Method and device for controlling an automated vehicle traveling in a road environment

Abstract

The present invention relates to a method and device for controlling an automated vehicle (10) in a road environment (1). To this end, an approach to a defined area (1000) of the road environment (1) is detected. A request to obtain a level of localization accuracy for the automated vehicle (10) within the defined area (1000) is transmitted to a remote device (110). Initial information representing the level of localization accuracy of the automated vehicle within the defined area (1000) is received. The automated vehicle (10) is controlled based on this initial information. (See Figure 1 for abbreviations.)

Description

Title of the invention: Method and device for controlling an automated vehicle traveling in a road environment technical field

[0001] The present invention relates to methods and devices for controlling an automated vehicle traveling in a road environment, particularly but not exclusively an urban environment. More specifically, the present invention relates to a method and device for controlling the movement of an automated vehicle approaching a specific area of ​​the road environment, particularly but not exclusively for an electric motor vehicle. Technological background

[0002] With the development of automated vehicles (from the English "Automated Vehicle"), also called autonomous vehicle(s), needs in terms of route planning and monitoring, particularly depending on the environment around the automated vehicle, have emerged.

[0003] Controlling the trajectory of an automated vehicle, by means of one or more driver assistance systems, called ADAS system(s) (from the English "Advanced Driver-Assistance System" or in French "Système d'aide à la conduite avancé"), embedded in the automated vehicle, requires a good knowledge of the environment around the automated vehicle as well as an exact knowledge of the position of the automated vehicle.

[0004] The location of an automated vehicle is obtained as for any vehicle by the use of a receiver of a geolocation system based on a satellite positioning system designated by the acronym GNSS (from the English "Global Navigation Satellite System" or in French "Système de navigation globale par satellite"), for example a GPS system from the English "Global Positioning System" or in French "Système de emplacement global"), Galileo or Glonass.

[0005] In certain environments, such as urban areas with buildings, mountainous areas, or densely wooded areas, the reception of signals emitted by satellites can be disrupted, with signal occlusion and reflection resulting in multiple paths for the emitted signals. This leads to inaccuracies in the GNSS receiver's position assessment. A lack of precise location of the automated vehicle in a hazardous area of ​​the environment in which the automated vehicle is operating creates risks regarding the control of the vehicle's movement. automated to reach or cross this hazardous area safely, without leaving the traffic lanes or hitting any obstacles. Summary of the present invention

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

[0007] Another object of the present invention is to improve the approach to or crossing of a determined area of ​​a road environment in which an automated vehicle is traveling.

[0008] According to a first aspect, the present invention relates to a method for controlling an automated vehicle circulating in a road environment, the automated vehicle being configured to circulate with a level of autonomy exceeding a threshold in the road environment, the method being implemented by at least one processor and comprising the following steps: - detection of approach to a specific area of ​​the road environment; - transmission of a request to obtain a level of accuracy of location of the automated vehicle in the determined area to a remote device via a wireless connection; - receipt of initial information representative of the level of accuracy of location of the automated vehicle in the determined area, the initial information being determined from data representative of the visibility of a set of satellites of a satellite geolocation system in the determined area; - automated vehicle control based on initial information.

[0009] The use of information on the level of accuracy of the location that can be obtained from a satellite geolocation system in a given area of ​​which the automated vehicle is approaching makes it possible to control the automated vehicle accordingly during the approach phase of the given area, for example to continue the approach along the planned route when the level of accuracy is sufficient, i.e. above a threshold, or to defer the approach or use other means of location when the level of accuracy is insufficient, i.e. below the threshold.

[0010] Ensuring that the level of accuracy of the location is sufficient reduces, for example, the risk of deviating from the intended trajectory, which reduces the risk of an accident and increases the safety of the automated vehicle, its possible passengers and other road users.

[0011] According to one variant, the automated vehicle control includes a control of the automated vehicle's movement along a predetermined route to pass or reach the determined area when the initial information is representative of a level of location accuracy greater than a first threshold.

[0012] According to another variant, when the first information is representative of a level of localization accuracy lower than the first threshold, the method further includes a step of receiving second information representative of a level of localization accuracy of the automated vehicle as a function of time over a time interval starting at a current instant, the second information being determined as a function of the data, the control of the automated vehicle being further a function of the second information.

[0013] According to yet another variant, when the second information is representative of a level of location accuracy of the automated vehicle greater than the first threshold at a time deadline less than a second threshold, the control of the automated vehicle includes a control of reducing the speed of the automated vehicle to pass or reach the determined area when the level of location accuracy of the automated vehicle will be greater than the first threshold.

[0014] According to another variant, when the second set of information represents a level of localization accuracy for the automated vehicle that is lower than the first threshold, the control of the automated vehicle includes: - activation of an automated vehicle localization system based on inertial data from an automated vehicle's inertial measurement unit; or - activation of an automated vehicle remote control system.

[0015] According to a further variant, the determined zone belongs to a set of zones comprising: - an area including an intersection between a regular traffic lane of the automated vehicle and at least one other traffic lane; - an automated vehicle stopping zone; - an area comprising at least one building possessing a set of determined characteristics.

[0016] According to yet another variant, the detection of approach to a determined area is a function of a current position of the automated vehicle and mapping data of the road environment.

[0017] According to a second aspect, the present invention relates to a control device for an automated vehicle circulating in a road environment, the device comprising a memory associated with a processor configured for the implementation of the steps of the process according to the first aspect of the present invention.

[0018] According to a third aspect, the present invention relates to an automated vehicle, for example of the automobile type, comprising a device as described above according to the second aspect of the present invention.

[0019] According to a fourth aspect, the present invention relates to a system comprising the automated vehicle as described above according to the third aspect of the present invention and a remote device connected by wireless communication to the automated vehicle.

[0020] According to a fifth aspect, the present invention relates to a computer program which includes instructions adapted for carrying out the steps of the process according to the first aspect of the present invention, in particular when the computer program is executed by at least one processor.

[0021] Such a computer program may use any programming language, and be in the form of source code, object code, or an intermediate code between source code and object code, such as in a partially compiled form, or in any other desirable form.

[0022] According to a sixth 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 process according to the first aspect of the present invention.

[0023] On the one hand, the recording medium can be any entity or device capable of storing the program. For example, the medium can include a storage means, such as a ROM, a CD-ROM or a microelectronic circuit-type ROM, or a magnetic recording means or a hard disk drive.

[0024] 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 transmitted via an electrical or optical cable, by conventional or radio frequency, by self-directing laser beam, or by other means. The computer program according to the present invention can, in particular, be downloaded from an Internet-type network.

[0025] 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 process in question. Brief description of the figures

[0026] Other features and advantages of the present invention will become apparent from the description of the particular and non-limiting embodiments of the present invention below, with reference to the attached Figures 1 to 4, in which:

[0027] [Fig.1] schematically illustrates an environment comprising an automated vehicle, according to a particular embodiment of the present invention;

[0028] [Fig.2] illustrates a flowchart of the different operations of a process of automated vehicle control of [Fig. 1], according to a particular and non-limiting embodiment of the present invention

[0029] [Fig.3] illustrates a device configured for the control of the automated vehicle of the [Fig.1] circulating in its environment, according to a particular and non-limiting embodiment of the present invention.

[0030] [Fig.4] illustrates a flowchart of the different stages of a process for controlling the automated vehicle of the [Fig. 1] circulating in its environment, according to a particular and non-limiting embodiment of the present invention. Description of embodiment examples

[0031] A method and a device for controlling an automated vehicle travelling in a road environment will now be described in what follows with joint reference to Figures 1 to 4. The same elements are identified with the same reference signs throughout the description that follows.

[0032] The terms "first," "second" (or "firsts," "seconds"), etc., are used in this document by arbitrary convention to allow for the identification and distinction of different elements (such as operations, means, etc.) implemented in the embodiments described below. Such elements may be distinct or correspond to a single element, depending on the embodiment.

[0033] According to a particular and non-limiting example of an embodiment of the present invention, the control of an automated vehicle is implemented by the automated vehicle, for example by one or more processors of one or more computers of the automated vehicle in charge of the driving assistance system(s) ensuring autonomous trajectory and / or route tracking control, i.e. without a driver or without intervention from a possible driver.

[0034] To this end, the approach to a specific area, for example a hazardous area, of the road environment is detected, for example from mapping data associated with the current position of the automated vehicle or from data received from sensors onboard the automated vehicle. Following this detection, a request to obtain a level of localization accuracy for the automated vehicle in the specified area is transmitted to a remote device via a wireless connection. In response to the request, initial information representing the level of localization accuracy of the automated vehicle in the specified area is received via the wireless connection.This initial information is determined from representative visibility data from a set of satellites in a satellite geolocation system within the defined area. Such data might correspond, for example, to data from a satellite visibility map associated with the defined area or, more broadly, with the road environment. The determination... Initial information is, for example, provided by the automated vehicle receiving data from a remote server-type device, or by the remote device storing the data and generating the initial information to transmit it to the automated vehicle. As the automated vehicle approaches the designated area, it is controlled based on this initial information to adapt its behavior, if necessary, to the level of localization accuracy that can be determined within that area.

[0035] Fig. 1 schematically illustrates a road environment 1 comprising an automated vehicle 10, according to a particular and non-limiting embodiment of the present invention.

[0036] The road environment 1 corresponds to any environment in which the automated vehicle 10 is likely to travel. The road environment 1 corresponds, for example, to an urban environment, a mixed environment comprising an urban part and an extra-urban part, a non-city environment, etc.

[0037] Environment 1 comprises one or more determined zones, that is to say one or more zones identified as particular via one or more characteristics of these zones.

[0038] A determined zone corresponds, for example, to an area presenting a greater risk of accident for the automated vehicle 10 than an average risk in the road environment 1. According to another example, a determined zone corresponds to an area presenting particular characteristics leading to risks of disruption in the reception of satellite signals enabling the automated vehicle 10 to locate itself, i.e. to determine its current position.

[0039] A given zone corresponds to a subsequent zone or to a zone comprising a combination of several subsequent zones: - an area comprising an intersection between a lane used by the automated vehicle and at least one other lane, such an intersection corresponding, for example, to a traffic light, stop sign, yield sign, priority to the right, etc.; and / or - an automated vehicle stopping zone, for example a stop to allow automated vehicle users to enter and / or exit the automated vehicle, a stop to pick up or drop off a package, along a pre-calculated route; and / or - an area comprising at least one building with a set of specific characteristics, for example one or more buildings with a height exceeding a threshold (for example 5 or 10 m) and / or a width exceeding a threshold (for example 10, 20 or 50 m) and / or a glazed area exceeding a threshold (for example 50, 100 or 500 m2), such a building being likely to obstruct the receiving satellite signals and / or generating multiple paths for receiving satellite signals.

[0040] According to the non-limiting example of [Fig.1], the road environment 1 includes a defined area 1000 corresponding to a crossroads or intersection between the current traffic lane of the automated vehicle 10 and one or more other traffic lanes.

[0041] An automated vehicle is defined as a vehicle equipped with a sophisticated driver assistance system that ensures vehicle control and is capable of operating in its road environment without driver intervention or under the control of a person not involved in driving the automated vehicle, except in emergencies, for example. A vehicle enabling such autonomous driving (also called automated driving) must have a level of autonomous driving higher than a certain level out of a total number of levels. For example, the automated vehicle has an autonomy level of 4 or higher out of the 5 levels defined in the classification published by the federal agency responsible for road safety in the USA, or out of the 6 levels defined in the classification published by the international organization of motor vehicle manufacturers, which comprises 6 levels.According to one embodiment, the automated vehicle 10 has an autonomy level greater than or equal to 3 out of the 5 or 6 levels provided for in the two classifications mentioned above.

[0042] The automated vehicle 10 corresponds for example to a vehicle with a thermal engine, an electric vehicle or a hybrid vehicle (combining a thermal engine and an electric motor).

[0043] According to one embodiment, the automated vehicle 10 corresponds to an autonomous shuttle, for example, with an electric motor. Such an autonomous shuttle is configured to follow a predetermined route with stops along the way to pick up one or more passengers. The route may be modified over time (for example, occasionally or seasonally), for example, according to user needs, to avoid temporary work zones, etc. According to another embodiment, the autonomous shuttle is shared by several users in an on-demand service mode, for example, that is, the autonomous shuttle picks up each passenger at a predetermined location via a mobile application or a website managed by the autonomous shuttle operator and drops them off at the destination desired by each passenger.

[0044] According to another embodiment, the automated vehicle 10 corresponds to a vehicle configured to transport and deliver parcels to one or more recipients, the route to be taken varying according to the delivery addresses of the parcels.

[0045] The automated vehicle 10 corresponds, for example, to a so-called connected vehicle, that is to say an automated vehicle configured to communicate (transmit and receive) data according to a wireless communication mode, for example via a wireless network infrastructure or according to a direct communication mode.

[0046] For this purpose, the automated vehicle 10 includes a communication system or interface comprising, for example, one or more communication antennas connected to a telematic control unit, called a TCU (from the English "Telematic Control Unit"), itself connected to one or more computers of the embedded system of the automated vehicle 10. The antenna(s), the TCU unit and the computer(s) form, for example, a multiplexed architecture for the implementation of various services useful for the proper functioning of the automated vehicle 10.The computer(s) and the TCU communicate and exchange data with each other via one or more computer buses, for example a CAN (Controller Area Network), CAN FD (Controller Area Network Flexible Data-Rate), FlexRay (according to ISO 17458) or Ethernet (according to ISO / IEC 802-3) type communication bus.

[0047] The network infrastructure includes, for example, communication devices 101 corresponding, for example, to an antenna of a cellular network of type LTE 4G or 5G or to a UBR (“Roadside Unit”).

[0048] Each communication device 101 is advantageously connected to one or more remote servers 110 or to the "cloud" 100 (or in French "nuage") via a wired and / or wireless connection. The communication device 101 is thus configured to act as a relay between the "cloud" 100 and its servers 110 on the one hand and the automated vehicle 10 on the other.

[0049] The automated vehicle 10 communicates, for example, using a so-called V2X communication system, for example based on the 3GPP LTE-V or IEEE 802.1 lp standards of ITS G5. In such a V2X communication system, each vehicle carries a node (or wireless communication system / interface) to enable vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I) and / or vehicle-to-pedestrian (V2P) communication, with pedestrians being equipped with mobile devices (for example, a smartphone) configured to communicate with the vehicles.

[0050] The automated vehicle 10 further comprises a receiver for a GNSS-type geolocation system configured to determine representative data Its geographic position at any given moment is determined based on signals received from a set of 111 to 114 GNSS satellites. The data representing the geographic position takes the form of coordinates (latitude and longitude). The geographic position obtained from the GNSS system is considered absolute because the coordinates are expressed in the same frame of reference for each vehicle, namely the world frame of reference.

[0051] A control process for the automated vehicle 10 in the road environment 1 is implemented by one or more computers of the automated vehicle 10, i.e. by one or more processors of this or these computers.

[0052] An example of implementation is described opposite [Fig.2] below.

[0053] Fig. 2 schematically illustrates a process of controlling the automated vehicle 10 in circulation in the road environment 1 and approaching the determined zone 1000, according to particular and non-limiting embodiments of the present invention.

[0054] In a first operation 20 of the process, the automated vehicle 10 is in the approach phase of the determined zone 1000 and the automated vehicle 10 detects this approach of the determined zone 1000 of the road environment 1.

[0055] Detection is implemented for example when the automated vehicle 10 is at a determined distance from this determined zone 1000, the determined distance depending for example on the type of the determined zone 1000. For example, when the automated vehicle 10 arrives at 100, 200 or 500 m from the determined zone, a detection signal of the determined zone 1000 is generated and transmitted on the on-board network of the automated vehicle 10.

[0056] Detection is achieved, for example, by using the current position of the automated vehicle 10 (obtained from the GNSS receiver) and road environment mapping data 1, the determined zone 1000 being identified as such in the mapping data. When the computer implementing the process detects that the automated vehicle 10 is approaching the determined zone 1000, that the determined zone 1000 is on the route followed by the automated vehicle 10, and that the distance between the current position and the determined zone 1000 exceeds a threshold value equal to the determined distance, the detection signal is generated and transmitted.

[0057] According to another example, detection is achieved from data obtained from one or more sensors on board the automated vehicle 10, for example one or more cameras. Detection is achieved, for example, by implementing one or more image processing methods on the image data received from the camera(s) to identify the area as corresponding to an identified area (for example, by comparing the features extracted from the images to positive image features comprising a determined area and to image features negatives not including a determined area) and to determine the distance separating the automated vehicle 10 from the determined area 1000.

[0058] According to yet another example, detection is obtained via the reception of a signal emitted by a beacon or transmitter located in the determined area 1000 and configured to emit an alert signal to connected vehicles such as the automated vehicle 10, for example according to an infrastructure to vehicle communication mode, known as I2V (from the English "Infrastructure to Vehicle").

[0059] In a second operation 21 of the process, the automated vehicle 10 generates and transmits a request to obtain a level of localization accuracy of the automated vehicle 10 in the determined area 1000, this request being transmitted to the remote device 110 via a wireless connection based on the wireless network infrastructure linking the automated vehicle 10 to the "cloud" 100.

[0060] This request is generated and transmitted automatically following the detection of the approach to the determined zone of the first operation 10.

[0061] Such a request includes, for example, identification data for the automated vehicle 10 and / or data representing the current position of the automated vehicle 10 and / or identification or location data for the determined area 1000.

[0062] In a third operation 22 of the process, first information representative of the level of localization accuracy of the automated vehicle 10 in the determined area 1000 is received by the computer of the automated vehicle 10 implementing the process.

[0063] This initial information is determined from representative visibility data of a set of satellites 111 to 114 of a satellite geolocation system in the determined area 1000.

[0064] According to a first embodiment, this first information is received from the remote device 110, the latter implementing the determination from the representative visibility data of the set of satellites 111 to 114 stored in the memory of the remote device 110 (or received from another remote device connected by wired or wireless communication to the remote device 110).

[0065] According to a second embodiment, this first information is determined by the computer of the automated vehicle 10 implementing the process (or by another computer of the automated vehicle 10) and received from a memory of the automated vehicle 10 in which it is stored after determination, this computer then receiving the representative visibility data of the set of satellites 111 to 114 of the remote device 110 via the wireless connection.

[0066] The representative visibility data for the set of satellites 111 to 114 include representative visibility information for each satellite of the set of satellites 111 to 114 of the GNSS system, from a set of points in the road environment 1 (or more precisely in the determined area).

[0067] The satellite visibility map is for example generated by the remote device 110 as a function of representative data of orbital parameters of each satellite of the set of satellites 111 to 114 and representative data of a digital surface model associated with the road environment 1, the determined area of ​​which 1000, the representative visibility data of the set of satellites 111 to 114 being thus obtained from the representative data of orbital parameters of each satellite and the representative data of a digital surface model associated with the road environment 1.

[0068] A satellite visibility map is, for example, associated with a specific time instant. Indeed, since the location of satellites varies in space over time according to the orbit followed by each of these satellites, the satellite visibility map also varies over time, the visibility of each satellite from a point or a surface element of the road environment 1 varying according to the location of the satellite in its orbit.

[0069] The representative visibility data for the set of satellites 111 to 114 thus obtained represent, for example, the visibility of each satellite in the GNSS system from a set of points in the road environment 1 for different time instants belonging to a given time range. According to one embodiment, the first data received correspond to representative data of the orbital parameters of each satellite and representative data of a digital surface model associated with the road environment 1, the computer(s) of the automated vehicle 10 and / or the processor(s) of the remote device 110 performing the calculations to predict the visibility over the determined area 1000 at different time instants, as required, as described below.

[0070] The satellite visibility map is generated according to any method known to a person skilled in the art, for example as described in the document entitled "Study for the production of GNSS satellite visibility maps", by Guillaume Bizouard, published in the XYZ magazine, No. 111 in the 2nd quarter of 2007.

[0071] The described method involves extrapolating the orbital / Keplerian parameters of the satellites to the desired forecast date or time, these calculations relating to celestial mechanics being known. Various changes of reference frames are applied to the results of the calculations to obtain topocentric coordinates (azimuth and elevation). For each satellite, its topocentric coordinates are thus obtained at the desired observation location on Earth, for example, for each point in a set of points in the road environment 1, the area of ​​which is defined as 1000.

[0072] The point set includes, for example, reference points defining the road sections of the road environment 1, of which the determined area 1000, the paths forming the road sections being discretized to obtain the point set.

[0073] To refine the satellite visibility of a point in the road environment 1, whose defined area is 1000, it is necessary to take into account the surrounding obstacles (buildings and vegetation, for example). To this end, the method described provides for the use of geodata describing the space associated with the road environment 1, whose defined area is 1000, this geodata being obtained, for example, from a LiDAR point cloud (for example, obtained from LiDAR(s) on board an aircraft that flew over the road environment 1, whose defined area is 1000, for the acquisition of the point cloud), this geodata corresponding, for example, to data from a digital surface model, known as a DSM, also called a digital terrain model.

[0074] The satellite visibility map thus obtained makes it possible to know at any point in the road environment 1, including the determined area 1000, the visibility of each satellite in the constellation, for example to determine if a satellite is visible in direct line of sight at a given time.

[0075] The representative visibility data of the set of satellites 111 to 114 thus correspond to data enabling the determination of which satellites 111 to 114 are visible from a point or a surface element of the road environment 1, whose determined area 1000, at a given time.

[0076] According to one variant, the representative visibility data of the set of satellites 111 to 114 correspond to data indicating the number of satellites visible from each point of the set of points of the road environment 1, of which the determined area 1000, for example 1, 2, 3, 4, 5 or more satellites at a given time.

[0077] The quality or level of accuracy of the automated vehicle 10's localization depends on the number of satellites visible from that location. To obtain a localization with a level of accuracy exceeding a certain threshold, it is necessary to obtain a signal emitted from four satellites within direct line of sight. Indeed, while three satellites are sufficient to obtain a position using the trilateration technique, a fourth satellite is necessary to precisely determine the offset of the receiver onboard the automated vehicle 10 relative to the clocks of satellites 111 to 114. For example, a clock offset of 10 nanoseconds results in a position error of 3 meters. The greater the number of satellites visible from a given position, the more precise the position determination will be.

[0078] The first information determined from the representative visibility data of the 111 to 114 satellite array corresponds to data indicating the level of positional accuracy that can be determined on the road environment at a given moment, this indicator taking for example a value in a defined set of values, for example in a set comprising 3, 5 or 10 values, the level of precision obtained being for example higher the greater the value of the indicator.

[0079] According to one variant, the first information determined from the representative visibility data of the satellite set 111 to 114 corresponds to data indicating the number of satellites visible from each point of the road environment point set 1, of which the determined area 1000, for example 1, 2, 3, 4, 5 or more satellites at a given time.

[0080] When the first information is representative of a level of localization accuracy greater than a first threshold, the process continues with an eighth operation 27 described below.

[0081] When the first information is representative of a level of localization accuracy lower than the first threshold, the process continues with a fourth operation 23.

[0082] The value of the first threshold depends on the initial information. For example, when the initial information corresponds to an indicator taking a value from a defined set of values, for example from a set comprising 3, 5 or 10 values, the first threshold is, for example, 3 when the number of values ​​in the set is equal to 5, or the first threshold is equal to 6 or 7 when the number of values ​​in the set is equal to 10. When the initial information corresponds to data indicating the number of visible satellites, the first threshold is, for example, equal to 4.

[0083] In the fourth operation 23 of the process, a request to obtain a level of localization accuracy for the automated vehicle 10 within the defined area 1000 over a time interval starting at a current instant is generated and transmitted. This request is transmitted to the remote device 110 via the wireless connection based on the wireless network infrastructure linking the automated vehicle 10 to the "cloud" 100.

[0084] In a fifth operation 24 of the process, second pieces of information representing a level of localization accuracy of the automated vehicle 10 as a function of time over a time interval starting at a current instant are received, in response to the request.

[0085] These second pieces of information are determined as the first pieces of information according to the representative visibility data of the set of satellites 111 to 114 from the determined area 1000 over a determined time horizon, for example over the next 3, 5 or 10 minutes.

[0086] This second information is, for example, determined by the remote device 110 and received from this remote device 110 via the wireless connection linking the remote device 110 to the automated vehicle 10. According to another example, this second data is determined by a computer of the automated vehicle 10 and received from a memory of the automated vehicle 10 in which it is stored after determination.

[0087] This second information allows the automated vehicle 10 to determine whether the level of accuracy of the location will improve in the time interval (or time horizon) for which this second information was determined, that is to say, to determine whether the level of accuracy of the location will soon rise above the first threshold value, for example in the next few minutes.

[0088] When the second information is representative of a level of localization accuracy of the automated vehicle 10 greater than the first threshold at a time deadline less than a second threshold (for example equal to 2, 3, 5 or 10 minutes), then the process continues with a sixth operation 25.

[0089] In the sixth operation 25 of the process, the speed of the automated vehicle 10 is controlled so as to be reduced so that the automated vehicle reaches or passes the determined zone 1000 when the level of localization accuracy of the automated vehicle 10 in this determined zone will be greater than the first threshold.

[0090] The speed reduction control is for example based on a set speed initially planned when the route of the automated vehicle 10 was initially established, the automated vehicle 10 moving in the road environment according to a determined route and according to determined control parameters, including speed.

[0091] Speed ​​reduction control corresponds to control of an on-board speed regulation system, such as an ACC system (from the English "Adaptive Cruise Control" or in French "system de régulation adaptative de vitesse") for example.

[0092] The process then continues with the eighth operation 27 of the process.

[0093] When the second information is representative of a level of localization accuracy of the automated vehicle lower than the first threshold over the determined interval or time horizon, the process continues with a seventh operation 26.

[0094] In the seventh operation 26 of the process, the computer in charge implements one of the following operations to adapt to the lack of precision in the location that can be obtained via the signals received from satellites 111 to 114 of the GNSS system in the determined time horizon: - activation of an automated vehicle localization system 10 based on inertial data from an inertial measurement unit of the automated vehicle 10: a Such activation allows the automated vehicle 10 to determine its position independently of the GNSS system, for the time it takes to reach or pass through the defined zone 1000 and access a zone where the level of accuracy of the localization via the GNSS system will rise above the first threshold; the activation of such a system corresponds, according to one variant, to an adaptation of the GNSS fusion module to the use of inertial data by modifying the weighting of the different parameters of the particle filter (for example, an extended Kalman filter) necessary for determining the position of the automated vehicle 10; or - activation of a remote control system for the automated vehicle 10, for example activation of trajectory control / driving of the automated vehicle 10 by a remote operator and / or activation of remote decision support.

[0095] Remote control of the automated vehicle corresponds for example to control of the automated vehicle by a remote operator transmitting commands to the automated vehicle 10 via a wireless connection to control the movement of the automated vehicle 10 in the determined area 1000, based for example on data captured by one or more environmental sensors of the automated vehicle 10 (radar, lidar, camera) and transmitted by the automated vehicle 10 to the remote operator.

[0096] The teleoperator corresponds, for example, to a human teleoperator or a virtual teleoperator implemented by one or more processors of a computer or server. The virtual teleoperator corresponds, for example, to an artificial intelligence implemented in the form of a neural network receiving as input data from sensors and providing as output control data for the automated vehicle 10.

[0097] In an eighth operation 27 of the process, the automated vehicle 10 is controlled according to the first information, and according to the second information according to the embodiments described with regard to operations 23 to 26, to pass or reach the determined area 1000.

[0098] The movement of the automated vehicle 10 in the determined area is, for example, automatically controlled according to instructions associated with a route established beforehand, according to initial control parameters or adapted according to the level of accuracy of the location that can be obtained in the determined area so that the automated vehicle 10 follows the route to reach the final destination of the journey and respecting any intermediate stops.

[0099] The trajectory of the automated vehicle 10 is automatically controlled by a set of on-board AD AS systems such as a speed regulation system, a lane keeping assist system, etc., according to instructions received by these systems.

[0100] The movement of the automated vehicle 10 is further controlled based on data obtained from environmental perception sensors on board the automated vehicle 10, such as, for example: - one or more millimeter-wave radars arranged on the automated vehicle 10, for example at the front, at the rear, on each front / rear corner of the vehicle; each radar is adapted to emit electromagnetic waves and to receive the echoes of these waves reflected by one or more objects, in order to detect obstacles and their distances from the automated vehicle 10; and / or - one or more LIDAR(s) (Light Detection and Ranging), a LIDAR sensor corresponding to an optoelectronic system composed of a laser emitter, a receiver including a light collector (to collect the portion of the light emitted by the emitter and reflected by any object located in the path of the light rays emitted by the emitter) and a photodetector that transforms the collected light into 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 the acquisition of one or more images of the environment around the automated vehicle 10 located in the field of vision of the camera(s).

[0101] The data obtained from these perception sensors makes it possible to adapt the behavior of the automated vehicle 10, for example its trajectory or its speed, to the real traffic conditions encountered in the road environment 1, of which the determined area 1000.

[0102] Figure 3 schematically illustrates a device 3 configured for controlling an automated vehicle, for example the automated vehicle 10, according to various specific and non-limiting embodiments of the present invention. The device 3 corresponds, for example, to a device embedded in the automated vehicle 10, corresponding, for example, to a computer.

[0103] According to a particular embodiment, device 3 corresponds to a remote device such as remote device 110.

[0104] Device 3 is, for example, configured to carry out at least some of the operations described opposite Figures 1 and 2 and / or the steps of the process described opposite [Fig. 4]. Examples of such a device 3 include, but are not limited to, embedded electronic equipment such as a vehicle's on-board computer, an electronic control unit such as an ECU (Electronic Control Unit), a TCU, a controller, a computer, a server, or a mobile communication device (e.g., embedded in an automated vehicle 10 and connected in wired or wireless communication to this automated vehicle 10). The elements of device 3, individually or in combination, can be integrated into a single integrated circuit, into several integrated circuits, and / or into discrete components. Device 3 can be implemented as electronic circuits or software (or computer) modules, or a combination of electronic circuits and software modules.

[0105] The device 3 comprises one (or more) processor(s) 30 configured to execute instructions for carrying out the steps of the process and / or for executing instructions from the software embedded in the device 3. The processor 30 may 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, for example, volatile and / or non-volatile memory, and / or includes a memory storage device that may include volatile and / or non-volatile memory, such as EEPROM, ROM, PROM, RAM, DRAM, SRAM, flash, magnetic disk, or optical disk.

[0106] The computer code of the embedded software(s) including the instructions to be loaded and executed by the processor is for example stored on memory 31.

[0107] According to various particular and non-limiting embodiments, the device 3 is coupled in communication with other similar devices or systems and / or with communication devices, for example a TCU (Telematic Control Unit), for example via a communication bus or through dedicated input / output ports.

[0108] According to a particular and non-limiting embodiment, the device 3 includes a block 32 of interface elements for communicating with external devices. The interface elements of the block 32 include one or more of the following interfaces: - radio frequency RF 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 2.4 GHz frequency band, or of the Sigfox® type using UBN (Ultra Narrow Band) radio technology, or LoRa in the 868 MHz frequency band, LTE (Long-Term Evolution), LTE-Advanced, 5G; - 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", or in French "Réseau interconnecté local").

[0109] According to another particular and non-limiting embodiment, the device 3 includes a communication interface 33 which enables communication with other devices (such as other computers in the embedded 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 (Controller Area Network), CAN FD (Controller Area Network Flexible Data-Rate), FlexRay (standardized by ISO 17458) or Ethernet (standardized by ISO / IEC 802-3) type.

[0110] According to a particular and non-limiting embodiment, the device 3 can provide output signals to one or more external devices, such as a display screen 340, touch or not, one or more speakers 350 and / or other peripherals 360 (projection system) via output interfaces 34, 35 and 36 respectively. According to a variant, one or more of the external devices is integrated into the device 3.

[0111] Figure 4 illustrates a flowchart of the different steps of a method for controlling an automated vehicle, for example the automated vehicle 10, according to a particular and non-limiting embodiment of the present invention. The method is implemented, for example, by a computer, for example by the device 3 of Figure 3.

[0112] In a first step 41, the approach to a determined area of ​​the road environment is detected.

[0113] In a second step 42, a request to obtain a level of localization accuracy of the automated vehicle in the determined area is transmitted to a remote device via a wireless connection.

[0114] In a third step 43, first information representative of the level of localization accuracy of the automated vehicle in the determined area is received, the first information being determined from representative visibility data of a set of satellites of a satellite geolocation system in the determined area.

[0115] In a fourth step 44, the automated vehicle is controlled according to the first information.

[0116] According to one variant, the variants and examples of the operations described in relation to [Fig.1] and / or [Fig.2] apply to the steps of the process in [Fig.4].

[0117] Of course, the present invention is not limited to the embodiments described above but extends to a method for determining the level of accuracy of the location of an automated vehicle by receiving signals from GNSS satellites, which would include secondary steps without departing from the scope of the present invention. The same would apply to a device configured for implementing such a method.

Claims

Demands

1. Method for controlling an automated vehicle (10) travelling in a road environment (1), said automated vehicle (10) being configured to travel with a level of autonomy greater than a threshold in said road environment (1), said method being implemented by at least one processor and comprising the following steps: - detection (41) of approach to a determined area (1000) of said road environment (1); - transmission (42) of a request to obtain a level of localization accuracy of said automated vehicle (10) in said determined area (1000) to a remote device (110) via a wireless connection;- reception (43) of initial information representative of said level of localization accuracy of said automated vehicle (10) in said determined area (1000), said initial information being determined from representative visibility data of a set of satellites (111 to 114) of a satellite geolocation system in said determined area (1000); - control (44) of said automated vehicle (10) according to said initial information.

2. A method according to claim 1, wherein said control of the automated vehicle (10) comprises a control of the movement of said automated vehicle (10) along a determined route to pass through or reach said determined zone (1000) when said first information is representative of a level of localization accuracy greater than a first threshold.

3. A method according to claim 2, wherein, when said first information is representative of a level of localization accuracy lower than said first threshold, said method further comprising a step of receiving second information representative of a level of localization accuracy of said automated vehicle (10) as a function of time over a time interval starting at a current instant, said second information being determined as a function of said data, the control of said automated vehicle (10) further being a function of the second information.

4. A method according to claim 3, wherein, when the second information is representative of a level of localization accuracy of said automated vehicle (10) greater than said first threshold at a time delay less than a second threshold, said control of the automated vehicle (10) includes a control of reducing the speed of said automated vehicle (10) to pass or reach said determined zone (1000) when the level of localization accuracy of said automated vehicle (10) will be greater than said first threshold.

5. A method according to claim 3, wherein, when the second information is representative of a level of localization accuracy of said automated vehicle (10) lower than said first threshold, the control of the automated vehicle comprises: - an activation of a localization system of said automated vehicle (10) from inertial data from an inertial unit of said automated vehicle (10); or - an activation of a remote control system of said automated vehicle (10).

6. A method according to any one of claims 1 to 5, wherein said determined zone (1000) belongs to a set of zones comprising: - a zone comprising an intersection between a current traffic lane of the automated vehicle (10) and at least one other traffic lane; - a stopping zone of said automated vehicle (10); - a zone comprising at least one building having a set of determined characteristics.

7. A method according to any one of claims 1 to 6, wherein the approach detection of a determined area (1000) is a function of a current position of said automated vehicle (10) and mapping data of said road environment (1).

8. Computer program comprising instructions for carrying out the method according to any one of claims 1 to 7, when such instructions are executed by at least one processor.

9. Device (3) for controlling an automated vehicle traveling in a road environment, said device comprising a memory (31) associated with at least one processor (30) configured for carrying out the steps of the method according to any one of claims 1 to 7.

10. Automated vehicle (10) comprising the device (3) according to claim 9.

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