A vehicle drivable lane determination method, device and equipment

Abstract

The embodiment of the specification discloses a vehicle drivable lane determination method, device and equipment. The scheme can include: obtaining preset lane data at each lane in a first preset area according to vehicle position information obtained for a target vehicle. Wherein, the preset lane data is generated according to the historical vehicle perception data collected by the perception device in the first preset area within the first preset time. Then, according to the preset lane data at each lane in the first preset area, the target lane with the highest matching degree with the target vehicle is determined from each lane, and the drivable lane of the target vehicle is obtained. Thus, it is not necessary to manually demarcate or obtain the drivable lane of the vehicle based on the visual perception device, so as to improve the convenience, real-time performance and accuracy of obtaining the drivable lane of the vehicle.

Description

Technical Field

[0001] This application relates to the field of map navigation, and in particular to a method, apparatus and device for determining a vehicle's drivable lane. Background Technology

[0002] Existing technologies mainly determine the drivable lanes of vehicles by manually marking them or by using visual perception devices carried by the vehicle.

[0003] Manually designating vehicle lanes requires manually identifying which lanes are usable and which are not, based on actual road conditions. When unexpected events occur, this method cannot update the available lanes in real-time according to traffic conditions. Therefore, manually designating vehicle lanes is labor-intensive and lacks real-time updates.

[0004] The vehicle's driving lane is determined by its own visual perception equipment. However, the visual perception equipment is greatly affected by the surrounding environment. When visibility is insufficient, the visual perception equipment cannot accurately obtain the vehicle's driving lane.

[0005] Therefore, improving the convenience, real-time nature, and accuracy of obtaining information on available vehicle lanes has become an urgent technical problem to be solved. Summary of the Invention

[0006] The embodiments of this specification provide a method, apparatus, and equipment for determining a vehicle's drivable lane, which can solve the problems of high manpower consumption, low real-time performance, and poor accuracy in the process of obtaining a vehicle's drivable lane.

[0007] To solve the above-mentioned technical problems, the embodiments in this specification are implemented as follows:

[0008] A method for determining a vehicle's drivable lane includes,

[0009] Obtain the vehicle location information of the target vehicle.

[0010] Based on the vehicle location information, preset lane data for each lane within a first preset area is obtained; the preset lane data is generated based on historical vehicle perception data collected by the sensing device for the first preset area within a first preset time period; the first preset time period is any time period within a second preset time period before a specified time, and the specified time is a time period earlier than the time when the vehicle location information is obtained by a preset time.

[0011] Based on the preset lane data of each lane in the first preset area, the target lane with the highest matching degree with the target vehicle is determined from each lane, and the drivable lane of the target vehicle is obtained.

[0012] A vehicle lane determination device, comprising,

[0013] The first acquisition module is used to acquire the vehicle location information of the target vehicle.

[0014] The second acquisition module is used to acquire preset lane data for each lane in a first preset area based on the vehicle location information. The preset lane data is generated based on historical vehicle perception data collected by the sensing device for the first preset area within a first preset time period. The first preset time period is any time period within a second preset time period before a specified time, and the specified time is a time period earlier than the time when the vehicle location information is acquired.

[0015] The determination module is used to determine the target lane with the highest matching degree with the target vehicle from the preset lane data of each lane in the first preset area, so as to obtain the drivable lane of the target vehicle.

[0016] A vehicle lane determination device, comprising,

[0017] At least one processor; and,

[0018] A memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to:

[0020] Obtain the vehicle location information of the target vehicle.

[0021] Based on the vehicle location information, preset lane data for each lane within a first preset area is obtained; the preset lane data is generated based on historical vehicle perception data collected by the sensing device for the first preset area within a first preset time period; the first preset time period is any time period within a second preset time period before a specified time, and the specified time is a time period earlier than the time when the vehicle location information is obtained by a preset time.

[0022] Based on the preset lane data of each lane in the first preset area, the target lane with the highest matching degree with the target vehicle is determined from each lane, and the drivable lane of the target vehicle is obtained.

[0023] At least one embodiment provided in this specification can achieve the following beneficial effects:

[0024] This solution first determines the drivable lanes within a preset area based on historical vehicle perception data collected from that area. Then, based on the vehicle location information of the target lane, it identifies the lane with the highest matching degree to the target vehicle, thus obtaining the target vehicle's driving lane. This eliminates the need for manual lane delineation and reliance on vehicle-mounted visual perception devices to determine drivable lanes, thereby improving the convenience, real-time performance, and accuracy of lane determination. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments or prior art of this specification, the drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart illustrating a method for determining a vehicle's drivable lane, as provided in an embodiment of this specification.

[0027] Figure 2 The embodiments provided in this specification correspond to Figure 1 A schematic diagram of a vehicle lane determination device;

[0028] Figure 3 The embodiments provided in this specification correspond to Figure 1 A schematic diagram of a vehicle lane determination device. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of one or more embodiments of this specification clearer, the technical solutions of one or more embodiments of this specification will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of them. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of one or more embodiments of this specification.

[0030] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0031] Figure 1This is a flowchart illustrating a method for determining a vehicle's drivable lane, as provided in an embodiment of this specification. From a programming perspective, the entity executing this process can be a device for determining a vehicle's drivable lane, or an application program mounted on the device for determining a vehicle's drivable lane. For example... Figure 1 As shown, the process may include the following steps:

[0032] Step 102: Obtain the vehicle location information of the target vehicle.

[0033] In the embodiments described in this specification, the target vehicle can be an unmanned vehicle traveling on a road or a manned vehicle. The vehicle location information can be the target vehicle's world coordinates in a world coordinate system or its corresponding coordinates in other coordinate systems. The device that generates the vehicle location information for the target vehicle can be a roadside sensing device or an onboard sensing device installed on the target vehicle. The vehicle location information can be obtained directly from the roadside sensing device or the onboard sensing device, or it can be obtained from a server that receives the corresponding sensing data from the roadside sensing device or the onboard sensing device.

[0034] Step 104: Based on the vehicle location information, obtain the preset lane data for each lane in the first preset area; the preset lane data is generated based on the historical vehicle perception data collected by the sensing device for the first preset area within a first preset time period; the first preset time period is any time period within a second preset time period before a specified time, and the specified time is a time period earlier than the time when the vehicle location information is obtained by a preset time.

[0035] In the embodiments of this specification, the first preset area can be the sensing area of ​​the target roadside sensing device, and the target roadside sensing device can be a sensing device whose sensing area includes the location of the target vehicle. The first preset duration can be any time period within a second preset duration prior to a specified time. The first preset duration is less than the second preset duration, where the first preset duration can be several hours, one day, or any other duration. The specified time can be a time that is a preset time earlier than the time when the vehicle location information of the target vehicle is obtained. For example, if the time when the vehicle location information of the target vehicle is obtained is 0:00 on September 15th, and the preset time is 24 hours, then the specified time is 0:00 on September 14th. Then, if the first preset duration is 24 hours and the second preset duration is 240 hours, then the first preset duration can be any 24-hour period between 0:00 on September 4th and 0:00 on September 14th.

[0036] In this embodiment, the statistical sensing device collects all historical vehicle sensing data for a first preset area within a first preset time period. The sensing device can be a roadside sensing device installed beside the road, or an onboard sensing device carried by vehicles passing through the first preset area within the first preset time period. Based on the collected historical vehicle sensing data, preset lane data for each lane within the first preset area is generated.

[0037] Step 106: Based on the preset lane data of each lane in the first preset area, determine the target lane that best matches the target vehicle from the lanes, and obtain the drivable lane of the target vehicle.

[0038] In this embodiment of the specification, based on the real-time vehicle location information of the target vehicle, the preset vehicle data with the highest matching degree with the vehicle location information of the target vehicle is determined from the preset vehicle data in each lane within a first preset area. The lane to which the preset vehicle data with the highest matching degree belongs is determined as the target lane with the highest matching degree with the target vehicle, thus obtaining the drivable lane of the target vehicle.

[0039] Figure 1 The method described above can determine the drivable lane of a target vehicle in real time based on the target vehicle's real-time location information and historical vehicle perception data, thereby improving the convenience, real-time performance, and accuracy of determining the drivable lane.

[0040] based on Figure 1 In addition to the method described in the embodiments of this specification, some specific implementation schemes of the method are also provided, which will be described below.

[0041] After the target vehicle obtains a drivable lane, in order to improve the actual drivability of the drivable lane, the preset lane data generated for each lane in the first preset area needs to be updated periodically to improve the freshness of the preset lane data generated for each lane. Based on this, step 102: before obtaining the vehicle location information of the target vehicle, may further include:

[0042] Determine whether the time interval between the most recent generation of preset lane data for each lane in the first preset area and the current time reaches a third preset duration.

[0043] If the time interval reaches the third preset duration, then based on any historical vehicle perception data within the first preset duration within the time interval, preset lane data for each lane in the first preset area is generated, wherein the historical vehicle perception data is vehicle perception data collected by the sensing device for the first preset area.

[0044] In this embodiment, after the server generates preset lane data for each lane within the first preset area, it records the corresponding generation time. When the server detects that the time interval between the current time and the time of the most recent generation of preset lane data for each lane within the first preset area reaches a third preset duration, the server can generate preset lane data for each lane within the first preset area based on the corresponding historical vehicle perception data. The third preset duration can be set according to the actual road scenario. If the actual road scenario is under construction, the third preset duration can be set shorter; if the actual road scenario is completed, the third preset duration can be set longer.

[0045] It should be noted that the duration of the time interval can be greater than or equal to the first preset duration. If the time interval is greater than the first preset duration, in general, historical vehicle perception data collected within the first preset duration closest to the current time can be selected to generate the preset lane data for each lane. However, if the actual road scene does not change within this time interval, any time period that meets the first preset duration can be selected from the time interval, and then the preset lane data for each lane can be generated based on the historical vehicle perception data collected within that time period.

[0046] In this embodiment of the specification, when the time interval between the most recent generation of preset lane data for each lane in the first preset area and the current time reaches a third preset duration, the corresponding preset lane data for each lane in the first preset area is regenerated so that the drivable lane successfully matched for the target vehicle is a real-time accurate drivable lane, thereby improving the real-time accuracy of the successfully matched drivable lane.

[0047] Historical vehicle perception data can include at least vehicle position information and vehicle heading angle information. Preset lane data for each lane can be generated based on the vehicle position information and / or vehicle heading angle information. Specifically, the methods for generating preset lane data for each lane can include at least the following three methods.

[0048] The step of generating preset lane data for each lane within the first preset area based on historical vehicle perception data within any one of the first preset time periods during the time interval may specifically include:

[0049] Obtain the location information of each vehicle from the historical vehicle perception data within any of the first preset time periods.

[0050] Based on the location information of each vehicle, vehicle trajectory data for each vehicle is generated.

[0051] Based on the degree of overlap between the different vehicle trajectory data, the vehicle trajectory data of each vehicle is classified according to the number of lanes in the first preset area to obtain vehicle trajectory data that matches each lane in the first preset area.

[0052] In the embodiments of this specification, the location information of each vehicle can be obtained from historical vehicle sensing data, and this location information can be latitude and longitude information. Vehicles are usually in motion. When a moving vehicle passes through a first preset area, the sensing device will collect multiple vehicle sensing data for that vehicle. Therefore, for the same vehicle, multiple latitude and longitude information can be obtained. Connecting these multiple latitude and longitude information for the same vehicle yields a trajectory data reflecting the vehicle's driving path. Therefore, the driving trajectory data of each vehicle can be generated based on the historical vehicle sensing data collected within a first preset time period.

[0053] In this embodiment, the driving trajectory data of each vehicle is divided into K groups based on the number of lanes K contained in the first preset area. Assuming the number of vehicles K is 3, three driving trajectory data lines L1, L2, and L3 are randomly selected from the driving trajectory data of each vehicle. Using L1, L2, and L3 as centers, the driving trajectory data of each vehicle is divided into three groups. Specifically, the division process can be as follows: Driving trajectory data line L4 is randomly selected from the remaining driving trajectory data. The degree of overlap between L4 and L1, L2, and L3 is determined. If the degree of overlap between L4 and L1 is the highest, then L4 and L1 are grouped together. The remaining driving trajectory data are then grouped sequentially until all driving trajectory data is grouped, resulting in grouped driving trajectory data groups Z1, Z2, and Z3. Next, determine the center driving trajectory data in driving trajectory data groups Z1, Z2, and Z3 respectively. Assuming the determined center driving trajectory data for driving trajectory data groups Z1, Z2, and Z3 are L5, L6, and L7 respectively, then, using L5, L6, and L7 as centers, further divide the driving trajectory data of each vehicle into three groups according to the above grouping principle. Iterate in this way until the driving trajectory data in each group no longer changes. Finally, determine the three groups of driving trajectory data as the preset lane data for three lanes.

[0054] The step of generating preset lane data for each lane within the first preset area based on historical vehicle perception data within any one of the first preset time periods during the time interval may specifically include:

[0055] Obtain the position information and heading angle information of each vehicle in the historical vehicle perception data within any one of the first preset time periods.

[0056] Based on the position and heading angle information of each vehicle, the vehicle trajectory points of each vehicle are generated.

[0057] Based on the angle difference between the heading angles of different vehicle trajectory points, the vehicle trajectory points of each vehicle are classified according to the number of lanes in the first preset area to obtain vehicle trajectory points that match each lane in the first preset area.

[0058] In this embodiment of the specification, the position information and heading angle information of each vehicle can be obtained from historical vehicle perception data. The vehicle position information can be latitude and longitude information, and each latitude and longitude point can represent a vehicle's trajectory point during its travel. Therefore, the trajectory points of each vehicle can be generated based on the historical vehicle perception data collected within a first preset time period.

[0059] In this embodiment, vehicle heading angle information is superimposed on the driving trajectory points of each vehicle. Based on the number of lanes K contained in the first preset area, the driving trajectory points of each vehicle after superimposing the vehicle heading angle information are divided into K groups. Assuming the number of vehicles K is 3, three driving trajectory points P1, P2, and P3 are randomly selected from the driving trajectory points of each vehicle, and the driving trajectory points of each vehicle are divided into three groups centered on P1, P2, and P3. The specific division process can be as follows: Driving trajectory point P4 is randomly selected from the remaining driving trajectory points. The angles between the heading angle of P4 and the heading angles of P1, P2, and P3 are determined. If the angle between the heading angle of P4 and the heading angle of P1 is the smallest, then P4 and P1 are grouped together. Other driving trajectory points in the remaining driving trajectory points are grouped sequentially until all driving trajectory points are grouped, resulting in the grouped driving trajectory point groups Z4, Z5, and Z6. Next, determine the center trajectory points in trajectory point groups Z4, Z5, and Z6 respectively. Assuming the determined center trajectory points for these groups are P5, P6, and P7 respectively, then divide the trajectory points of each vehicle into three groups again, centered on P5, P6, and P7, following the same grouping principle. Iterate in this way until the trajectory points in each group no longer change. Finally, use the three groups of trajectory points as the preset lane data for the three lanes.

[0060] The step of generating preset lane data for each lane within the first preset area based on historical vehicle perception data within any one of the first preset time periods during the time interval may specifically include:

[0061] Obtain the position information and heading angle information of each vehicle in the historical vehicle perception data within any one of the first preset time periods.

[0062] Based on the position information and heading angle information of each vehicle, vehicle trajectory points and vehicle trajectory data are generated for each vehicle.

[0063] Based on the angular difference between the heading angles of different vehicle trajectory points and the degree of overlap between different vehicle trajectory data, the vehicle trajectory points and vehicle trajectory data of each vehicle are classified according to the number of lanes in the first preset area to obtain vehicle trajectory points and vehicle trajectory data that match each lane in the first preset area.

[0064] In this embodiment, the location information and heading angle information of each vehicle can be obtained from historical vehicle perception data. The location information can be latitude and longitude. Based on the latitude and longitude information, the driving trajectory points of each vehicle can be obtained, and connecting the latitude and longitude information for the same vehicle yields the driving trajectory data for that vehicle. Therefore, driving trajectory points and driving trajectory data for each vehicle can be generated based on historical vehicle perception data collected within a first preset time period.

[0065] In this embodiment, based on the number K of lanes contained in the first preset area, the driving trajectory data of each vehicle can be divided into K groups, and the driving trajectory points of each vehicle can also be divided into K groups. The specific grouping principle can be referred to the grouping process described above, and will not be repeated here. The final grouped driving trajectory points and driving trajectory data are then determined as the preset lane data for each lane.

[0066] In this embodiment, during the grouping of driving trajectory data and driving trajectory points for each vehicle, the grouping can be combined with the included angle between the heading angles of different driving trajectory points and the degree of overlap between different driving trajectory data, thereby improving the accuracy of grouping driving trajectory data and driving trajectory points for each vehicle.

[0067] After determining the preset lane data corresponding to each lane, the target lane with the highest matching degree can be determined based on the vehicle location information of the target vehicle. Therefore, step 106: determining the target lane with the highest matching degree to the target vehicle from among the preset lane data of each lane in the first preset area, and obtaining the drivable lane for the target vehicle, can specifically include:

[0068] Based on the vehicle location information of the target vehicle, a second preset area containing the target vehicle is determined; the second preset area is an area with a preset size and preset shape, centered on the target vehicle.

[0069] Based on the preset lane data of each lane in the second preset area, the target lane with the highest matching degree with the target vehicle is determined.

[0070] In the embodiments of this specification, the second preset area can be a portion of the first preset area. The second preset area can be an area with a preset size and preset shape, centered on the vehicle position of the target vehicle. Its preset shape can include any one of a circle, an isosceles triangle, a square, or a regular polygon. The preset size can be expressed as the distance from the vehicle position to each side of the shape being equal, and each distance being greater than or equal to the width of a lane and less than or equal to the width of the road. Alternatively, the preset size can be determined to other values ​​according to actual needs.

[0071] In the embodiments of this specification, the target lane with the highest matching degree with the target vehicle is determined based on the preset lane data of each lane contained in the determined preset graphic.

[0072] Specifically, the preset lane data includes at least one of vehicle trajectory points and vehicle trajectory data.

[0073] The step of determining the target lane with the highest matching degree with the target vehicle based on the preset lane data of each lane in the second preset area may specifically include:

[0074] Determine the total number of vehicle trajectory points contained in each lane within the second preset area.

[0075] The lane corresponding to the maximum value among the total number of vehicle trajectory points is determined as the target lane with the highest matching degree to the target vehicle; or...

[0076] Determine the total number of vehicle trajectory data entries contained in each lane within the second preset area.

[0077] The lane to which the maximum value among the total number of vehicle trajectory data entries belongs is determined as the target lane with the highest matching degree with the target vehicle.

[0078] In this embodiment, based on the range defined by the second preset area, the total number of vehicle trajectory points in each lane within the second preset area is calculated. The lane with the most vehicle trajectory points is determined as the target lane with the highest matching degree with the target vehicle. It should be noted that if two lanes have the same total number of vehicle trajectory points among the total number of vehicle trajectory points calculated for each lane within the second preset area, then either lane can be determined as the target lane with the highest matching degree with the target vehicle. If multiple lanes have the same total number of vehicle trajectory points among the total number of vehicle trajectory points calculated for each lane within the second preset area, then the middle lane among these multiple lanes can be determined as the target lane with the highest matching degree with the target vehicle. If there are two middle lanes, either one can be determined as the target lane with the highest matching degree with the target vehicle.

[0079] In the embodiments described in this specification, the target lane with the highest matching degree with the target vehicle is determined based on the driving trajectory data in each lane of the second preset area. The principle is the same as that of determining the target lane with the highest matching degree with the target vehicle based on the driving trajectory points in each lane of the second preset area, and will not be repeated here.

[0080] After identifying the target lane with the highest matching degree to the target vehicle, the target lane can be used as the drivable lane for the target vehicle, allowing the target vehicle to travel in the drivable lane. Based on this, after determining the target lane with the highest matching degree to the target vehicle from the preset lane data of each lane in the first preset area, and obtaining the drivable lane for the target vehicle, the process may further include:

[0081] Based on the drivable lane, a vehicle driving instruction is generated for the target vehicle.

[0082] The vehicle driving instruction is sent to the target vehicle, which is then used to drive in accordance with the drivable lane in response to the vehicle driving instruction.

[0083] In this embodiment of the specification, the drivable lane can be a data set consisting of a preset number of driving trajectory points and / or driving trajectory data. Based on this data set, the server generates a vehicle driving instruction that can guide driving in the target lane and sends the instruction to the target vehicle. Upon receiving the driving instruction from the server, the target vehicle parses it to obtain the set of driving trajectory points and / or driving trajectory data contained within the instruction, and then drives according to the parsed set of driving trajectory points and / or driving trajectory data.

[0084] Based on the same idea, embodiments of this specification also provide apparatus corresponding to the above methods. Figure 2 The embodiments provided in this specification correspond to Figure 1 A schematic diagram of a vehicle lane determination device. Figure 2 As shown, the device may include:

[0085] The first acquisition module 202 is used to acquire the vehicle location information of the target vehicle.

[0086] The second acquisition module 204 is used to acquire preset lane data for each lane in a first preset area based on the vehicle location information. The preset lane data is generated based on historical vehicle perception data collected by the sensing device for the first preset area within a first preset time period. The first preset time period is any time period within a second preset time period before a specified time, and the specified time is a time period earlier than the time when the vehicle location information is acquired.

[0087] The determining module 206 is used to determine the target lane with the highest matching degree with the target vehicle from the preset lane data of each lane in the first preset area, and obtain the drivable lane of the target vehicle.

[0088] Optionally, before the first acquisition module 202, it may further include:

[0089] The judgment module is used to determine whether the time interval between the most recent generation time of the preset lane data of each lane in the first preset area and the current time reaches the third preset duration.

[0090] The generation module is used to generate preset lane data for each lane in the first preset area based on historical vehicle perception data within any one of the first preset durations in the time interval if the time interval reaches the third preset duration. The historical vehicle perception data is vehicle perception data collected by the sensing device for the first preset area.

[0091] Optionally, the generation module may specifically include:

[0092] The first acquisition unit is used to acquire the location information of each vehicle in the historical vehicle perception data within any one of the first preset time periods.

[0093] The first generation unit is used to generate vehicle trajectory data for each vehicle based on the location information of each vehicle.

[0094] The first classification unit is used to classify the vehicle trajectory data of each vehicle according to the number of lanes in the first preset area based on the degree of overlap between different vehicle trajectory data, so as to obtain vehicle trajectory data that matches each lane in the first preset area.

[0095] Optionally, the generation module may specifically include:

[0096] The second acquisition unit is used to acquire the position information and heading angle information of each vehicle in the historical vehicle perception data within any one of the first preset time periods.

[0097] The second generation unit is used to generate the vehicle trajectory points of each vehicle based on the position information and heading angle information of each vehicle.

[0098] The second classification unit is used to classify the vehicle trajectory points of each vehicle according to the number of lanes in the first preset area based on the angle difference between the heading angles of different vehicle trajectory points, so as to obtain vehicle trajectory points that match each lane in the first preset area.

[0099] Optionally, the generation module may specifically include:

[0100] The third acquisition unit is used to acquire the position information and heading angle information of each vehicle in the historical vehicle perception data within any one of the first preset time periods.

[0101] The third generation unit is used to generate vehicle trajectory points and vehicle trajectory data for each vehicle based on the position information and heading angle information of each vehicle.

[0102] The third classification unit is used to classify the vehicle trajectory points and vehicle trajectory data of each vehicle according to the number of lanes in the first preset area based on the angle difference between the heading angles of different vehicle trajectory points and the degree of overlap between different vehicle trajectory data, so as to obtain vehicle trajectory points and vehicle trajectory data that match each lane in the first preset area.

[0103] The determining module may specifically include:

[0104] The first determining unit is used to determine a second preset area containing the target vehicle based on the vehicle location information of the target vehicle; the second preset area is an area with a preset size and preset shape determined with the target vehicle as the center.

[0105] The second determining unit is used to determine the target lane that best matches the target vehicle based on the preset lane data of each lane in the second preset area.

[0106] Optionally, the preset lane data includes at least one of vehicle trajectory points and vehicle trajectory data.

[0107] The second determining unit may specifically include:

[0108] The first determining subunit is used to determine the total number of vehicle trajectory points contained in each lane within the second preset area.

[0109] The second determining subunit is used to determine the lane to which the maximum value among the total number of the vehicle's travel trajectory points belongs as the target lane with the highest matching degree with the target vehicle.

[0110] or,

[0111] The third determining subunit is used to determine the total number of vehicle trajectory data entries contained in each lane within the second preset area.

[0112] The fourth determining subunit is used to determine the lane to which the maximum value among the total number of vehicle trajectory data belongs is the target lane with the highest matching degree with the target vehicle.

[0113] Optionally, after the determining module, the system may further include:

[0114] The second generation unit is used to generate vehicle driving instructions for the target vehicle based on the drivable lane.

[0115] A sending unit is configured to send the vehicle driving instruction to the target vehicle, and the target vehicle is configured to drive in accordance with the drivable lane in response to the vehicle driving instruction.

[0116] Based on the same idea, this specification also provides devices corresponding to the above methods in its embodiments.

[0117] Figure 3 The embodiments provided in this specification correspond to Figure 3 A schematic diagram of a vehicle lane determination device. Figure 3 As shown, device 300 may include:

[0118] At least one processor 310; and,

[0119] Memory 330 communicatively connected to the at least one processor; wherein,

[0120] The memory 330 stores instructions 320 that can be executed by the at least one processor 310, the instructions being executed by the at least one processor 310 to enable the at least one processor 310 to:

[0121] Obtain the vehicle location information of the target vehicle.

[0122] Based on the vehicle location information, preset lane data for each lane within a first preset area is obtained; the preset lane data is generated based on historical vehicle perception data collected by the sensing device for the first preset area within a first preset time period; the first preset time period is any time period within a second preset time period before a specified time, and the specified time is a time period earlier than the time when the vehicle location information is obtained by a preset time.

[0123] Based on the preset lane data of each lane in the first preset area, the target lane with the highest matching degree with the target vehicle is determined from each lane, and the drivable lane of the target vehicle is obtained.

[0124] It should be understood that in the methods described in one or more embodiments of this specification, the order of some steps may be adjusted according to actual needs, or some steps may be omitted.

[0125] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on its differences from other embodiments. In particular, for... Figure 3 As the device shown is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0126] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for determining a vehicle's drivable lane, characterized in that, include: Obtain the vehicle location information of the target vehicle; Based on the vehicle location information, obtain the preset lane data for each lane within the first preset area; The preset lane data is generated based on historical vehicle perception data collected by the sensing device for the first preset area within a first preset time period; the first preset time period is any time period within a second preset time period before a specified time, and the specified time is a time period earlier than the time when the vehicle location information is obtained. Based on the preset lane data of each lane in the first preset area, the target lane with the highest matching degree with the target vehicle is determined from each lane, and the drivable lane of the target vehicle is obtained. The step of determining the target lane with the highest matching degree with the target vehicle from the preset lane data of each lane in the first preset area, and obtaining the drivable lane of the target vehicle, specifically includes: Based on the vehicle location information of the target vehicle, a second preset area containing the target vehicle is determined; the second preset area is an area with a preset size and preset shape, centered on the target vehicle. Based on the preset lane data of each lane in the second preset area, the target lane with the highest matching degree with the target vehicle is determined; The preset lane data includes at least one of vehicle trajectory points and vehicle trajectory data; The step of determining the target lane with the highest matching degree with the target vehicle based on the preset lane data of each lane in the second preset area specifically includes: Determine the total number of vehicle trajectory points contained in each lane within the second preset area; The lane corresponding to the maximum value among the total number of vehicle trajectory points is determined as the target lane with the highest matching degree to the target vehicle; or... Determine the total number of vehicle trajectory data entries contained in each lane within the second preset area; The lane to which the maximum value in the total number of vehicle trajectory data is assigned is determined as the target lane with the highest matching degree with the target vehicle; Its preset shape includes any one of the following: circle, isosceles triangle, square, regular polygon. The preset size means that the distance from the vehicle position to each side of the shape is equal, and each distance is greater than or equal to the width of a lane and less than or equal to the width of the road.

2. The method as described in claim 1, characterized in that, Before obtaining the vehicle location information of the target vehicle, the process also includes: Determine whether the time interval between the most recent generation time of the preset lane data for each lane in the first preset area and the current time reaches the third preset duration; If the time interval reaches the third preset duration, then based on any historical vehicle perception data within the first preset duration within the time interval, preset lane data for each lane in the first preset area is generated, wherein the historical vehicle perception data is vehicle perception data collected by the sensing device for the first preset area.

3. The method as described in claim 2, characterized in that, The step of generating preset lane data for each lane within the first preset area based on historical vehicle perception data within any one of the first preset time periods within the time interval specifically includes: Obtain the location information of each vehicle in the historical vehicle perception data within any one of the first preset time periods; Based on the location information of each vehicle, vehicle trajectory data for each vehicle is generated. Based on the degree of overlap between the different vehicle trajectory data, the vehicle trajectory data of each vehicle is classified according to the number of lanes in the first preset area to obtain vehicle trajectory data that matches each lane in the first preset area.

4. The method as described in claim 2, characterized in that, The step of generating preset lane data for each lane within the first preset area based on historical vehicle perception data within any one of the first preset time periods within the time interval specifically includes: Obtain the position information and heading angle information of each vehicle in the historical vehicle perception data within any one of the first preset time periods; Based on the position information and heading angle information of each vehicle, the vehicle trajectory points of each vehicle are generated. Based on the angle difference between the heading angles of different vehicle trajectory points, the vehicle trajectory points of each vehicle are classified according to the number of lanes in the first preset area to obtain vehicle trajectory points that match each lane in the first preset area.

5. The method as described in claim 2, characterized in that, The step of generating preset lane data for each lane within the first preset area based on historical vehicle perception data within any one of the first preset time periods within the time interval specifically includes: Obtain the position information and heading angle information of each vehicle in the historical vehicle perception data within any one of the first preset time periods; Based on the position information and heading angle information of each vehicle, generate the vehicle trajectory points and vehicle trajectory data for each vehicle. Based on the angular difference between the heading angles of different vehicle trajectory points and the degree of overlap between different vehicle trajectory data, the vehicle trajectory points and vehicle trajectory data of each vehicle are classified according to the number of lanes in the first preset area to obtain vehicle trajectory points and vehicle trajectory data that match each lane in the first preset area.

6. The method as described in claim 1, characterized in that, After determining the target lane with the highest matching degree to the target vehicle from the preset lane data of each lane in the first preset area, and obtaining the drivable lane for the target vehicle, the method further includes: Based on the drivable lane, a vehicle driving instruction is generated for the target vehicle. The vehicle driving instruction is sent to the target vehicle, which is then used to drive in accordance with the drivable lane in response to the vehicle driving instruction.

7. A vehicle lane determination device, characterized in that, include: The first acquisition module is used to acquire the vehicle location information of the target vehicle; The second acquisition module is used to acquire preset lane data for each lane in the first preset area based on the vehicle location information. The preset lane data is generated based on historical vehicle perception data collected by the sensing device for the first preset area within a first preset time period; the first preset time period is any time period within a second preset time period before a specified time, and the specified time is a time period earlier than the time when the vehicle location information is obtained. The determination module is used to determine the target lane with the highest matching degree with the target vehicle from the preset lane data of each lane in the first preset area, and obtain the drivable lane of the target vehicle. The step of determining the target lane with the highest matching degree with the target vehicle from the preset lane data of each lane in the first preset area, and obtaining the drivable lane of the target vehicle, specifically includes: Based on the vehicle location information of the target vehicle, a second preset area containing the target vehicle is determined; the second preset area is an area with a preset size and preset shape, centered on the target vehicle. Based on the preset lane data of each lane in the second preset area, the target lane with the highest matching degree with the target vehicle is determined; The preset lane data includes at least one of vehicle trajectory points and vehicle trajectory data; The step of determining the target lane with the highest matching degree with the target vehicle based on the preset lane data of each lane in the second preset area specifically includes: Determine the total number of vehicle trajectory points contained in each lane within the second preset area; The lane corresponding to the maximum value among the total number of vehicle trajectory points is determined as the target lane with the highest matching degree to the target vehicle; or... Determine the total number of vehicle trajectory data entries contained in each lane within the second preset area; The lane to which the maximum value in the total number of vehicle trajectory data is assigned is determined as the target lane with the highest matching degree with the target vehicle; Its preset shape includes any one of the following: circle, isosceles triangle, square, regular polygon. The preset size means that the distance from the vehicle position to each side of the shape is equal, and each distance is greater than or equal to the width of a lane and less than or equal to the width of the road.

8. A vehicle lane determination device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to: Obtain the vehicle location information of the target vehicle; Based on the vehicle location information, preset lane data for each lane within a first preset area is obtained; the preset lane data is generated based on historical vehicle perception data collected by the sensing device for the first preset area within a first preset time period; the first preset time period is any time period within a second preset time period before a specified time, and the specified time is a time period earlier than the time when the vehicle location information is obtained. Based on the preset lane data of each lane in the first preset area, the target lane with the highest matching degree with the target vehicle is determined from each lane, and the drivable lane of the target vehicle is obtained. The step of determining the target lane with the highest matching degree with the target vehicle from the preset lane data of each lane in the first preset area, and obtaining the drivable lane of the target vehicle, specifically includes: Based on the vehicle location information of the target vehicle, a second preset area containing the target vehicle is determined; the second preset area is an area with a preset size and preset shape, centered on the target vehicle. Based on the preset lane data of each lane in the second preset area, the target lane with the highest matching degree with the target vehicle is determined; The preset lane data includes at least one of vehicle trajectory points and vehicle trajectory data; The step of determining the target lane with the highest matching degree with the target vehicle based on the preset lane data of each lane in the second preset area specifically includes: Determine the total number of vehicle trajectory points contained in each lane within the second preset area; The lane corresponding to the maximum value among the total number of vehicle trajectory points is determined as the target lane with the highest matching degree to the target vehicle; or... Determine the total number of vehicle trajectory data entries contained in each lane within the second preset area; The lane to which the maximum value in the total number of vehicle trajectory data is assigned is determined as the target lane with the highest matching degree with the target vehicle; Its preset shape includes any one of the following: circle, isosceles triangle, square, regular polygon. The preset size means that the distance from the vehicle position to each side of the shape is equal, and each distance is greater than or equal to the width of a lane and less than or equal to the width of the road.

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