Vehicle speed control method, device and computer equipment in rainstorm conditions

Abstract

The present application relates to a method, device, computer equipment, storage medium and computer program product for controlling vehicle speed under heavy rain conditions. The method includes: obtaining a data structure of traffic simulation data and a path planning data structure; obtaining lane data, AOI data, and vehicle data respectively; starting traffic simulation, updating AOI data with a preset simulation step length, and determining a target vehicle in the AOI according to the updated AOI data; obtaining path planning data corresponding to the target vehicle, and storing the path planning data in the path planning data structure; updating lane data and vehicle data according to the stored path planning data, reading heavy rain data, and determining the depth of water accumulation corresponding to the heavy rain data; simulating traffic conditions under heavy rain conditions, and obtaining the maximum allowable speed of vehicles on lanes under heavy rain conditions. The present method can accurately control the vehicle speed under heavy rain conditions.

Description

Technical Field

[0001] The present application relates to the technical field of traffic simulation, and in particular to a method, device, computer equipment, storage medium and computer program product for controlling vehicle speed in rainstorm conditions. Background Art

[0002] With the acceleration of urbanization, urban traffic problems are becoming increasingly serious. Extreme weather events such as rainstorms have a serious impact on urban traffic, leading to problems such as road waterlogging and traffic accidents. Therefore, in order to avoid a large number of traffic problems in the future, it is very important to simulate the traffic conditions under sudden rainstorms so that the vehicle speed can be controlled under rainstorms.

[0003] In traditional technology, the speed of vehicles under heavy rain is controlled by building a heavy rain waterlogging model, simulating a single vehicle on a simulated road section under heavy rain scenarios, and obtaining heavy rain data that will not cause accidents, and then improving flood prevention measures through heavy rain data. However, this method is not accurate and cannot accurately control the speed of vehicles under heavy rain. Summary of the invention

[0004] Based on this, it is necessary to provide an accurate method, device, computer equipment, computer-readable storage medium and computer program product for controlling vehicle speed under heavy rain conditions in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for controlling the speed of a vehicle in a rainstorm, the method comprising:

[0006] Acquire a data structure of traffic simulation data and a path planning data structure, wherein the data structure of the traffic simulation data includes a lane data structure, an AOI (area of ​​interest) data structure, and a vehicle data structure;

[0007] The acquired traffic simulation data is stored correspondingly according to the data structure of the traffic simulation data, and lane data, AOI data, and vehicle data are obtained respectively;

[0008] Starting a traffic simulation, updating the AOI data with a preset simulation step length, and determining a target vehicle in the AOI according to the updated AOI data, wherein the target vehicle is a vehicle in the AOI that makes a navigation request;

[0009] Acquire path planning data corresponding to the target vehicle, and store the path planning data in the path planning data structure;

[0010] updating the lane data and the vehicle data according to the stored path planning data, reading the rainstorm data, and determining the water depth corresponding to the rainstorm data;

[0011] Traffic conditions under heavy rain conditions are simulated based on the updated lane data, the updated vehicle data, the heavy rain data, and the water depth corresponding to the heavy rain data to obtain the maximum allowable driving speed of the vehicle on the lane under heavy rain conditions.

[0012] In one embodiment, before obtaining lane data, AOI data, and vehicle data according to the traffic simulation data corresponding to the data structure and stored respectively, the method further includes:

[0013] Allocate memory space to the data structure of the traffic simulation data, and initialize the data structure of the traffic simulation data in the allocated memory space.

[0014] In one embodiment, starting the traffic simulation, updating the AOI data with a preset simulation step length, and determining the target vehicle in the AOI according to the updated AOI data comprises:

[0015] Obtaining a preset simulation time and a preset simulation step length, and starting a traffic simulation according to the preset simulation time and the preset simulation step length;

[0016] When the simulation time is less than the preset simulation time, the AOI data is updated with a preset simulation step size, and a target vehicle in the AOI is determined according to the updated AOI data.

[0017] In one embodiment, obtaining the path planning data corresponding to the target vehicle and storing the path planning data in the path planning data structure includes:

[0018] Get the global navigation instance;

[0019] Establishing a communication link between the target vehicle and the external navigation module through the global navigation instance, and uploading a navigation request to the external navigation module;

[0020] Receive the path planning data corresponding to the target vehicle returned by the navigation module through the communication link, and store the path planning data in the path planning data structure.

[0021] In one embodiment, before reading the rainstorm data and determining the water depth corresponding to the rainstorm data, the method further includes:

[0022] Import the storm data and cache the storm data using an unordered map.

[0023] In one embodiment, the traffic condition under heavy rain is simulated according to the updated lane data, the updated vehicle data, the heavy rain data, and the water depth corresponding to the heavy rain data to obtain the maximum allowable speed of the vehicle on the lane under heavy rain, including:

[0024] Simulating traffic conditions under heavy rain conditions according to the updated lane data, the updated vehicle data, the heavy rain data, and the water depth corresponding to the heavy rain data;

[0025] determining a global speed limit of the vehicle on the lane according to the rainstorm data;

[0026] The maximum permissible speed of the vehicle on the lane under heavy rain conditions is determined according to the global speed limit and the water depth corresponding to the heavy rain data.

[0027] In a second aspect, the present application also provides a vehicle speed control device in rainstorm conditions, the device comprising:

[0028] A data structure acquisition module, used to acquire a data structure of traffic simulation data and a path planning data structure, wherein the data structure of the traffic simulation data includes a lane data structure, an AOI data structure, and a vehicle data structure;

[0029] A data acquisition module, used to store the acquired traffic simulation data in accordance with the data structure of the traffic simulation data, and obtain lane data, AOI data, and vehicle data respectively;

[0030] A navigation vehicle determination module is used to start a traffic simulation, update the AOI data with a preset simulation step length, and determine a target vehicle in the AOI according to the updated AOI data, wherein the target vehicle is a vehicle in the AOI that makes a navigation request;

[0031] A path planning module, used for acquiring path planning data corresponding to the target vehicle, and storing the path planning data into the path planning data structure;

[0032] A rainstorm data reading module, used to update the lane data and the vehicle data according to the stored path planning data, read the rainstorm data, and determine the water depth corresponding to the rainstorm data;

[0033] The traffic simulation module is used to simulate the traffic conditions under heavy rain conditions according to the updated lane data, the updated vehicle data, the heavy rain data, and the water depth corresponding to the heavy rain data, so as to obtain the maximum allowable driving speed of the vehicle on the lane under heavy rain conditions.

[0034] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0035] Acquire a data structure of traffic simulation data and a path planning data structure, wherein the data structure of the traffic simulation data includes a lane data structure, an AOI data structure, and a vehicle data structure;

[0036] The acquired traffic simulation data is stored correspondingly according to the data structure of the traffic simulation data, and lane data, AOI data, and vehicle data are obtained respectively;

[0037] Starting a traffic simulation, updating the AOI data with a preset simulation step length, and determining a target vehicle in the AOI according to the updated AOI data, wherein the target vehicle is a vehicle in the AOI that makes a navigation request;

[0038] Acquire path planning data corresponding to the target vehicle, and store the path planning data in the path planning data structure;

[0039] updating the lane data and the vehicle data according to the stored path planning data, reading the rainstorm data, and determining the water depth corresponding to the rainstorm data;

[0040] Traffic conditions under heavy rain conditions are simulated based on the updated lane data, the updated vehicle data, the heavy rain data, and the water depth corresponding to the heavy rain data to obtain the maximum allowable driving speed of the vehicle on the lane under heavy rain conditions.

[0041] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0042] Acquire a data structure of traffic simulation data and a path planning data structure, wherein the data structure of the traffic simulation data includes a lane data structure, an AOI data structure, and a vehicle data structure;

[0043] The acquired traffic simulation data is stored correspondingly according to the data structure of the traffic simulation data, and lane data, AOI data, and vehicle data are obtained respectively;

[0044] Starting a traffic simulation, updating the AOI data with a preset simulation step length, and determining a target vehicle in the AOI according to the updated AOI data, wherein the target vehicle is a vehicle in the AOI that makes a navigation request;

[0045] Acquire path planning data corresponding to the target vehicle, and store the path planning data in the path planning data structure;

[0046] updating the lane data and the vehicle data according to the stored path planning data, reading the rainstorm data, and determining the water depth corresponding to the rainstorm data;

[0047] Traffic conditions under heavy rain conditions are simulated based on the updated lane data, the updated vehicle data, the heavy rain data, and the water depth corresponding to the heavy rain data to obtain the maximum allowable driving speed of the vehicle on the lane under heavy rain conditions.

[0048] In a fifth aspect, the present application also provides a computer program product, including a computer program, which implements the steps of the above method when executed by a processor.

[0049] Acquire a data structure of traffic simulation data and a path planning data structure, wherein the data structure of the traffic simulation data includes a lane data structure, an AOI data structure, and a vehicle data structure;

[0050] The acquired traffic simulation data is stored correspondingly according to the data structure of the traffic simulation data, and lane data, AOI data, and vehicle data are obtained respectively;

[0051] Starting a traffic simulation, updating the AOI data with a preset simulation step length, and determining a target vehicle in the AOI according to the updated AOI data, wherein the target vehicle is a vehicle in the AOI that makes a navigation request;

[0052] Acquire path planning data corresponding to the target vehicle, and store the path planning data in the path planning data structure;

[0053] updating the lane data and the vehicle data according to the stored path planning data, reading the rainstorm data, and determining the water depth corresponding to the rainstorm data;

[0054] Traffic conditions under heavy rain conditions are simulated based on the updated lane data, the updated vehicle data, the heavy rain data, and the water depth corresponding to the heavy rain data to obtain the maximum allowable driving speed of the vehicle on the lane under heavy rain conditions.

[0055] The above-mentioned vehicle speed control method, device, computer equipment, storage medium and computer program product under heavy rain conditions, first, obtain the data structure of traffic simulation data and the path planning data structure, the data structure includes a lane data structure, an AOI data structure and a vehicle data structure; according to the traffic simulation data corresponding to the data structure, the lane data, the AOI data, and the vehicle data are obtained respectively; start the traffic simulation, update the AOI data with a preset simulation step length, determine the vehicle in the AOI that needs to send a navigation request according to the updated AOI data, obtain the path planning data corresponding to the vehicle that needs to send a navigation request, and store the path planning data in the path planning data structure; read the heavy rain data, determine the water depth corresponding to the heavy rain data; update the lane data and the vehicle data according to the stored path planning data, and simulate the traffic conditions when the heavy rain breaks out according to the updated lane data, the updated vehicle data, the heavy rain data, and the water depth corresponding to the heavy rain data, and obtain the maximum allowable driving speed of the vehicle on the lane when the heavy rain breaks out. During the whole process, by obtaining the data structure of traffic simulation data that can store and obtain traffic simulation data, the data required for traffic simulation can be accurately obtained, the accuracy of traffic simulation can be improved, and the traffic simulation can be started to determine the target vehicle to which the navigation request needs to be sent, thereby accurately obtaining the path planning data of the target vehicle; finally, the lane data and vehicle data are updated according to the stored path planning data, and the traffic conditions under heavy rain are accurately simulated according to the updated lane data, the updated vehicle data, the heavy rain data, and the water depth corresponding to the heavy rain data, thereby accurately obtaining the maximum allowable driving speed of the vehicle on the lane under heavy rain. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A diagram showing an application environment of a method for controlling vehicle speed under heavy rain conditions in an embodiment;

[0057] Figure 2 A schematic diagram of a flow chart of a method for controlling vehicle speed in a rainstorm in an embodiment;

[0058] Figure 3 A schematic flow chart of a method for controlling vehicle speed in rainstorm conditions in another embodiment;

[0059] Figure 4 A schematic diagram of the structure of the server and client models of the external navigation module method in one embodiment;

[0060] Figure 5 A schematic flow chart of a method for controlling vehicle speed in rainstorm conditions in yet another embodiment;

[0061] Figure 6is a structural block diagram of a vehicle speed control device in a rainstorm situation in one embodiment;

[0062] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0064] The vehicle speed control method under heavy rain conditions provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the traffic simulation platform 102 communicates with the server 104 through the network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The traffic simulation platform 102 sends a vehicle speed control request to the server 104. The vehicle speed control request carries traffic simulation data, a data structure of the traffic simulation data, and a path planning data structure. The server 104 obtains the vehicle speed control request from the traffic simulation platform 102, and obtains the traffic simulation data, the data structure of the traffic simulation data, and the path planning data structure in the vehicle speed control request. The data structure of the traffic simulation data includes a lane data structure, an AOI data structure, and a vehicle data structure; according to the data structure of the traffic simulation data, the acquired traffic simulation data is stored correspondingly, and the lane data, AOI data, and the path planning data structure are obtained respectively. and vehicle data; start traffic simulation, update AOI data with a preset simulation step length, determine the target vehicle in AOI according to the updated AOI data, the target vehicle is the vehicle that makes a navigation request in AOI; obtain the path planning data corresponding to the target vehicle, and store the path planning data in the path planning data structure; update lane data and vehicle data according to the stored path planning data, read rainstorm data, and determine the depth of water accumulation corresponding to the rainstorm data; simulate the traffic conditions under rainstorm conditions according to the updated lane data, updated vehicle data, rainstorm data, and the depth of water accumulation corresponding to the rainstorm data, and obtain the maximum allowable driving speed of the vehicle on the lane under rainstorm conditions. Among them, the traffic simulation platform 102 can be, but is not limited to, a platform on various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices, and the Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0065] In one embodiment, Figure 2 As shown, a method for controlling vehicle speed in rainstorm conditions is provided. Figure 1 Taking the server 104 in the example as an example, the following steps are included:

[0066] S100, obtaining a data structure of traffic simulation data and a path planning data structure, wherein the data structure of the traffic simulation data includes a lane data structure, an AOI data structure, and a vehicle data structure.

[0067] Among them, the data structure refers to a collection of data elements that have one or more relationships with each other and the relationships between the data elements in the collection, which can be considered as a way to store and organize data in a computer. The traffic simulation data structure is used to characterize the lanes, specific areas, and vehicles in the traffic network. It includes a lane data structure, an AOI data structure, and a vehicle data structure. Among them, AOI refers to the area of ​​interest of the user, which is used to limit the area in this application. For example, AOI can be a residential area or a parking lot. The path planning data structure is used to characterize the driving path of a vehicle in a traffic simulation environment.

[0068] Specifically, the user defines the data structure of the traffic simulation data and the path planning data structure on the traffic simulation platform 102, and sends a vehicle speed control request to the server 104. The vehicle speed control request carries the data structure of the traffic simulation data and the path planning data structure. The server 104 obtains the vehicle speed control request from the traffic simulation platform 102, obtains the data structure of the traffic simulation data and the path planning data structure in the vehicle speed control request, and the data structure of the traffic simulation data includes a lane data structure, an AOI data structure and a vehicle data structure.

[0069] S200, storing the acquired traffic simulation data in correspondence with the data structure of the traffic simulation data, and obtaining lane data, AOI data, and vehicle data respectively.

[0070] Specifically, the vehicle speed control request also carries externally input traffic simulation data, and the externally input traffic simulation data is obtained. The externally input traffic simulation data is data based on a real-world map that is traversed externally, such as map data of Beijing or map data of Shanghai, and includes lane data, AOI data, and vehicle data. The acquired traffic simulation data can be stored in correspondence with the data structure of the traffic simulation data, and the lane data, AOI data, and vehicle data stored in the corresponding data structure can be obtained respectively. The means for storing the acquired traffic simulation data in correspondence with the data structure of the traffic simulation data are respectively:

[0071] 1. For lane data: After initializing the lane data structure variable that has allocated memory space, traverse all externally input lane data and read the attributes in the lane data, such as the center line, left boundary line, and right boundary line, etc., calculate the direction of each lane, initialize the center line length and direction of each lane, store lane attributes, such as the section ID, turn, length, maximum speed, etc. in the lane data structure variable, process the relationship between lanes, such as the left lane, right lane, leading lane, successor lane, etc., initialize the first and last vehicles in each lane, and store the acquired traffic simulation data according to the data structure of the traffic simulation data to obtain the final lane data stored in the lane data structure.

[0072] 2. For vehicle data: After initializing the vehicle data structure variable that has allocated memory space, it will traverse all external input vehicle data and read vehicle attributes, such as ID, length, width, maximum speed, etc., store the vehicle attributes in the vehicle array, and initialize some runtime data, such as speed, etc., store the mapping relationship between vehicle index and array subscript in the vehicleId2Index mapping, and add the vehicle to the corresponding AOI data, update the number of vehicles in the AOI, and if the vehicle has schedule data (schedules), read the schedule data and store it in the vehicle array.

[0073] 3. For AOI data: After initializing the AOI data structure variable that has allocated memory space, traverse all externally input AOI data and read the attributes of the AOI data, such as ID, area size, driving position, pass position, etc. Store the AOI attributes in the AOI data structure variable. Store the mapping relationship between the AOI index and the array subscript in the AoiID2Index mapping. Read the pass position and driving door (driving area entrance) information in the AOI. Set the number of vehicles in each AOI, as well as the starting and ending positions of the vehicle list. Traverse the vehicles in the AOI and add them to the bidirectional linked list of connected vehicles.

[0074] S300, starting a traffic simulation, updating AOI data with a preset simulation step length, and determining a target vehicle in the AOI according to the updated AOI data, where the target vehicle is a vehicle in the AOI that makes a navigation request.

[0075] Specifically, each preset simulation step can be called a time step. Start the traffic simulation. During the traffic simulation, since vehicles will move, there will be vehicles leaving the AOI and vehicles entering the AOI in each time step, and the AOI data of each time step needs to be updated. Since each vehicle only needs to initiate navigation when it departs from the AOI, it is not necessary to initiate navigation at every time step. Therefore, in a certain time step, some vehicles may be ready to depart. At this time, the vehicles ready to depart will initiate navigation. The target vehicle that makes a navigation request in the AOI can be determined based on the updated AOI data. After initiating navigation, it will obtain a section of the driving path returned by the navigation service. In the next time step, it will travel according to the obtained driving path.

[0076] S400, obtaining path planning data corresponding to the target vehicle, and storing the path planning data in a path planning data structure.

[0077] Specifically, determine whether there is a vehicle that needs to make a navigation request. If so, traverse the target vehicles that need to make a navigation request in the AOI, and output the navigation request of the target vehicle to the navigation module. The navigation module returns the path planning data corresponding to the target vehicle and stores the path planning data in the path planning data structure.

[0078] Furthermore, if there is no vehicle making a navigation request, it is considered that there is no departure vehicle in the AOI at this time, and the vehicles have already obtained the corresponding path planning data. At this time, there is no need to continue to obtain the path planning data, and the vehicle can directly drive according to the path planning data.

[0079] S500, updating lane data and vehicle data according to the stored path planning data, reading rainstorm data, and determining the water depth corresponding to the rainstorm data.

[0080] Specifically, the stored path planning data can make the vehicle travel according to the path planning data. Therefore, the vehicle data and the lane data related to the vehicle will change in each time step, that is, the lane data and the vehicle data will be updated in each time step. Updating the lane data and the vehicle data can be: according to the given vehicle attributes, road conditions and environmental parameters, the acceleration, deceleration, position update and lane change of the vehicle are calculated. In addition, it is also necessary to read the external input rainstorm data. Different rainstorm data are mapped to different water depths, and the rainstorm data corresponding to different time steps may be different, so the water depth corresponding to the rainstorm data can be determined according to the time step and lane index. If the current water depth is zero, it is considered that there is no rainstorm data in the current time step.

[0081] Furthermore, externally inputted rainstorm data can be easily and dynamically added, deleted or modified as needed, so that traffic simulation under rainstorm conditions can be performed more conveniently.

[0082] S600, simulating the traffic conditions under heavy rain conditions according to the updated lane data, the updated vehicle data, the heavy rain data, and the water depth corresponding to the heavy rain data, and obtaining the maximum allowable driving speed of the vehicle on the lane under the heavy rain conditions.

[0083] Specifically, the vehicle conditions on the lane are simulated according to the updated lane data and the updated vehicle data, and the rainstorm water accumulation conditions are simulated according to the rainstorm data and the water accumulation depth corresponding to the rainstorm data. The traffic conditions under rainstorm conditions can be accurately simulated according to the vehicle conditions on the lane and the rainstorm water accumulation conditions, so as to obtain the maximum allowable driving speed of vehicles on the lane under rainstorm conditions based on the simulated traffic conditions under rainstorm conditions, and adjust the vehicle speed according to the maximum allowable driving speed of the vehicle on the lane.

[0084] In the above-mentioned vehicle driving speed control method under heavy rain conditions, first, a data structure of traffic simulation data and a path planning data structure are obtained, and the data structure includes a lane data structure, an AOI data structure and a vehicle data structure; according to the traffic simulation data corresponding to the data structure, the lane data, the AOI data, and the vehicle data are obtained respectively; the traffic simulation is started, and the AOI data is updated with a preset simulation step size, and the vehicle in the AOI that needs to send a navigation request is determined according to the updated AOI data, and the path planning data corresponding to the vehicle that needs to send a navigation request is obtained, and the path planning data is stored in the path planning data structure; the heavy rain data is read to determine the water depth corresponding to the heavy rain data; the lane data and the vehicle data are updated according to the stored path planning data, and the traffic conditions under heavy rain conditions are simulated according to the updated lane data, the updated vehicle data, the heavy rain data, and the water depth corresponding to the heavy rain data, and the maximum allowable driving speed of the vehicle on the lane under heavy rain conditions is obtained. During the whole process, by obtaining the data structure of traffic simulation data that can store and obtain traffic simulation data, the data required for traffic simulation can be accurately obtained, the accuracy of traffic simulation can be improved, and the traffic simulation can be started to determine the target vehicle to which the navigation request needs to be sent, thereby accurately obtaining the path planning data of the target vehicle; finally, the lane data and vehicle data are updated according to the stored path planning data, and the traffic conditions under heavy rain are accurately simulated according to the updated lane data, the updated vehicle data, the heavy rain data, and the water depth corresponding to the heavy rain data, thereby accurately obtaining the maximum allowable driving speed of the vehicle on the lane under heavy rain.

[0085] Furthermore, the data structure of the traffic simulation data includes the following specific fields of various data structures:

[0086] 1. The defined vehicle data structure includes vehicle status flag, index information, vehicle attributes, vehicle motion information, vehicle physical location information, vehicle travel information, vehicle relationship list and vehicle list in AOI, etc. The vehicle data structure is used to calculate the driving status and interaction of vehicles during traffic simulation.

[0087] Specifically, the fields in the vehicle data structure may include:

[0088] 1.1. Vehicle status flags: valid (whether it is valid), inLane (whether it is in the lane) and canRoute (whether navigation is possible);

[0089] 1.2. Index information: index (vehicle index), AoiId (AOI ID);

[0090] 1.3. Vehicle attributes: A VehicleAttribute structure, including vehicle length, width, maximum speed, maximum acceleration, common acceleration, maximum braking acceleration, common braking acceleration, minimum interval, and length required for lane change;

[0091] 1.4. Vehicle motion information: including snapshot (equivalent to the backup within the previous simulation time step) and runtime VehicleMotion structures. Both VehicleMotion structures contain lane index, lane pointer, speed, coordinates, total lane change length, completed lane change length, whether there is a shadow vehicle, left move sign, distance to the end point, lane change status, etc.

[0092] 1.5. Physical location information: x (horizontal coordinate), y (vertical coordinate) and direction;

[0093] 1.6. Trip information: a Schedule array, which contains a series of Trips (predetermined travel plans), where the Schedule structure contains a set of Trip arrays, Trip quantity, loop count, expected departure time and waiting time, and the Trip structure contains the destination AOIID, expected departure time and waiting time;

[0094] 1.7. Relationship list: relation array and shadow_relation array, used to represent the relationship between the vehicle and its front, back, left, center and right;

[0095] 1.8. Vehicle linked list in AOI: pre (previous vehicle) and next (next vehicle). pre and next are pointers to two data structure types of vehicle data structures, pointing to the previous vehicle and the next vehicle of the vehicle respectively. They are used to establish a vehicle linked list in AOI and connect the vehicles in AOI.

[0096] 2. The defined lane data structure includes lane status, lane index information, lane attributes, lane geometry information, adjacent lane information, and a list of vehicles on the lane, etc., which are used to calculate the driving status and interaction of vehicles on the lane in traffic simulation. Specifically, the fields in the lane data structure may include:

[0097] 2.1. Lane status flag: valid (whether valid);

[0098] 2.2. Lane index information: index (lane index);

[0099] 2.3. Lane attributes: turn (road type), length (length), max_speed (maximum speed), dirction (direction, dirction = 1, 2, 3, 4 correspond to up, down, left, right, i.e. north, south, west, east);

[0100] 2.4. Lane geometry information: width (lane width), CenterLine (xy coordinates of each point on the centerline), LeftBorderLine (xy coordinates of each point on the left borderline), RightBorderLine (xy coordinates of each point on the right borderline), centerDirections (direction of each segment of the centerline segment), centerLengths (list of lengths corresponding to the centerline points), and centerLineSize (number of valid data in the centerLengths and centerDirections arrays);

[0101] 2.5. Adjacent lane information: left_lane_index (left lane index), left_lane (left lane pointer), right_lane_index (right lane index), right_lane (right lane pointer), before_lane_size (number of preceding lanes), before_lanes_index (preceding lane index array), before_lanes (preceding lane pointer array), next_lane_size (number of succeeding lanes), next_lanes_index (successor lane index array) and next_lanes (successor lane pointer array);

[0102] 2.6. The vehicle list on the lane: first (list head pointer), end (list tail pointer), num_vehicles (number of vehicles), insert_num (number of insertions), remove_num (number of deletions), insert_buffer (newly inserted vehicles, merged and sorted and then connected to the main chain) and remove_buffer (deleted vehicles, need to be removed from the main chain).

[0103] 3. The defined AOI data structure is used to represent a specific area in the traffic network simulation, including AOI identification, AOI and road network connection information, AOI original location information, AOI and road network connection point information on its own boundary, area, AOI vehicle list information, and AOI in and out of vehicle information, etc., which is used to calculate the driving status and interaction of vehicles in AOI in traffic simulation. Specifically, the fields in the lane data structure may include:

[0104] 3.1、AOI identification: AoiID;

[0105] 3.2. AOI and road network connection information: DrivingPositionSize (number of connection points between AOI and road network), DrivingPositions (connection points between AOI and road network), lanS (stores the mapping of laneID of connection points between AOI and road network to coordinate s);

[0106] 3.3. AOI original position information: PositionSize (number of AOI original positions), Positions (AOI original position, if there is only one value, it is the point where the AOI is located, otherwise it is the boundary of the AOI polygon);

[0107] 3.4. Information about the connection points on the boundary of the AOI when it is connected to the road network: DrivingGateSize (the number of connection points on the boundary of the AOI when it is connected to the road network), DrivingGates (the connection points on the boundary of the AOI when it is connected to the road network, corresponding to DrivingPositions by index);

[0108] 3.5, AOI area: area (AOI area);

[0109] 3.6. Vehicle list information in AOI: vehicleNum (number of vehicles in AOI), vehicleListStart (head pointer of vehicle list in AOI), vehicleListEnd (end pointer of vehicle list in AOI). The middle part of the vehicle list in AOI can be connected through the vehicle list field in AOI in the vehicle data structure.

[0110] 3.7. Information about vehicles entering and exiting AOI: insertNum (the number of vehicles entering AOI), insertBuff (the pointer storing the vehicles entering AOI), removeNum (the number of vehicles exiting AOI), removeBuff (the pointer storing the vehicles exiting AOI).

[0111] From the above, we can know that the vehicle data structure includes basic vehicle attributes, the relationship between vehicles, vehicle motion status, vehicle scheduling and vehicle path planning; the lane data structure fields include basic lane attributes, lane geometric features, lane connections and vehicle management in the lane; the AOI data structure includes AOI attribute geometric features, AOI and lane road network connection and management of vehicles in the AOI. Therefore, the constructed lane, AOI, and vehicle data structures can be used to describe the traffic network characteristics, specific area characteristics and vehicle characteristics of the lane respectively.

[0112] 4. In addition, some other data structures are defined in the data structure of traffic simulation data, such as:

[0113] 4.1. Store the vehicle index (vehicle index in the vehicle data structure) and quantity (AoiVehicle) in the AOI;

[0114] 4.2. Record the metadata of the vehicle route, such as the ID of the starting and ending AOI (RouteMetaInfo);

[0115] 4.3、Support the parallel construction of routing lists on the device side, including vehicle index, start index, route length and other information (RouteInfo);

[0116] 4.4. Vehicle perception module, including the vehicle pointer and the intention to change lanes left or right (localController); records the next lane information, including the pointer and relative distance (laneAhead);

[0117] 4.5. Record lane change related information, including activation status, lane change target, front / rear vehicle, etc. (laneChange);

[0118] 4.6. Record driving behavior information, such as acceleration and lane change length (drivingAction).

[0119] In addition, in order to make the vehicle not rely on the road network topology during the calculation process, the vehicle's path planning attributes need to be self-describing. The defined path planning data VehicleRoute structure includes: the number of road segments (size), the starting AOI pointer (startAoi), the end AOI pointer (endAoi) and a Segment list (segment), where the Segment structure contains the lane index (laneIndex), the lane pointer (lane), the next lane type (nextLaneType), the distance to the end point (distanceToEnd) and the pointer to the next road segment (nextSegment), which is used to represent the vehicle's navigation path.

[0120] In one embodiment, before obtaining the lane data, AOI data, and vehicle data according to the traffic simulation data corresponding to the data structure, the method further includes:

[0121] Allocate memory space to the data structure of the traffic simulation data, and initialize the data structure of the traffic simulation data in the allocated memory space.

[0122] Specifically, memory space is allocated to the data structure of traffic simulation data, and a hash table is used to store mappings of various indexes to memory space locations, including mappings of lane indexes to lane array subscripts, mappings of vehicle indexes to vehicle array subscripts, and mappings of AOI indexes to AOI array subscripts. And because the allocated memory space may contain uncleaned data residues, the data structure of the traffic simulation data in the allocated memory space needs to be initialized to improve the accuracy of traffic simulation.

[0123] In this embodiment, by allocating memory space to the data structure of traffic simulation data, the data structure of traffic simulation can rely on the memory space to store the corresponding traffic simulation data, and initialize the data structure of traffic simulation data in the allocated memory space, which can effectively improve the accuracy of traffic simulation.

[0124] In one embodiment, traffic simulation is started, AOI data is updated with a preset simulation step length, and target vehicles in the AOI are determined according to the updated AOI data, including:

[0125] Obtain a preset simulation time and a preset simulation step, and start traffic simulation according to the preset simulation time and the preset simulation step; when the simulation time is less than the preset simulation time, update the AOI data with the preset simulation step, and determine the target vehicle in the AOI according to the updated AOI data.

[0126] The preset simulation duration refers to the continuous simulation time for starting the traffic simulation. When it is greater than the preset simulation duration, the traffic simulation stops. In the preset simulation time of the traffic simulation, the simulation will be repeated in a time period, and the time period of the repeated simulation is the preset simulation step.

[0127] Specifically, the preset simulation time and the preset simulation step length are obtained, and the traffic simulation is started according to the preset simulation time and the preset simulation step length, that is, the duration of the traffic simulation is the preset simulation time, and the traffic will be repeatedly simulated with the preset simulation step length in the preset simulation time, and the traffic simulation result of the previous preset simulation step length is updated. Taking the simulation of the traffic conditions from 8 to 9 in the morning in Beijing as an example, the preset simulation time and the preset simulation step length can be in seconds, and the traffic conditions of 28800 seconds to 32400 seconds are simulated, that is, the cycle of the preset simulation step length, and the preset simulation step length is gradually increased from 28800 to 32400. The preset simulation step length or other preset cycle values ​​are used to determine whether the simulation is over. When the simulation time is less than the preset simulation time, it is considered that the traffic is still being simulated, and the AOI data is updated with the preset simulation step length. The target vehicle in the AOI is determined according to the updated AOI data, wherein the target vehicle is the vehicle that makes a navigation request in the AOI.

[0128] Furthermore, before updating the AOI data and the data related to the AOI with the preset simulation step length, and determining the target vehicle in the AOI according to the updated AOI data, it is necessary to initialize an integer variable for recording the number of vehicles that issued the navigation request, and at the same time initialize another integer variable for storing temporary results in the subsequent loop of the preset simulation step length. After updating the AOI data, loop through all vehicles in the given AOI area, and for each vehicle, obtain vehicle-related information according to its index in the vehicle array, and the vehicle-related information may be the vehicle index, the starting AOI index, and the destination AOI index, etc.

[0129] Furthermore, updating the AOI data and the data related to the AOI with the preset simulation step size may include the following three types:

[0130] 1. Update the navigation request vehicle: In the loop of the preset simulation step, determine each time whether the vehicle in the AOI meets the conditions for issuing a navigation request. The conditions for issuing a navigation request include that the vehicle is valid (valid is not equal to 0), navigation can be performed (canRoute is true), and the current global time step is greater than or equal to the scheduled departure time of the vehicle, and the scheduled departure time comes from the trip information. If the conditions are met, the relevant information of the vehicle (including the vehicle index, the starting AOI index, and the destination AOI index) is recorded in the RouteMetaInfo structure array, and the canRoute attribute of the vehicle is set to false. At this time, update the number of vehicles that issued the navigation request.

[0131] 2. Update lane insertion information: If the vehicle has already started from the AOI and needs to leave the AOI and insert into the lane, insert the vehicle into the insert_buffer of the corresponding lane (newly inserted vehicles, merged and sorted and connected to the main chain), and count the number of inserted vehicles in each lane. At the same time, add the vehicle to the removeBuff of the corresponding AOI (storing the pointer of the AOI vehicle) so that it can be removed from the AOI in the subsequent steps.

[0132] 3. Update the mapping from AOI to lanes: Add the vehicles in the AOI to the aoi2Lane array and update aoi2LaneNum, which is used to record the lane index corresponding to each AOI.

[0133] In this embodiment, by obtaining a preset simulation time and a preset simulation step, and when the simulation time is less than the preset simulation time, updating the AOI data and data related to the AOI with the preset simulation step, traffic simulation can be accurately performed, thereby more accurately determining the target vehicle in the AOI to which the navigation request needs to be sent.

[0134] In one embodiment, Figure 3 As shown, S400 includes:

[0135] S420: Obtain a global navigation instance.

[0136] The global navigation instance is an instance used to communicate with an external navigation module.

[0137] Specifically, a routing request object is created for each target vehicle that issues a navigation request, and the AOI indexes of the start and end points of the request are set. Then the global navigation RoutingClient instance is obtained, and a routing request is initiated to the navigation module through the global navigation instance, and then the navigation module is waited for to process these requests and return the navigation results, that is, the path planning data corresponding to the target vehicle is obtained.

[0138] Furthermore, before obtaining the global navigation instance, when allocating memory space for the data structure of the traffic simulation data, memory space is also allocated for the routing information to store the navigation request information, and an array for storing the path planning results is defined to store the road section information of each vehicle and the corresponding lane index variable. When the data structure of the traffic simulation data with allocated storage space is initialized, the global navigation instance is also initialized.

[0139] S440, establishing a communication link between the target vehicle and the external navigation module through the global navigation instance, and uploading the navigation request to the external navigation module.

[0140] Specifically, the initialized global navigation instance is responsible for establishing a communication link between the target vehicle and the external navigation module, communicating with the gRPC (g Remote Procedure Call, a type of remote procedure call) service in the external navigation module, and uploading the navigation request to the external navigation module. At this time, the server and client models are used. The structural diagram of the external navigation module method server and client models is as follows: Figure 4 As shown, the external navigation module is used as the server. The external navigation module first starts and listens to the routing service based on the gRPC protocol, reads the map path and the gRPC service port, creates a server.NewRoutingServer instance, and creates a gRPC server instance through grpc.NewServer, registers the server to the server, and finally starts listening to the specified port. When in the listening state, the client can connect to the server through the specified address and port. The server receives the navigation request uploaded by the client, processes the route request sent from the client, and obtains the starting point (start) and the end point (end) from the request. The route search method is called according to the route type in the request, and the searched route segment information is added to the return result and assembled into an object. Finally, the response result containing the searched route information is returned. Among them, calling the route search method can be a gRPC asynchronous call GetRoute method in the external navigation module to obtain the routing information of the target vehicle. The routing information refers to the path information between the starting point and the terminal. Among them, gRPC is a high-performance, open source communication framework that can make full use of the advantages of gRPC, such as low latency, high throughput, and cross-platform and cross-language support. When the load on the computing part increases, the number of navigation servers can be increased to cope with the increase in request volume, thereby improving the scalability and processing power of the system.

[0141] Furthermore, the GetRoute method is used to initiate an asynchronous call to the gRPC service to request the vehicle's routing information, that is, to request the vehicle's path planning data. The input parameters of the GetRoute method include the request, vehicle index, starting AOI index, and destination coordinates. In the GetRoute function, first create an AsyncClientCall object and set the relevant information of the request for the object. Then use the stub_ member variable (a pointer of type RoutingService) to prepare an asynchronous call and send the request to the gRPC service. Finally, associate the response reader, status, and context of the asynchronous call with the AsyncClientCall object.

[0142] S460, receiving the path planning data corresponding to the target vehicle returned by the navigation module through the communication link, and storing the path planning data into the path planning data structure.

[0143] Specifically, the response received from the navigation module is processed by the AsyncCompleteRpc method, the response carries the acquired vehicle routing information, and the routing information in the response is stored in the corresponding path planning data structure.

[0144] Furthermore, each navigation request starts a new thread and calls the AsyncCompleteRpc method. The AsyncCompleteRpc method is a worker thread function that processes the response received from the gRPC service and uses the Next method of the CompletionQueue object (cq_) to wait for the server response. When a response is received, the Next method fills in an AsyncClientCall object pointer that contains the context information of the response. Next, the function checks whether the status of the RPC (Remote Procedure Call) call is successful. If successful, it parses the response, extracts the routing information, and stores the information in the corresponding data structure. Otherwise, the function throws a runtime error. The method loops until all request responses are processed.

[0145] In this embodiment, by adopting a global navigation instance, a communication link can be established between the target vehicle and the external navigation module, thereby efficiently uploading the navigation request to the external navigation module, efficiently receiving the returned path planning data, and storing the path planning data in the path planning data structure.

[0146] In one embodiment, it further includes:

[0147] Use an unordered map to cache water depth.

[0148] Specifically, an unordered map is an associative container that stores elements composed of a key value key and a mapping value value, and allows fast retrieval of individual elements based on their keys, where each key is unique and the corresponding value can be found through the key. In this application, the time step key1 and the road index key2 together form a pair, and the corresponding water depth is value. The rainstorm data for each time step may be different. The pair consisting of the time step and the road index can be mapped to the water depth value corresponding to the key through a hash map variable, and because the key will be mapped to an integer within a certain range after being organized by the hash value and the hash bucket, the order of elements of multiple water depths cached using an unordered map is uncertain.

[0149] Furthermore, a file name array and a driving speed array are initialized to store data files of different rainstorm levels and maximum driving speeds respectively.

[0150] In this embodiment, by using unordered mapping to cache the water depth corresponding to the rainstorm data, the water depth can be efficiently searched, thereby improving the efficiency of simulating the water depth in the traffic simulation.

[0151] In one embodiment, Figure 5 As shown, S600 includes:

[0152] S620: Simulate traffic conditions under heavy rain conditions based on the updated lane data, the updated vehicle data, the heavy rain data, and the water depth corresponding to the heavy rain data.

[0153] Specifically, the vehicle's acceleration, deceleration, position update, lane change and other behaviors are calculated through the given vehicle attributes, road conditions and environmental parameters mentioned above, and the corresponding water accumulation depth is found by using a pair consisting of a time step and a lane index through the initialized hash map variable. The vehicle condition on the lane can be simulated according to the updated lane data and the updated vehicle data, and the rainstorm water accumulation condition can be simulated according to the rainstorm data and the water accumulation depth corresponding to the rainstorm data. The traffic condition under rainstorm conditions can be accurately simulated according to the vehicle condition on the lane and the rainstorm water accumulation condition.

[0154] S640: Determine a global speed limit of the vehicle on the lane according to the rainstorm data.

[0155] Specifically, to determine the maximum permissible speed of a vehicle on a lane, first determine the global speed limit (rain_max_speed) of the vehicle on the lane based on the rainstorm data. If the rainstorm data at the current time step is 0, the global speed limit does not need to be applied.

[0156] S660: Determine a maximum allowable speed of the vehicle on the lane in a rainstorm condition according to the global speed limit and the water depth corresponding to the rainstorm data.

[0157] Specifically, first determine whether to apply the global speed limit, then calculate the maximum speed of the lane based on the global speed limit and the water depth corresponding to the heavy rain data, and finally determine the maximum allowable speed of the vehicle on the lane under heavy rain conditions.

[0158] Furthermore, the adjusted maximum permissible speed (max_speed) is used as a constraint for the calculation of vehicle acceleration. When the current speed (currentSpeed) of the vehicle is greater than the maximum permissible speed, the vehicle needs to decelerate. The deceleration calculation takes into account factors such as the current speed of the vehicle, the maximum permissible speed, and the relative distance between the vehicle and the vehicle in front in the traffic simulation. Since the maximum permissible speed has taken into account the effects of rainfall and road water depth, it indirectly affects the acceleration, deceleration and speed of the vehicle. Therefore, this method can simulate driving behavior on slippery roads and under different road conditions to a certain extent. In actual driving applications, rainfall and road water depth will affect the road friction coefficient, thereby changing the driving performance of the vehicle. This method does not directly simulate the changes in the road friction coefficient, but indirectly simulates these effects by adjusting the maximum speed of the lane, so that it has better adaptability under different environmental conditions.

[0159] In this embodiment, by simulating the traffic conditions under heavy rain conditions and determining the global speed limit of vehicles on the lane, the maximum allowable speed of vehicles on the lane in the traffic simulation under heavy rain conditions can be determined more accurately.

[0160] In one embodiment, after the iterations of all time steps are completed, that is, after the preset simulation time ends, the resources are released.

[0161] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0162] Based on the same inventive concept, the embodiment of the present application also provides a vehicle speed control device in heavy rain for implementing the vehicle speed control method in heavy rain. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the vehicle speed control device in heavy rain provided below can refer to the limitations of the vehicle speed control method in heavy rain above, and will not be repeated here.

[0163] In one embodiment, Figure 6As shown, a vehicle speed control device in rainstorm conditions is provided, comprising: a data structure acquisition module 100, a data acquisition module 200, a navigation vehicle determination module 300, a path planning module 400, a rainstorm data reading module 500 and a traffic simulation module 600, wherein:

[0164] A data structure acquisition module 100 is used to acquire a data structure of traffic simulation data and a path planning data structure, wherein the data structure of the traffic simulation data includes a lane data structure, an AOI data structure, and a vehicle data structure;

[0165] The data acquisition module 200 is used to store the acquired traffic simulation data according to the data structure of the traffic simulation data, and obtain lane data, AOI data, and vehicle data respectively;

[0166] The navigation vehicle determination module 300 is used to start the traffic simulation, update the AOI data with a preset simulation step length, and determine the target vehicle in the AOI according to the updated AOI data, where the target vehicle is the vehicle that makes a navigation request in the AOI;

[0167] A path planning module 400 is used to obtain path planning data corresponding to the target vehicle and store the path planning data into a path planning data structure;

[0168] A rainstorm data reading module 500 is used to update lane data and vehicle data according to the stored path planning data, read the rainstorm data, and determine the water depth corresponding to the rainstorm data;

[0169] The traffic simulation module 600 is used to simulate the traffic conditions under heavy rain conditions according to the updated lane data, the updated vehicle data, the heavy rain data, and the water depth corresponding to the heavy rain data, and obtain the maximum allowable driving speed of the vehicle on the lane under heavy rain conditions.

[0170] In one embodiment, a memory allocation module is further included, and the memory allocation module is used to allocate memory space to the data structure of the traffic simulation data and initialize the data structure of the traffic simulation data with the allocated memory space.

[0171] In one embodiment, the navigation vehicle determination module 300 is also used to obtain a preset simulation time and a preset simulation step, and start a traffic simulation according to the preset simulation time and the preset simulation step; when the simulation time is less than the preset simulation time, the AOI data is updated with the preset simulation step, and the target vehicle in the AOI is determined according to the updated AOI data.

[0172] In one embodiment, the path planning module 400 is also used to obtain a global navigation instance; establish a communication link between the target vehicle and the external navigation module through the global navigation instance, and upload the navigation request to the external navigation module; receive the path planning data corresponding to the target vehicle returned by the navigation module through the communication link, and store the path planning data in the path planning data structure.

[0173] In one embodiment, a water accumulation depth cache module is further included, and the water accumulation depth cache module is used to cache the water accumulation depth using an unordered mapping.

[0174] In one embodiment, the traffic simulation module 600 is also used to simulate traffic conditions under heavy rain conditions based on updated lane data, updated vehicle data, heavy rain data, and the water depth corresponding to the heavy rain data; determine the global speed limit of the vehicle on the lane based on the heavy rain data; determine the maximum allowable speed of the vehicle on the lane under heavy rain conditions based on the global speed limit and the water depth corresponding to the heavy rain data.

[0175] Each module in the above-mentioned vehicle speed control device under heavy rain conditions can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0176] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 7 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the data structure of traffic simulation data and the path planning data structure data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a vehicle speed control method under heavy rain conditions is implemented.

[0177] Those skilled in the art will understand that Figure 7The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0178] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0179] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0180] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0181] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0182] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0183] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0184] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for controlling vehicle speed in rainstorm conditions, characterized in that: The method comprises: Acquire a data structure of traffic simulation data and a path planning data structure, wherein the data structure of the traffic simulation data includes a lane data structure, an AOI data structure, and a vehicle data structure; The acquired traffic simulation data is stored in correspondence with the data structure of the traffic simulation data, and lane data, AOI data, and vehicle data are obtained respectively; Starting a traffic simulation, updating the AOI data with a preset simulation step length, and determining a target vehicle in the AOI according to the updated AOI data, wherein the target vehicle is a vehicle in the AOI that makes a navigation request; Acquire path planning data corresponding to the target vehicle, and store the path planning data in the path planning data structure; The lane data and the vehicle data are updated according to the stored path planning data, and the rainstorm data is read to determine the water depth corresponding to the rainstorm data; the updating of the lane data and the vehicle data includes: calculating the acceleration, deceleration, position update and lane change behavior of the vehicle according to the vehicle attributes, road conditions and environmental parameters; Traffic conditions under heavy rain conditions are simulated based on the updated lane data, the updated vehicle data, the heavy rain data, and the water depth corresponding to the heavy rain data to obtain the maximum allowable driving speed of the vehicle on the lane under heavy rain conditions.

2. The method according to claim 1, characterized in that Before the traffic simulation data is stored and acquired according to the data structure of the traffic simulation data, and the lane data, the AOI data, and the vehicle data are obtained respectively, the method further includes: Allocate memory space to the data structure of the traffic simulation data, and initialize the data structure of the traffic simulation data in the allocated memory space.

3. The method according to claim 1, characterized in that The starting of the traffic simulation, updating the AOI data with a preset simulation step length, and determining the target vehicle in the AOI according to the updated AOI data include: Obtaining a preset simulation time and a preset simulation step length, and starting a traffic simulation according to the preset simulation time and the preset simulation step length; When the simulation time is less than the preset simulation time, the AOI data is updated with a preset simulation step size, and a target vehicle in the AOI is determined according to the updated AOI data.

4. The method according to claim 1, characterized in that The obtaining of the path planning data corresponding to the target vehicle and storing the path planning data in the path planning data structure comprises: Get the global navigation instance; Establishing a communication link between the target vehicle and the external navigation module through the global navigation instance, and uploading a navigation request to the external navigation module; Receive the path planning data corresponding to the target vehicle returned by the navigation module through the communication link, and store the path planning data in the path planning data structure.

5. The method according to claim 1, characterized in that Also includes: An unordered map is used to cache the water depth.

6. The method according to claim 1, characterized in that The traffic condition under a rainstorm is simulated according to the updated lane data, the updated vehicle data, the rainstorm data, and the water depth corresponding to the rainstorm data to obtain the maximum allowable speed of the vehicle on the lane under the rainstorm, including: Simulating traffic conditions under heavy rain conditions according to the updated lane data, the updated vehicle data, the heavy rain data, and the water depth corresponding to the heavy rain data; determining a global speed limit of the vehicle on the lane according to the rainstorm data; The maximum permissible speed of the vehicle on the lane under heavy rain conditions is determined according to the global speed limit and the water depth corresponding to the heavy rain data.

7. A vehicle speed control device in rainstorm conditions, characterized in that: The device comprises: A data structure acquisition module, used to acquire a data structure of traffic simulation data and a path planning data structure, wherein the data structure of the traffic simulation data includes a lane data structure, an AOI data structure, and a vehicle data structure; A data acquisition module, used to store the acquired traffic simulation data in accordance with the data structure of the traffic simulation data, and obtain lane data, AOI data, and vehicle data respectively; A navigation vehicle determination module is used to start a traffic simulation, update the AOI data with a preset simulation step length, and determine a target vehicle in the AOI according to the updated AOI data, wherein the target vehicle is a vehicle in the AOI that makes a navigation request; A path planning module, used for acquiring path planning data corresponding to the target vehicle, and storing the path planning data into the path planning data structure; A rainstorm data reading module is used to update the lane data and the vehicle data according to the stored path planning data, read the rainstorm data, and determine the water depth corresponding to the rainstorm data; the updating of the lane data and the vehicle data includes: calculating the acceleration, deceleration, position update and lane change behavior of the vehicle according to the vehicle attributes, road conditions and environmental parameters; The traffic simulation module is used to simulate the traffic conditions under heavy rain conditions according to the updated lane data, the updated vehicle data, the heavy rain data, and the water depth corresponding to the heavy rain data, so as to obtain the maximum allowable driving speed of the vehicle on the lane under heavy rain conditions.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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