Speed ​​limit estimate

Abstract

We propose a method implemented in a vehicle for building a collaborative network between vehicles to determine speed limits. The vehicle travels on a section of road with one or more other vehicles. Each other vehicle has its own speed limit detection system. The method includes receiving (S10) information sent by each other vehicle, including, for each other vehicle, a respective speed limit value detected by the other vehicle's respective detection system, as well as the section of road where the detected speed limit value applies. The method includes estimating (S40), by the vehicle, a speed limit for the section of road based on each received speed limit value. The method improves speed limit detection. [Fig. 1]

Description

Title of the invention: Speed ​​limit estimation technical field

[0001] This disclosure relates to a method implemented in a vehicle, a computer program for a vehicle system for executing such a method, a storage medium for such a program, and a vehicle system. Technical background

[0002] Vehicles equipped with speed limit detection systems now exist that can detect and display speed limits to the driver while the vehicle is in motion. Such speed limit detection systems can, for example, detect speed limits by analyzing images of the road taken by a vehicle camera, or by consulting a map, for example stored on a remote server to which the vehicle connects, which lists speed limits for roads, for example at the regional or departmental level. Current systems also offer a combination of these two approaches by cross-referencing information extracted from camera images and map data.

[0003] A current limitation of these speed limit detection systems concerns the reliability of the estimate provided. Indeed, certain problems can affect the performance of such systems. In particular, in the case of map-based systems, the difficulties encountered include loss of connectivity, map obsolescence due to changes in road layout, and / or unlisted temporary speed limits, for example, due to roadworks. In the case of camera-based systems, the difficulties encountered may arise from limited visibility in the captured images, for example, caused by obstacles such as trucks or other vehicles blocking the view. In the situation illustrated in [Fig. 4], for example, vehicle 101 is positioned behind a truck 201, which obstructs the camera's field of vision, thus preventing the detection of the 80 km / h speed limit sign 301.Such systems can also encounter difficulties related to camera malfunctions, such as hardware problems or memory corruption. These problems impact the reliability of speed limit detection systems.

[0004] There is therefore a need to improve speed limit detection. Summary

[0005] A method implemented in a vehicle is proposed for building a collaborative network between vehicles to determine the speed limit. The vehicle travels on a section of road with one or more other vehicles. Each other vehicle has its own speed limit detection system. The method includes receiving information from each other vehicle, including, for each other vehicle, the respective speed limit value detected by the other vehicle's respective detection system, as well as the section of road where the detected speed limit value applies. The method includes the vehicle estimating a speed limit for the section of road based on each received speed limit value.

[0006] A transmission, by each other vehicle, of a respective value of speed limit detected by the detection system of the other vehicle as well as the portion of lane where the detected speed limit value is applicable may be a prerequisite for acceptance.

[0007] Each other vehicle requests a transmission, by each other vehicle, of a respective speed limit value detected by the detection system of the other vehicle as well as the portion of the lane where the detected speed limit value is applicable may be a prerequisite for receipt.

[0008] The vehicle may include a speed limit detection system. The method may include detection of a speed limit value by the vehicle's detection system. The speed limit estimation may take into account the speed limit value detected by the vehicle's detection system.

[0009] The method may further include, before estimating the speed limit, a determination, by the vehicle detection system, of a confidence index on the detected speed limit value, and a determination that the determined confidence index is less than a predetermined confidence threshold.

[0010] The one or more other vehicles may include several other vehicles. The estimation of the speed limit value may include the application of a consensus algorithm taking as input the respective speed limit values ​​detected by the other vehicles and providing as output the estimated speed limit, and optionally a confidence index.

[0011] The information sent may include, for at least one other vehicle, a confidence level for the respective speed limit value sent, a current speed value, and / or a vehicle category. The speed limit estimation may take into account the confidence level, the current speed, and / or the vehicle category sent for at least one other vehicle.

[0012] The method may further include, after estimation, sending to each other vehicle the speed limit estimated by the vehicle.

[0013] The method may further include, after estimation, displaying the estimated speed limit to the driver of the vehicle.

[0014] A computer program for vehicle systems is also proposed. The computer program includes instructions that, when executed by a processor, cause the processor to implement the process. The vehicle system may include a telematics system, for example, comprising means of communication (e.g., 5G and / or V2X) with one or more other vehicles (e.g., a signal transmitting and / or receiving antenna). Alternatively or additionally, the vehicle system may include a centralized driver assistance system. The centralized driver assistance system may be connected to the aforementioned means of communication. In some examples, the centralized driver assistance system may be connected. For example, the connected centralized driver assistance system may include means of communication (e.g., 5G and / or V2X) with one or more devices (such as mobile phones), for example, to transmit the estimated speed limit.

[0015] A computer-readable storage medium is also proposed on which said computer program is recorded.

[0016] Such a vehicle system is also proposed, comprising said storage medium. The vehicle system is configured to implement said method. The system may include said telematics system and / or said centralized driver assistance system, for example, connected. Brief description of the figures

[0017] Non-limiting examples will be described with reference to the following figures:

[0018] Figures 1, 2, and 3 illustrate examples of process flowcharts.

[0019] Figure 4 illustrates an example of a problematic situation for current camera-based detection systems.

[0020] Figure 5 illustrates an example of receiving information sent by other vehicles.

[0021] Figure 6 illustrates an example of sending the estimated speed limit. Detailed description

[0022] With reference to the flowchart in [Fig. 1], a method implemented by a telematics system in a vehicle is proposed. The vehicle travels on a section of road with one or more other vehicles. Each other vehicle includes its respective speed limit detection system. The method includes a reception S10, by the vehicle's telematics system, of information sent by each other vehicle, including, for each other vehicle, a respective speed limit value detected by the other vehicle's respective detection system. The method includes an estimation S40, by the vehicle, of a speed limit for the section of road based on each respective received speed limit value. This information may have been provided by a speed limit detection network infrastructure or sent by vehicles to the network infrastructure for future use.

[0023] The method improves speed limit detection.

[0024] Indeed, the estimation of the speed limit at which the vehicle is traveling is carried out cooperatively between the vehicles. Each vehicle shares the respective speed limit value detected by its respective detection system. The method thus allows the speed limit to be estimated from all the respective speed limit values ​​shared by the vehicles. In other words, the vehicles can self-monitor and assist each other. This gives the estimated speed limit a high degree of reliability, with a success rate exceeding 90%.

[0025] Furthermore, the method overcomes the difficulties encountered in current speed limit detection systems. Indeed, collaborative speed limit estimation allows for the estimation of the speed limit even when the vehicle's detection system fails to provide a speed limit value. In other words, the vehicle relies on values ​​detected by other detection systems to estimate a value. In the event of a camera malfunction (for example, due to an obstruction), loss of connectivity with the map, or temporary speed limits due to roadworks, the method allows for the estimation of the speed limit based on other vehicles. The method thus increases the reliability of speed limit detection.

[0026] On the other hand, self-monitoring and mutual assistance between vehicles make speed limit estimation particularly efficient. This allows any new speed limit encountered by the vehicle to be quickly displayed to the driver, for example within 2 seconds.

[0027] The steps of the process can be executed by the vehicle's telematics system. Each of these steps is now discussed in more detail.

[0028] The vehicle that includes the telematics system performing the method can be a so-called "EGO" vehicle, that is, the one traveling on the road whose speed limit is being determined from other vehicles involved in a situation. Each other vehicle involved in the method can be a so-called "remote" vehicle, that is, another vehicle that is at a given time in the vicinity of the so-called EGO vehicle. The EGO vehicle and one or more other remote vehicles can be any type of vehicle, for example, configured to include a telematics system (for example, a car, a truck, or a motorcycle).

[0029] The telematics systems of the EGO vehicle and / or remote vehicles may use V2X communication technology (acronym for "Vehicle-to- "Everything" (Vehicle-to-Everything) allows a vehicle to exchange information with any other system using this technology. For example, a V2X telematics system can exchange information with the telematics systems of other vehicles, or with roadside equipment such as traffic lights, camera systems, or pedestrian smartphones. Sending and receiving S10 information can be done using this V2X communication technology, for example, via direct (vehicle-to-vehicle) and / or indirect ("uu link" or cellular) links.

[0030] The method can be executed while the vehicle is in motion. All steps of the method can be repeated by the method while the vehicle is in motion. For example, the method can include repeating all the steps each time the EGO vehicle receives new information sent by another vehicle. The steps of the method can then be repeated, incorporating this new information received. The estimated speed limit can in this case either remain the same between two repetitions or change, for example, if the speed limit on the road changes. The details given below apply to each repetition of the steps of the method.

[0031] The vehicle (referred to as EGO) and the other vehicles (referred to as distant) travel on the same section of road. The road on which they travel can be any type of road on which vehicles are permitted to travel and on which a speed limit exists, such as a local road, a departmental road, a national road, a European road, a motorway, an emergency lane, a siding, a weaving lane, a metropolitan road, a ring road, a major road, a cul-de-sac, a 30 km / h zone, or a secondary road. The section of road can be a portion of this road, for example, one on which the speed limit is constant and on which vehicles travel in the same direction. On this section of road, a speed limit exists, and this corresponds to the maximum speed at which vehicles are permitted to travel on this road.It may depend on events on the road (such as roadworks) or current weather conditions. The process estimates this speed limit at step S40.

[0032] Each of one or more other vehicles (referred to as remote vehicles) can be configured to send information, including the respective speed limit value detected by the vehicle's respective detection system, to the vehicle implementing the method (referred to as EGO). For example, each remote vehicle may include a telematics system and may be configured to detect, for example continuously, the speed limit it is traveling in using its respective speed limit detection system, and send signals, for example regularly. including the speed limit it detects for other vehicles traveling in the same lane around it, for example within a perimeter that depends on the range of the signals sent. These other vehicles include the EGO vehicle, which receives, at step S10, the information sent by each of the one or more remote vehicles. The information can be sent in messages exchanged between the vehicles, for example, V2X messages.Each other vehicle can be configured to send this information via a V2X message, for example, a standardized V2X message, an update to an existing standardized V2X message, and / or a non-standardized V2X message that includes information fields for each piece of information (the respective speed limit value detected by the vehicle's detection system, the lane segment where the detected speed limit value applies, the confidence level for the respective speed limit value, the vehicle's current speed, and / or vehicle category). Each telematics system can include a transmitting / receiving antenna configured to send and receive signals from other vehicles, and means for encoding or decoding the information contained in the sent and received signals.

[0033] The respective speed limit detection systems of one or more other so-called remote vehicles, or of the so-called EGO vehicle (if it has one), can be of any type. For example, speed limit detection systems can be based on image processing or map positioning. Such systems can be called ISA systems (an acronym for "Intelligent Speed ​​Assistant"). In the case of image processing, the vehicle may include a camera, and the detection system can be configured to detect a speed limit value by processing images taken by this camera.For example, the camera can be configured to take images towards the front of the vehicle, and the detection system can be configured to analyze these images, for example using an algorithm, in order to detect speed limit signs encountered, as well as the speed limit displayed on these detected signs.

[0034] In the case of map-based localization, the detection system can be configured to detect a speed limit value by analyzing the vehicle's location on a map indicating speed limits. The map may include representations of traffic lanes (for example, of the region in which the vehicle is located) annotated with the speed limits imposed on those lanes. The analysis may include determining the vehicle's position on a lane represented on the map and determining the speed limit of the lane on which the vehicle's position is determined (for example, the limits may (to be annotated on the map). The map can be saved locally on the vehicle, or downloaded from a remote server by the vehicle.

[0035] Other detection systems can perform both types of detection (camera image analysis and map localization), for example, to increase robustness in detection. In all cases, the detection system can also be configured to record detected speed limits and transmit them to the vehicle, for example, to the centralized driver assistance system, which can then send them to other vehicles traveling nearby using the vehicle's telematics system. Examples of such systems include the system developed by the ADASIS consortium (an acronym for "Assistance Systems Interface Specifications").

[0036] At step S10, the vehicle's telematics system receives information sent by each other vehicle. This step can be performed continuously. For example, the other so-called remote vehicles can be configured to regularly send signals including this information at a given frequency, and all these signals can be received by the vehicle's telematics system. The S10 reception can include recording the information received from each other vehicle, for example, in the vehicle's memory. When the other so-called remote vehicles are configured to regularly send this information, the vehicle's recording can retain only the last information sent by each other so-called remote vehicle.For example, for each other so-called remote vehicle, the information sent may include an identifier of the other vehicle, and the recording may include a recording of the identifier and the information sent or, when this identifier is already stored in memory, an overwriting of the information already stored for this identifier by the new information received for this identifier. The S10 reception may include, prior to recording, a decoding of the received signals to determine the information sent by each other so-called remote vehicle.

[0037] In examples, the information sent may include any other type of information. For example, the information sent may include, for at least one other so-called distant vehicle, a confidence index on the respective speed limit value sent, a current speed value, and / or a vehicle category. The confidence index may represent the level of confidence in the respective speed limit value sent by the other so-called distant vehicle (for example, as a percentage). This confidence index may be determined by the respective detection system of the other so-called distant vehicle, for example, at the time of speed limit detection. For example, this index may be high when image processing is performed The probability of detecting a sign is certain, and conversely, it can be low when the sign is not precisely detected (for example, only half of a sign was visible in the analyzed image). The current speed value can be the speed at which the other, or remote, vehicle is currently traveling, for example, at the moment it sends the information. This current speed value can be the speed measured by the other, or remote, vehicle and displayed to the driver in real time. The vehicle category is the category of the other, or remote, vehicle. This category can be from a predetermined set of categories. The category can represent any type of characteristic of the other, or remote, vehicle, such as its level of automated driving. For example, the predetermined set of categories could include an autonomous vehicle category, a manual vehicle category, and / or a semi-autonomous vehicle category.The speed limit estimation at step S40 can take this other information into account. For example, the estimation can use confidence levels to weight the impact of the respective received speed limit values ​​on the estimated speed limit. Alternatively or additionally, the estimation can use, for at least one vehicle, the current speed and vehicle category to determine the speed limit value considered by the vehicle (the received value then being deduced from the other received information). The determination of the speed limit value can include an estimation of the acceleration initiated by the vehicle in the case of an autonomous vehicle.

[0038] In step S40, the vehicle estimates the speed limit for the lane segment based on each received speed limit value. The estimation may consider all received speed limit values ​​from all other, so-called distant, vehicles. When other, so-called distant, vehicles regularly send information, the estimation may consider the speed limit values ​​included in the latest information sent by each other vehicle (i.e., the most recently detected speed limit values). The estimation may include calculating the probability of the speed limit given the received speed limit value(s). For example, the speed limit may be that of the majority of the respective values.

[0039] In some examples, several other, so-called remote, vehicles may each send a respective speed limit value. These respective speed limit values ​​are received by the vehicle at step S10. The respective speed limit values ​​may all be identical, or some values ​​may differ from one another. The estimation S40 of the speed limit value may include the application of a consensus algorithm that takes as input the respective speed limit values ​​detected by the other vehicles and provides as output the speed limit. estimated. The method can apply any consensus algorithm. For example, the consensus algorithm can be configured to determine, from among the set of received respective values, which value represents the consensus or majority, and output that value. In examples, the respective values ​​provided by the detection systems can each belong to a respective limit from a predetermined set of limits, for example, representative of the regulations in force in the locality of the road on which the vehicle is traveling. For example, the set could include a "30 km / h" limit, a "50 km / h" limit, a "70 km / h" limit, an "80 km / h" limit, a "90 km / h" limit, a "110 km / h" limit, and a "130 km / h" limit.In this case, the algorithm can be configured to determine which of these limitations in the set represents a consensus or the majority of the respective values ​​received from other, so-called distant, vehicles. The estimated speed limit can be included in this predetermined set of limitations.

[0040] In examples, the algorithm can also output a confidence index for the estimated speed limit. The confidence index can represent the level of confidence in the estimated speed limit. The confidence index can correspond to the calculated probability that the speed limit is the estimated one. For example, the confidence index can correspond to the percentage of other so-called distant vehicles that detected this speed limit value. The consensus algorithm can be configured to calculate the confidence index at the same time as the speed limit. When the information sent by at least one other so-called distant vehicle includes confidence indices, these indices can also be taken into account when calculating the confidence index for the provided speed limit.For example, the consensus algorithm can apply different weights to the respective received values ​​based on the confidence levels of those values. These weights can then be used to adjust the algorithm's emphasis on the respective received values ​​in the provided speed limit estimate. Alternatively or additionally, the consensus algorithm can apply weights that are a function of the distribution of the received values. For example, the consensus algorithm can assign a lower weight to each value for a vehicle whose speed limit is significantly different from the other received values ​​(or omit that value altogether).

[0041] In examples, when the information sent includes, for at least one other so-called distant vehicle, a current speed value and / or the vehicle category, this information can be taken into account by the consensus algorithm to improve the estimation. This information can be taken into account in any way. For example, when at least one other vehicle is For autonomous or semi-autonomous vehicles, the method may include determining the speed limit considered by at least one other vehicle based on its current speed, and optionally, an acceleration of at least one other vehicle extracted from information sent by that vehicle. This determined speed limit may be the speed limit value considered by the consensus algorithm for at least one other vehicle.

[0042] In some examples, the method may include, between steps S10 and S40 discussed with reference to [Fig. 1], one or more of the steps in the flowchart of [Fig. 2]. In these examples, the vehicle implementing the method may also include a speed limit detection system. In this case, the method may include a detection S20 of a speed limit value by the vehicle's detection system. The detection may be performed by the speed limit detection system as discussed previously, i.e., for example, by analyzing images taken by a camera and / or by locating the vehicle on a map.

[0043] When the vehicle detection system detects a speed limit, the speed limit estimation may take into account the speed limit value detected by the vehicle detection system. For example, the estimation may consider the detected speed as an additional speed limit value, i.e., in the same way as the value(s) detected by one or more other vehicles (i.e., as the respective speed limit value(s) received). Alternatively, the estimation may consider the detected speed as the estimated speed limit in some cases, while in others it may consider it along with the speed(s) detected by one or more other vehicles, or it may ignore the speed limit altogether (only the speed(s) detected by one or more other vehicles being considered in this case).In all cases, the speed limit detected by the vehicle's detection system improves upon the provided estimate of the speed limit.

[0044] For example, the vehicle detection system can also output a confidence index for the detected speed. In this case, the method can include a determination S31, by the vehicle detection system, of a confidence index for the detected speed limit value. The method can then include a determination S32 that the determined confidence index is below a predetermined confidence threshold (e.g., 90 or 95%), and then perform the estimation S40 from the respective speed limit value received from each other so-called distant vehicle (in addition to that detected by the EGO vehicle or not). This increases the robustness of the system while optimizing the computational effort. Indeed, the estimation takes into account the other value(s) received from one or more other so-called distant vehicles only when the confidence index for the value measured by the system is low.Otherwise, the estimation may, for example, consider the speed. The detected speed limit is the estimated limit. The S40 estimate can therefore be conditioned by the value of the confidence index. In other examples, the S40 estimate may not be conditioned, and the method may estimate the speed limit from the respective speed limit value received from each other so-called distant vehicle at each time, i.e., independently of the value of the confidence index (the method then not including steps S31 and S32).

[0045] In examples, alternatively or additionally, when the confidence level is below another predetermined threshold (lower than the first) (for example, 30 or 50%), the estimation may ignore the detected speed. In this case, the estimation may be based solely on the other value(s) received from one or more other so-called distant vehicles.

[0046] Examples will now be described with reference to Figures 3 to 6.

[0047] Figure 3 illustrates an example of a process flowchart. In this example, when the vehicle includes a speed limit detection system, and the process begins by detecting a speed limit value (S20) by the vehicle's detection system, the estimation can be based solely on this value detected by the vehicle's detection system. For example, the speed limit value can be detected (S31) by the detection system with a confidence level exceeding a predetermined acceptable threshold (e.g., 90%). In this case, the estimated limit is the detected value, and the process can include a display (S50) and a transmission (S60) of the estimated limit to other vehicles. The transmission (S60) of the estimated speed limit to other vehicles improves speed limit detection for these other vehicles because they can take this additional estimate into account when assessing the speed limit.

[0048] Alternatively, that is, when the vehicle does not include a speed limit detection system, or when the speed limit value is detected S31 by the detection system with a confidence level lower than the predetermined acceptable threshold, the method may include step S10 of receiving information sent by all other surrounding vehicles. At this point, upon each receipt of information from another vehicle, the method includes a determination S70 of whether the message includes information relating to the movement of the other vehicle and the category of the other vehicle (for example, whether the V2X message containing the information is of type CAM / CBSM / BSM). If so, the method includes a determination of the speed limit from the information provided by the other vehicle by following steps S71 to S75.

[0049] The method begins by extracting S71 the information relating to the movement of the other vehicle and the category of the other vehicle present in the information sent. The information relating to the movement may include a speed The method involves identifying the other vehicle's current speed and assigning a confidence level to that current speed. It then includes S72 identification of the other vehicle based on its category (autonomous vehicle, manual, etc.). Next, it extracts S73 information about the current acceleration when this information is available in the message (for example, the activation of an "Adaptive Cruise Control" feature). Finally, it assigns a confidence level based on the confidence level of the current speed, the other vehicle's identity, and the extracted acceleration information.The process then includes an S75 insertion into a first data buffer for the other vehicle, the inserted data comprising an identifier of the other vehicle, the assigned confidence index, the current speed, the confidence index on that current speed, the identity of the other vehicle, and the extracted acceleration information. When data already exists for the identifier of the other vehicle, the insertion may include replacing the existing data with the newly extracted data.

[0050] Alternatively, when the message does not include information relating to the movement of the other vehicle and its category, the method includes determining whether the information includes a respective speed limit value and an associated confidence level. If not, the method ignores the message. Otherwise, the method includes an S77 data extraction of the received information, the extracted data including an identifier of the other vehicle, the respective speed limit value, and the associated confidence level. The method then includes inserting the extracted data for the other vehicle into a second buffer. When data is already present for the identifier of the other vehicle, the insertion may include replacing the existing data with the newly extracted data.

[0051] The method then includes the vehicle's S40 estimation of a speed limit for the lane segment based on all the data contained in the two buffers. The S40 estimation begins with a cross-check of the data in the two buffers using the vehicle identifiers. In other words, when data exists in both buffers for the same vehicle, the method selects one of the two buffers (for example, the first buffer) and only includes in the calculation for that vehicle the data stored in the selected buffer. The S40 estimation then includes a re-evaluation of a confidence index for the value considered for each vehicle. The re-evaluation may include applying a lower weight to the value or omitting it when it is far from the other values. detected. The S40 estimation then includes an S43 calculation of the speed limit and its associated confidence index based on the re-evaluated confidence indices. The S43 calculation may include the application of the consensus algorithm.

[0052] Once the speed limit is estimated, the method includes a display S50 and a transmission S60 to other vehicles of the estimated speed limit. The transmission S60 may include sending a V2X message to other vehicles including the estimated speed limit and its associated confidence level.

[0053] Figures 4 to 6 illustrate a practical example of the method. The method guarantees a 90% success rate. It also solves the aforementioned problems with current detection systems. In particular, the method relies on V2X messages containing information on the speed limit detected by each connected vehicle (via standardized V2X messages, for example). The vehicles can be equipped with cameras conforming to the ISA standard. The method estimates the speed limit based on these V2X messages exchanged with other vehicles in the vicinity. The method allows the dissemination of information on the speed limit and its confidence level to all surrounding vehicles within a given geographic area via V2X messages.The Ego vehicle receives all V2X speed limit information from surrounding vehicles and calculates the confidence level associated with this information by following the rules of its internal consensus algorithm. The Ego vehicle is configured to broadcast speed limit information with an updated confidence level. One advantage of this process is that the collaborative system will continue to function even if the front cameras fail or if the information extracted from the map (for example, in the case of the system developed by the ADASIS consortium) is unavailable.

[0054] Figure 4 illustrates an example of a problematic situation for systems of Current detection methods are camera-based. In this example, vehicle Ego 101 is a car whose map is not up to date. Vehicle 201, in front of Ego 101, is obstructing speed limit signs 301, and the camera is unable to detect them. The other vehicles, 202, 203, 204, and 205, which are in front of vehicle 301, each have a detection system (called an ISA system) that has determined the speed limit displayed on sign 301. Table 1 below shows the respective speed limit values ​​detected by the vehicles' respective detection systems in this situation.

[0055] [Tables] Vehicle Speed ​​Limit Confidence Index Vehicle considered distant 1 80 90% Vehicle described as remote 2 60 40% Vehicle described as remote 3 80 70% Vehicle described as remote 4 80 90% Vehicle described as Ego Undetermined Undetermined

[0056] Figures 5 and 6 illustrate the steps of the process that allow the speed limit to be estimated even in the problematic situation illustrated in [Fig. 4]. [Fig. 4] illustrates in particular the reception of information sent by the other vehicles 202, 203, 204, and 205. These other vehicles 202, 203, 204, and 205, which are located in front of vehicle 301, each have a detection system (called an ISA system) that has determined the speed limit displayed on sign 301. These other vehicles 202, 203, 204, and 205 are each configured to broadcast a V2X message containing information on the speed limit they have detected. Each other vehicle also sends a confidence index for the detected speed limit. The Ego vehicle receives all 10 V2X messages, each containing the speed limit and the level of confidence detected by each of the other vehicles 202, 203, 204 and 205.

[0057] Based on an internal consensus algorithm, the Ego vehicle is configured to calculate / determine its own speed limit (the most probable speed limit associated with a confidence level). In particular, the method includes estimating the speed limit by applying a consensus algorithm, the algorithm being applied to the speed limits and associated confidence levels detected by the other vehicles 202, 203, 204, and 205. The algorithm can output an estimated speed limit and an associated confidence level. This speed limit can be displayed to the driver. In this case, the method further includes, after the estimation S40, a display S50 to the driver of the vehicle of the estimated speed limit. For example, the limit can be displayed on a screen on the dashboard or projected onto a corner of the windshield.Table 2 below shows the respective speed limit values ​​detected by the other vehicles, and the estimated speed limit value provided by the algorithm for vehicle Ego. In this example, the algorithm outputs the value "80" because this is the most common (3 out of 4 vehicles give this value). The algorithm can also weight the received values ​​using the associated confidence indices also sent (which are shown in Table 1), in order, for example, to exclude or reduce the impact of the value 60 received only from vehicle 2, which has a low confidence index.

[0058] [Tables2] Vehicle Speed ​​limit Vehicle said to be distant 1 80 Vehicle said to be distant 2 60 Vehicle said to be distant 3 80 Vehicle said to be distant 4 80 Vehicle said to be Ego 80

[0059] As illustrated in [Fig. 6], the Ego vehicle can be configured to then broadcast the estimated speed limit and the associated updated confidence level. In this case, the method can further include, after the S40 estimation, a S60 transmission to each other vehicle 202, 203, 204, and 205 of the vehicle's estimated speed limit and, optionally, the associated confidence level. For example, the Ego vehicle can send a V2X message containing this information to each other vehicle 202, 203, 204, and 205.

Claims

Demands

1. A method implemented in a vehicle, for building a collaborative network between vehicles to determine the speed limit, the vehicle traveling on a portion of road with one or more other vehicles, each other vehicle comprising a respective speed limit detection system, the method comprising: • a reception (S 10) of information sent by each other vehicle including, for each other vehicle, a respective speed limit value detected by the respective detection system of the other vehicle as well as the portion of road where the detected speed limit value is applicable; and • an estimation (S40), by the vehicle, of a speed limit for the portion of road from each respective received speed limit value.

2. Method according to claim 1, wherein a sending (S 10a), by each other vehicle, of a respective value of speed limit detected by the detection system of the other vehicle as well as the portion of lane where the detected speed limit value is applicable is a prerequisite to acceptance (S 10).

3. Method according to claim 1, wherein each other vehicle requests a transmission (S 10a), by each other vehicle, of a respective speed limit value detected by the detection system of the other vehicle as well as the portion of lane where the detected speed limit value is applicable is a prerequisite to acceptance (S 10).

4. A method according to any one of claims 1 to 3, wherein the vehicle includes a speed limit detection system, the method comprising: • a detection (S20) of a speed limit value by the vehicle's detection system, the estimation of the speed limit taking into account the speed limit value detected by the vehicle's detection system.

5. A method according to claim 4, wherein the method further comprises, before the estimation (S40) of the speed limitation: • a determination (S31), by the vehicle detection system, of a confidence index on the detected speed limit value; and • a determination (S32) that the determined confidence index is less than a predetermined confidence threshold.

6. A method according to any one of claims 1 to 5, wherein the one or more other vehicles include several other vehicles, the estimation (S40) of the speed limit value comprising the application of a consensus algorithm taking as input the respective speed limit values ​​detected by the other vehicles and providing as output the estimated speed limit, and optionally a confidence index.

7. A method according to any one of claims 1 to 6, wherein the information sent includes, for at least one other vehicle: • a confidence index on the respective value of speed limit sent, • a value of actual speed, and / or • a vehicle category, the speed limit estimate taking into account the confidence index, actual speed and / or vehicle category sent for at least one other vehicle.

8. A method according to any one of claims 1 to 7, wherein the method further comprises, after the estimation (S40): • sending (S60), to each other vehicle, the speed limit estimated by the vehicle

9. Computer program for vehicle system comprising instructions which, when the program is executed by a processor, cause the processor to implement the method according to any one of claims 1 to 8.

10. System for vehicle, the system being configured to perform the method according to any one of claims 1 to 8.

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