 is traveling. Thus, the traffic direction of the second traffic lane 102 obtained from the detection data of the second vehicle 12 is compared with the traffic direction of the second traffic lane 102 obtained from the mapping data. In the same way, the direction associated with the second traffic lane 102 (right turn) obtained from the detection data of the second vehicle 12, and more precisely from the data relating to the detection of the activation of the right turn signals, is compared to the direction associated with this second traffic lane 102 and obtained mapping data.

When the first information and the third information correspond for a given traffic lane, then the first information obtained from the mapping data is validated for this given traffic lane, which reinforces the confidence in the mapping data.

Conversely, when the first information differs from the third information for a given traffic lane, then the level of confidence associated with the mapping data is reduced. According to a variant, if the level of confidence associated with the third information is high (for example, when several second vehicles traveling on the same traffic lane make it possible to obtain the same third direction and/or direction information for this traffic lane, then the confidence level for the third information is high), then the third information replaces the first information in the navigation system. If the if the level of confidence associated with the third information is low (for example if obtained from a single second vehicle or if differences are obtained between several second vehicles traveling on the same traffic lane), then a request for confirmation of the validity or reliability of the first information is for example transmitted and transmitted to a control server or to the first vehicle 11 so that a driver or a passenger of the first vehicle 11 can verify and control this first information.

schematically illustrates a process for validating map data of the road environment of the first vehicle, according to a second particular and non-limiting embodiment of the present invention.

Such a process is for example implemented in a device 2, for example a computer, on board the first vehicle 11. According to a variant, the process is implemented in a system comprising for example several computers and on board the first vehicle 11.

Each of the operations described below is for example implemented by a hardware module, a software module or a module combining hardware and software. Some operations are for example implemented in the same module.

In a first operation, map data of the road environment of the first vehicle are obtained from a navigation system. These data are for example received according to one or more wireless connections from a satellite system and/or from one or more "cloud" servers (or "cloud" in French) 210, via a satellite link and/or a link wireless conforming to LTE 4G or 5G wireless communication system.

In a second operation 21, information associated with each traffic lane of the road environment is obtained or extracted from the mapping data. This information includes, for example, information on the number of traffic lanes, first information relating to the direction and/or the direction associated with each traffic lane of the road environment, information on the type of road environment (for example intersection, crossroads, expressway, etc.). The first information comprises for example the type of the traffic lane, selected from a set of lane types comprising for example: right lane according to the direction of traffic of the first vehicle 11, left lane according to the direction of traffic of the first vehicle , left-turn lane and right-turn lane.

In a third operation, a set of detection data from one or more second vehicles is obtained from object detection sensors 211 (radar(s), lidar(s) and/or camera(s)) on board the first vehicle 11.

In a fourth operation 22, first data representative of actuation of turn signal(s) by one or more second vehicles are obtained from the set of data, for example sequence data of images of the second vehicle(s) acquired by a or more on-board cameras in the first vehicle.

In a fifth operation 23, second data representative of the movement of one or more second vehicles are obtained from the set of data, for example data of successive positions taken by this or these second vehicles and acquired by the radars or lidar( s) of the first vehicle over a time period, of a duration for example equal to 2, 5, 10, 30 or 60 seconds.

According to a variant embodiment, only one or the other of operations 22 and 23 is implemented, according to the data obtained from the sensors of the first vehicle 11 and/or according to the type of mobility (stationary or in progress). movement) of the second vehicle(s).

In a sixth operation 24, second information representing the trajectory of each second vehicle is obtained from the set of data received from the sensors, for example from the first data obtained at operation 22 and/or from the second data obtained at operation 23. This second information makes it possible to obtain a set of third information representative of direction and/or direction associated with the lane or lanes of the road environment of the first vehicle 11.

In a seventh operation 25, the third operations are compared to the second information to validate the accuracy of the first information, the accuracy of the first information being validated when the first information and the third information are consistent.

In an eighth operation 26, parameters 212 for controlling the direction or trajectory of the first vehicle 11 are determined from the mapping data and the first information when this first information has been validated. When the first information has not been validated, because it is different from the third information, the steering control parameters of the first vehicle 11 are for example determined from the mapping data and the third information. These parameters 212 are for example transmitted, via for example a multiplexed data bus CAN or CAN FD, to control elements of the first vehicle 11 such as for example the steering system (steering angle for example), the control system speed defining the trajectory to be followed by the first vehicle 11, in particular when the first vehicle 11 corresponds to an autonomous vehicle of level 3 or more.

Some of the operations described above are for example implemented in parallel, for example operations 21 to 23.

schematically illustrates a device 3 configured to validate mapping data of a vehicle road environment, for example the first vehicle 11, according to a particular and non-limiting example embodiment of the present invention. The device 3 corresponds for example to a device on board the first vehicle 11, such as for example a computer.

The device 3 is for example configured for the implementation of the operations described with regard to FIG. 1, of FIG. 2 and/or of the steps of the method described with regard to FIG. 4. Examples of such a device 3 comprise , without being limited thereto, on-board electronic equipment such as a vehicle's on-board computer, an electronic computer such as an ECU ("Electronic Control Unit"), a smart telephone (from the English "smartphone" ), a tablet, a laptop. The elements of device 3, individually or in combination, can be integrated in a single integrated circuit, in several integrated circuits, and/or in discrete components. The device 3 can be made in the form of electronic circuits or software (or computer) modules or else a combination of electronic circuits and software modules. According to different particular embodiments, the device 3 is coupled in communication with other similar devices or systems, for example via a communication bus or through dedicated input/output ports.

The device 3 comprises one (or more) processor(s) 30 configured to execute instructions for carrying out the steps of the method and/or for executing the instructions of the software or software embedded in the device 3. The processor 30 can include integrated memory, an input/output interface, and various circuits known to those skilled in the art. The device 3 further comprises at least one memory 31 corresponding for example to a volatile and/or non-volatile memory and/or comprises a memory storage device which can comprise volatile and/or non-volatile memory, such as EEPROM, ROM, PROM, RAM, DRAM, SRAM, flash, magnetic or optical disk.

The computer code of the onboard software or software comprising the instructions to be loaded and executed by the processor is for example stored on the memory 31.

According to a particular and non-limiting embodiment, the device 3 comprises a block 32 of interface elements for communicating with external devices, for example a remote server or the "cloud", on-board sensors, devices such as radar or camera. Block 32 interface elements include one or more of the following interfaces:

- RF radio frequency interface, for example of the Bluetooth® or Wi-Fi® type, LTE (from English “Long-Term Evolution” or in French “Evolution à long terme”), LTE-Advanced (or in French LTE-avanced );

- 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 Multimedia Haute Definition” in French);

- LIN interface (from English “Local Interconnect Network”, or in French “Réseau interconnecté local”).

According to another particular embodiment, the device 3 comprises a communication interface 33 which makes it possible to establish communication with other devices (such as other computers of the on-board system when the device 3 corresponds to a computer of the on-board system ) via a communication channel 330. The communication interface 33 corresponds for example to a transmitter configured to transmit and receive information and/or data via the communication channel 330. The communication interface 33 corresponds for example to a wired network of the CAN type (from English "Controller Area Network" or in French "Réseau de Contrôleurs"), CAN FD (from English "Controller Area Network Flexible Data-Rate" or in French "Réseau de Contrôleurs à Flow flexible data system”), FlexRay or Ethernet.

According to an additional particular embodiment, the device 3 can supply output signals to one or more external devices, such as a display screen, one or more loudspeakers and/or other peripherals respectively via interfaces output not shown.

illustrates a flowchart of the different steps of a method for validating map data of a road environment of a vehicle, for example of the first vehicle 11, according to a particular and non-limiting example embodiment of the present invention. The method is for example implemented by a device on board the first vehicle 11 or by the device 3 of FIG. 3.

In a first step 41, a set of data associated with a detection of at least one second vehicle in the road environment is received, for example from a navigation system or from a memory of the device 3.

In a second step 42, second information representative of the trajectory of the at least one second vehicle is determined from the set of data.

In a third step 43, third information representing direction and/or direction associated with the at least one traffic lane is determined from the second information.

In a fourth step 44, the first information is validated from the second information.

Steps 41 to 44 are advantageously repeated as the first vehicle moves according to the map data received and representative of the road environment of the first vehicle 11.

Of course, the invention is not limited to the embodiments described above but extends to a method for determining a state representative of parking of a second vehicle, and to the device configured for the implementation of the process.

The invention also relates to a vehicle, for example an automobile or more generally a land motor vehicle, comprising the device 3 of FIG. 3.

Claims (10)

Method for validating mapping data of a road environment (1) of a first vehicle (11), said data being obtained from a navigation system and comprising first information representative of direction and/or direction associated with at at least one traffic lane (101, 102, 103) of said road environment (1), said method being implemented by said first vehicle (11), the method comprising:
- reception (41) of a set of data associated with a detection of at least one second vehicle (12, 13) in said road environment (1);
- determination (42) of second information representative of the trajectory of said at least one second vehicle (12, 13) from said set of data;
- determination (43) of third information representative of direction and/or direction associated with said at least one traffic lane (101, 102, 103) from said second information;
- validation (44) of said first information from said third information. A method according to claim 1, wherein said data set comprises:
- first data representative of actuation of at least one indicator (121) by said at least one second vehicle (12, 13); and or
- second data representative of a movement of said at least one second vehicle (12, 13) in said road environment (1). A method according to claim 1 or 2, further comprising the steps of:
- detection of said at least one traffic lane (101, 102, 103);
- association between said at least one second vehicle (12, 13) and said at least one traffic lane (101, 102, 103) as a function of third positioning data of said at least one second vehicle (12, 13), said third data belonging to said data set. Method according to one of claims 1 to 3, for which said first information comprises a type of traffic lane associated with said at least one traffic lane (101, 102, 103), said type of traffic lane belonging to a set of traffic lane types including:
- right lane in the direction of travel of said first vehicle;
- left lane in the direction of travel of said first vehicle;
- left-turn lane; And
- right-turn lane. Method according to one of Claims 1 to 4, further comprising a step of trajectory control of said first vehicle (11) according to a result of said validation (44). A method according to any of claims 1 to 5, wherein said first vehicle (11) is an autonomous vehicle. Method according to one of Claims 1 to 6, for which the said road environment (1) corresponds to an intersection between several traffic lanes. Device (3) comprising a memory (31) associated with at least one processor (30) configured for implementing the steps of the method according to any one of Claims 1 to 7. Vehicle (11) comprising the device (3) according to claim 8. Computer program product comprising instructions adapted for the execution of the steps of the method according to one of Claims 1 to 7, when the computer program is executed by at least one processor.