Vehicle driving lane discrimination method, apparatus and device, and storage medium

Abstract

The invention relates to a vehicle driving lane judgment method, device and equipment and a storage medium, and the method comprises the steps: obtaining the vehicle driving state data of a first vehicle, the position data of the first vehicle, and map data within a preset range away from the position of the first vehicle; determining distance data between the first vehicle and a lane center line corresponding to the lane data; determining first included angle data between the first vehicle and a lane center line corresponding to the lane data according to the vehicle driving state data, the position data and the lane data; according to the distance data and the first included angle data, first driving lane data corresponding to the first vehicle is determined, and by means of the technical scheme, driving lanes of surrounding traffic participants can be accurately judged in various complex environments; the conditions of emergency braking, unnecessary lane changing and the like caused by misjudgment of driving lanes of surrounding traffic participants are reduced, and the driving comfort and smoothness are improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent driving technology, and in particular to a vehicle lane identification method, device, equipment and storage medium. Background Art

[0002] In the field of intelligent driving, the importance of predicting the driving lanes of traffic participants around the vehicle is self-evident. Accurately predicting the driving lanes of traffic participants around the vehicle is not only the key to ensuring driving safety and improving driving efficiency, but also the basis for enhancing driving comfort and achieving collaborative driving. Accurately judging the driving lanes of surrounding traffic participants on the road plays a vital role in improving the accuracy of behavior predictions of surrounding vehicles.

[0003] In the existing judgment methods for the driving lanes of surrounding traffic participants on the road, the judgment is often made based on observation results or historical trajectories. However, when a sufficiently long historical trajectory cannot be obtained or the observation error is large, an accurate judgment cannot be made, which results in the risk of collision between the vehicle and the traffic participants around the vehicle, and low driving comfort and safety. Summary of the invention

[0004] In order to solve the above technical problems, the embodiments of the present application provide a technical solution for a vehicle driving lane identification method, device, computer equipment and storage medium, that is, the present application determines the distance data and first angle data between the first vehicle and the lane centerline corresponding to the lane data through the vehicle driving status data and position data of the first vehicle (surrounding traffic participants) and the lane data in the map data, so as to determine the first driving lane data corresponding to the first vehicle based on the distance data and the first angle data, so as to accurately judge the driving lanes of the surrounding traffic participants in various complex environments, so as to reduce the occurrence of emergency braking, unnecessary lane changes, etc. caused by misjudgment of the driving lanes of the surrounding traffic participants, and improve driving comfort and smoothness.

[0005] On the one hand, an embodiment of the present application provides a method for distinguishing a vehicle lane, the method comprising:

[0006] Acquire vehicle driving state data of a first vehicle, location data of the first vehicle, and map data within a preset range from the location of the first vehicle, wherein the first vehicle is a vehicle in a preset area around the second vehicle, and the map data includes a plurality of lane data;

[0007] Determining distance data between the first vehicle and a lane centerline corresponding to the lane data based on the position data and the lane data;

[0008] Determine first angle data between the first vehicle and a lane centerline corresponding to the lane data according to the vehicle driving state data, the position data and the lane data;

[0009] First driving lane data corresponding to the first vehicle is determined according to the distance data and the first angle data.

[0010] Furthermore, the vehicle driving state data includes vehicle speed data and historical trajectory data;

[0011] Correspondingly, determining first angle data between the first vehicle and a lane centerline corresponding to the lane data according to the vehicle driving state data, the position data and the lane data includes:

[0012] When the vehicle speed data does not meet a preset speed condition, determining the direction data corresponding to the first vehicle according to the historical trajectory data;

[0013] Determine first angle data between the first vehicle and the lane centerline corresponding to the lane data based on the orientation data, the position data, and a lane centerline corresponding to the lane data.

[0014] Further, determining first driving lane data corresponding to the first vehicle according to the distance data and the first angle data includes:

[0015] If the first angle data is greater than a preset angle threshold, determining second driving lane data corresponding to the first vehicle according to the distance data, where the second driving lane data is driving lane data adjacent to the first driving lane data;

[0016] Determine second angle data between the first vehicle and a lane centerline corresponding to the second lane data according to the vehicle driving state data, the position data and the second driving lane data;

[0017] When the second angle data satisfies a preset angle condition, the second driving lane data is determined as the first driving lane data corresponding to the first vehicle.

[0018] Further, the vehicle driving state data includes vehicle speed data, the map data includes traffic light data and driving topology data between the plurality of lane data, and the method further includes:

[0019] In a case where the vehicle speed data does not satisfy a preset speed condition, determining the area data where the first vehicle is located according to the position data;

[0020] In the case where the area data indicates a first area, determining a first candidate driving lane corresponding to the first vehicle according to the driving topology data, the first area being an area where lanes overlap with each other;

[0021] First target driving lane data corresponding to the first vehicle is determined according to the first candidate driving lane, the signal light data, and the vehicle traffic flow corresponding to the first candidate driving lane.

[0022] Furthermore, the method further comprises:

[0023] In the case where the area data indicates a second area, determining a second candidate driving lane corresponding to the first vehicle according to the driving topology data, the second area being an area adjacent to the first area;

[0024] Acquiring vehicle driving data of a third vehicle on the second candidate driving lane;

[0025] Second target driving lane data corresponding to the first vehicle is determined according to the second candidate driving lane, the vehicle driving data, and the traffic light data.

[0026] Further, after determining the second target driving lane data corresponding to the first vehicle according to the second candidate driving lane, the vehicle driving data and the signal light data, the method further includes:

[0027] When the second driving lane data indicates that the number of driving lanes is greater than a preset number threshold, acquiring a perception size corresponding to the first vehicle;

[0028] When the perceived size meets a preset size condition, third target driving lane data corresponding to the first vehicle is determined according to the distance data, and the second target driving lane data includes the third target driving lane data.

[0029] Further, determining the third target driving lane data corresponding to the first vehicle according to the distance data includes:

[0030] In a case where the distance data indicates that the first vehicle is located on the left side of the lane centerline, determining the driving lane data close to the left side of the lane centerline in the second target driving lane data as the third target driving lane data;

[0031] When the distance data indicates that the first vehicle is located on the right side of a lane centerline, driving lane data close to the right side of the lane centerline in the second target driving lane data is determined as the third target driving lane data.

[0032] On the other hand, an embodiment of the present application provides a vehicle lane identification device, the device comprising:

[0033] a data acquisition module, configured to acquire vehicle driving state data of a first vehicle, location data of the first vehicle, and map data within a preset range from the location of the first vehicle, wherein the first vehicle is a vehicle in a preset area around the second vehicle, and the map data includes data of a plurality of lanes;

[0034] a distance data determination module, configured to determine, based on the position data and the lane data, distance data between the first vehicle and a lane centerline corresponding to the lane data;

[0035] A first angle data determination module, configured to determine first angle data between the first vehicle and a lane centerline corresponding to the lane data according to the vehicle driving state data, the position data and the lane data;

[0036] The first driving lane data determination module is used to determine the first driving lane data corresponding to the first vehicle according to the distance data and the first angle data.

[0037] On the other hand, a vehicle lane determination device is provided, which includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the vehicle lane determination method as described above.

[0038] On the other hand, a computer-readable storage medium is provided, in which at least one instruction, at least one program, a code set or an instruction set is stored. The at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by a processor to implement the vehicle lane determination method as described above.

[0039] The implementation of this application has the following beneficial effects:

[0040] The present application determines the distance data and first angle data between the first vehicle and the lane centerline corresponding to the lane data through the vehicle driving status data and position data of the first vehicle (surrounding traffic participants) and the lane data in the map data, so as to determine the first driving lane data corresponding to the first vehicle based on the distance data and the first angle data, so as to accurately judge the driving lanes of the surrounding traffic participants in various complex environments, so as to reduce the occurrence of emergency braking, unnecessary lane changes, etc. caused by misjudgment of the driving lanes of the surrounding traffic participants, and improve driving comfort and smoothness. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 A schematic diagram of a flow chart of a vehicle lane identification method provided in an embodiment of the present application;

[0043] Figure 2 A schematic diagram of a flow chart of a method for determining first angle data provided in an embodiment of the present application;

[0044] Figure 3 A schematic diagram of a scenario in which, when the first vehicle is at an intersection, the orientation of the first vehicle sensed by the second vehicle and the orientation of the first vehicle determined based on the historical trajectory are provided in an embodiment of the present application;

[0045] Figure 4 A schematic diagram of a flow chart of a first driving lane data determination method provided in an embodiment of the present application;

[0046] Figure 5 A schematic diagram of a flow chart of a method for determining first target driving lane data provided in an embodiment of the present application;

[0047] Figure 6 A schematic diagram of a scene defining an intersection area provided in an embodiment of the present application;

[0048] Figure 7 A schematic diagram of a scenario for all possible first candidate driving lanes for a first vehicle provided in an embodiment of the present application;

[0049] Figure 8 A schematic diagram of a scenario for inferring using pedestrian data provided in an embodiment of the present application;

[0050] Fig. 9 A schematic diagram of a flow chart of a method for determining second target driving lane data provided in an embodiment of the present application;

[0051] Fig.10 A schematic diagram of a scenario for all possible second candidate driving lanes for a first vehicle provided in an embodiment of the present application;

[0052] Fig.11 A schematic diagram of a scenario of all possible second candidate driving lanes for the first vehicle when the first vehicle is in the second area provided in an embodiment of the present application;

[0053] Fig.12A schematic diagram of a flow chart of a method for determining third target driving lane data provided in an embodiment of the present application;

[0054] Fig.13 A schematic diagram of the structure of a vehicle lane identification device provided in an embodiment of the present application;

[0055] Fig.14 A schematic diagram of the structure of a first angle data determination module provided in an embodiment of the present application;

[0056] Fig.15 A schematic diagram of the structure of a first driving lane data determination module provided in an embodiment of the present application;

[0057] Fig.16 A schematic diagram of the structure of a first target driving lane data determination module provided in an embodiment of the present application;

[0058] Fig.17 A schematic diagram of the structure of a second target driving lane data determination module provided in an embodiment of the present application;

[0059] Fig.18 A schematic diagram of the structure of a server provided in an embodiment of the present application. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0061] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0062] See also Figure 1 , which is a flow chart of a vehicle lane identification method provided by an embodiment of the present application, and the following is combined with Figure 1The technical solution of the present application is described in detail. It should be noted that this specification provides method operation steps as described in the embodiments or flow charts, but more or fewer operation steps may be included based on conventional or non-creative work. The order of steps listed in the embodiments is only one way of executing the steps among many, and does not represent the only execution order. The method specifically includes the following steps:

[0063] S101: Acquire vehicle driving status data of a first vehicle, location data of the first vehicle, and map data within a preset range from the location of the first vehicle, wherein the first vehicle is a vehicle in a preset area around a second vehicle, and the map data includes data of multiple lanes.

[0064] In an embodiment of the present application, the first vehicle is a traffic participant in a preset area around the second vehicle, that is, the driving of the first vehicle may affect the driving of the second vehicle, wherein the preset area can be an area formed by the second vehicle as the center and a radius of 100m, and the second vehicle is a vehicle used to determine the driving lane of the first vehicle. Furthermore, by accurately determining the driving lane of the first vehicle, it helps to optimize the driving decision of the second vehicle, so that the second vehicle (autonomous driving vehicle) can make more reasonable and efficient driving decisions, such as changing lanes, overtaking and other driving decisions.

[0065] In a specific embodiment, the vehicle driving status data is the status data of the first vehicle during driving. Exemplarily, the vehicle driving status data includes but is not limited to vehicle speed data, vehicle driving direction data and historical trajectory data. The position data of the first vehicle is the position of the first vehicle in the map. The map data within a preset range from the position of the first vehicle is data about lane information on the map within a preset range around the first vehicle. Exemplarily, the map data within a preset range from the position of the first vehicle includes but is not limited to lane data, driving topology data between multiple lane data and traffic sign data. The preset range is a range formed by the first vehicle as the center and a radius of 100m.

[0066] Furthermore, the present application can determine the distance data and first angle data between the first vehicle and the lane centerline corresponding to the lane data through the vehicle driving status data and position data of the first vehicle, as well as the lane data in the map data, so as to determine the first driving lane data corresponding to the first vehicle based on the distance data and the first angle data, thereby being able to accurately judge the driving lanes of surrounding traffic participants in various complex environments, so as to reduce the occurrence of emergency braking, unnecessary lane changes, etc. caused by misjudgment of the driving lanes of surrounding traffic participants, and improve driving comfort and smoothness.

[0067] S102: Determine distance data between the first vehicle and a lane centerline corresponding to the lane data based on the position data and the lane data.

[0068] Specifically, the distance data between the first vehicle and the lane centerline corresponding to the lane data is the distance from the center point of the first vehicle to the lane centerlines corresponding to each of the multiple lane data. That is to say, the distance data between the first vehicle and the lane centerline corresponding to the lane data can be obtained by drawing a perpendicular line from the center point of the first vehicle to the lane centerlines corresponding to each of the multiple lane data, so as to obtain the distance data between the first vehicle and the lane centerlines corresponding to each of the multiple lane data.

[0069] In actual applications, it is only necessary to determine the distance data between the first vehicle traveling relative to the second vehicle and the lane centerline corresponding to the lane data relative to the lane in which the second vehicle is traveling, because only the first vehicle that meets the above driving conditions can have an impact on the driving decision of the second vehicle. Furthermore, the distance calculation efficiency can be improved by only calculating the distance from the first vehicle that meets the driving conditions to the lane centerlines corresponding to each of the multiple lane data.

[0070] S103: Determine first angle data between the first vehicle and a lane centerline corresponding to the lane data according to the vehicle driving state data, the position data and the lane data.

[0071] S104: Determine first driving lane data corresponding to the first vehicle according to the distance data and the first angle data.

[0072] In the embodiment of the present application, the first angle data is the angle between the driving direction of the first vehicle and the center line of the lane. It should be noted that when the first vehicle is in a turning state, the first angle data is the angle between the driving direction of the first vehicle and the straight line where the lane representative point closest to the first vehicle and the lane representative point in front of the lane representative point are connected, wherein the lane representative point is a representative point on the lane center line, and there is the same preset interval between its adjacent lane representative points, and the lane representative point in front of the lane representative point is a lane representative point adjacent to the lane representative point along the driving direction of the first vehicle. The lane representative point closest to the first vehicle can be determined from the determined distance data between the first vehicle and the lane center lines corresponding to each of the multiple lane data, that is, the smallest distance in the distance data between the first vehicle and the lane center lines corresponding to each of the multiple lane data is selected, and then the lane representative point on the lane center line with the smallest distance is the lane representative point closest to the first vehicle.

[0073] In a specific embodiment, the vehicle driving status data includes vehicle speed data and vehicle driving direction data. When the vehicle speed data meets the preset speed condition, the first angle data between the first vehicle and the lane centerline corresponding to the lane data can be determined directly according to the vehicle driving direction data, position data and lane data. The preset speed condition is that the vehicle speed data is greater than a preset speed threshold. Exemplarily, the preset speed threshold may be 5km / h, 10km / h or 15km / h, etc. Preferably, the preset speed threshold is 10km / h. It should be noted that when the vehicle speed data meets the preset speed condition, it indicates that the vehicle driving direction data of the first vehicle perceived by the second vehicle is more stable, that is, the first vehicle has not slowed down to prepare for turning.

[0074] In actual applications, when the lane data is determined, the lane centerline corresponding to the lane data can be obtained, and then the first angle data between the lane representative point closest to the first vehicle and the lane representative point in front of the lane representative point and the orientation data of the first vehicle are determined. If the obtained first angle data is less than the preset angle threshold, the first driving lane data corresponding to the first vehicle can be directly determined, that is, the driving lane where the first vehicle is located, wherein the driving lane where the first vehicle is located is the lane where the lane representative point closest to the first vehicle is located. If the obtained first angle data is greater than the preset angle threshold, it indicates that the determined lane is not the most accurate driving lane, and then it can be determined whether the angle between the driving lane adjacent to the driving lane and the orientation data of the first vehicle meets the preset angle condition, so as to accurately obtain the driving lane where the first vehicle is located.

[0075] In an optional embodiment, the vehicle driving state data includes vehicle speed data and historical trajectory data; accordingly, Figure 2 As shown, it is a schematic flow chart of the method for determining the first angle data provided in an embodiment of the present application, and the specific step S103 includes:

[0076] S1031: When the vehicle speed data does not meet the preset speed condition, determine the direction data corresponding to the first vehicle according to the historical trajectory data;

[0077] S1032: Determine first angle data between the first vehicle and the lane centerline corresponding to the lane data based on the orientation data, the position data, and the lane centerline corresponding to the lane data.

[0078] In an embodiment of the present application, when the vehicle speed data does not meet the preset speed condition, it indicates that the vehicle speed data is less than the preset speed threshold, that is, the speed of the first vehicle is slower. When the driving speed of the first vehicle is slower, due to the limitations of perception, the driving direction data of the first vehicle perceived by the second vehicle has a great deviation. When the second vehicle can obtain the historical trajectory data of the first vehicle, and the length of the acquired historical trajectory data is greater than the preset length, the direction data corresponding to the first vehicle can be determined according to the historical trajectory data of the preset length, so as to determine the first angle data between the first vehicle and the lane centerline corresponding to the lane data according to the direction data, the position data and the lane centerline corresponding to the lane data, thereby improving the accuracy of the angle between the first vehicle and the lane centerline, and solving the technical problem of misjudging the direction data of the first vehicle due to the excessively low speed of the first vehicle.

[0079] For example, Figure 3 As shown, it is a schematic diagram of a scenario provided by an embodiment of the present application of the orientation of the first vehicle perceived by the second vehicle and the orientation of the first vehicle determined based on the historical trajectory when the first vehicle is at an intersection, wherein the vehicle located on the driving lane labeled 3 is the first vehicle, the dotted lines without arrows located on labels 1 and 3 are the historical trajectory data of the first vehicle, the solid line with an arrow located on the driving lane labeled 3 is the orientation data of the first vehicle obtained based on the historical trajectory data, the dotted line with an arrow located on the driving lane labeled 3 is the orientation data perceived by the second vehicle, and the orientation of the first vehicle can be determined based on the historical trajectory data as the orientation data determined based on the historical trajectory data, and further, when the vehicle speed data of the first vehicle does not meet the preset speed condition and the historical trajectory data of a preset length can be obtained, the actual orientation data of the first vehicle can be determined based on the historical trajectory data, so as to improve the accurate judgment of the orientation data of the first vehicle.

[0080] In an optional embodiment, if Figure 4 As shown, it is a flow chart of the first driving lane data determination method provided in an embodiment of the present application, and the specific step S104 includes:

[0081] S1041: If the first angle data is greater than a preset angle threshold, determine second driving lane data corresponding to the first vehicle according to the distance data, where the second driving lane data is driving lane data adjacent to the first driving lane data;

[0082] S1042: Determine second angle data between the first vehicle and a lane centerline corresponding to the second driving lane data according to the vehicle driving state data, the position data, and the second driving lane data;

[0083] S1043: When the second angle data satisfies a preset angle condition, the second driving lane data is determined as the first driving lane data corresponding to the first vehicle.

[0084] In an embodiment of the present application, if the obtained first angle data is greater than a preset angle threshold, it indicates that the currently determined lane is not the most accurate driving lane, and then it can be determined whether the angle between the driving lane adjacent to the driving lane and the orientation data of the first vehicle meets the preset angle condition, that is, according to the distance data, the driving lane data adjacent to the first driving lane data, that is, the second driving lane data corresponding to the first vehicle is determined, so as to determine the second angle data between the first vehicle and the lane centerline corresponding to the second driving lane data according to the vehicle driving state data, position data and the second driving lane data, wherein the method for determining the second angle data is the same as the method for determining the first angle data, and if the second angle data meets the preset angle condition, the second driving lane data is determined as the first driving lane data corresponding to the first vehicle, that is, the second driving lane data is determined as the driving lane corresponding to the first vehicle, thereby improving the accuracy of the driving lane judgment.

[0085] Exemplarily, the preset angle threshold may be 25 degrees, 30 degrees or 35 degrees, etc. Preferably, the preset angle threshold is 30 degrees.

[0086] In an optional embodiment, the vehicle driving state data includes vehicle speed data, and the map data includes traffic light data and driving topology data between multiple lane data, such as Figure 5 As shown, it is a flow chart of a method for determining first target driving lane data provided in an embodiment of the present application. Specifically, the method further includes:

[0087] S1051: When the vehicle speed data does not meet the preset speed condition, determine the area data where the first vehicle is located according to the position data;

[0088] S1052: When the area data indicates a first area, determine a first candidate driving lane corresponding to the first vehicle according to the driving topology data, the first area being an area where lanes overlap with each other;

[0089] S1053: Determine first target driving lane data corresponding to the first vehicle based on the first candidate driving lane, the traffic light data, and the vehicle traffic flow corresponding to the first candidate driving lane.

[0090] In an embodiment of the present application, when the vehicle speed data does not meet the preset speed condition, it indicates that the vehicle speed data is less than the preset speed threshold, that is, the speed of the first vehicle is slower. For example, when the first vehicle is at an intersection, the first vehicle needs to slow down in order to turn or wait for a red light, and thus, the speed of the first vehicle is lower. At the same time, the second vehicle is unable to obtain the historical trajectory data of the first vehicle, or, when the length of the acquired historical trajectory data is less than the preset length, the area data of the first vehicle is determined according to the position data, so that different driving lane judgment methods can be performed according to the different areas where the first vehicle is located, thereby improving the accurate judgment of the driving lane, thereby reducing emergency braking, unnecessary lane changes, etc. caused by misjudgment of the driving lane, and improving driving comfort and smoothness.

[0091] Specifically, the area data includes a first area and a second area, the first area is an area where lanes overlap with each other, and the second area is an area adjacent to the first area. For example, Figure 6 As shown, it is a schematic diagram of the scene definition of the intersection area provided in an embodiment of the present application, wherein area O is the first area, area I is the second area, the red vehicle is the first vehicle, and the blue vehicle is the second vehicle.

[0092] In a specific embodiment, the driving topology data between the plurality of lane data is the driving topology relationship between the driving lanes, that is, the connectivity and relative position relationship between the driving lanes on the road. Then, when the area data indicates the first area, the first candidate driving lane corresponding to the first vehicle can be determined according to the driving topology data. Specifically, Figure 7 As shown, it is a schematic diagram of a scenario of all possible first candidate driving lanes for the first vehicle provided by an embodiment of the present application, wherein the second vehicle drives in the direction of the arrow in front of the second vehicle, and the first vehicle drives in the direction opposite to the direction of the arrow in front of the second vehicle or turns, thereby Figure 7 It can be seen that the first candidate driving lane for the first vehicle currently includes three lanes, namely, number 1, number 4 and number 3. Then, according to the first candidate driving lane, the signal light data and the corresponding vehicle traffic flow on the first candidate driving lane, the first target driving lane data corresponding to the first vehicle can be determined, so as to accurately judge the lane in which the first vehicle is traveling when the first vehicle is located in the first area and the second vehicle cannot obtain the historical trajectory data of the first vehicle, or the length of the obtained historical trajectory data is less than the preset length.

[0093] In practical applications, the following Figure 7 The judgment of the first target driving lane data of the first vehicle is described in detail. Specifically, when the first vehicle stops in the following Figure 74. If the second vehicle senses that the vehicle traffic flow on the first candidate driving lane labeled 4 is greater than a preset traffic flow threshold, illustratively, the preset traffic flow threshold is that more than a preset number of vehicles pass through within a preset time, for example, 5 vehicles pass through within 30 seconds, then the first vehicle will not stop on the first candidate driving lane labeled 4, and the first vehicle can be excluded from being located in the first candidate driving lane labeled 4, and the number of the first candidate driving lanes can be further reduced according to the signal light data. Specifically, the signal light in the direction of the second vehicle's advance is green, and the intersection The signal light for the direction is red, and thus the first vehicle can be excluded from traveling on the first alternate driving lane labeled 1. Because, if the first vehicle is traveling on the first alternate driving lane labeled 1, the first vehicle will stop behind the crosswalk instead of in the middle of the intersection, or will travel slowly in the middle of the intersection. Therefore, it can be determined that the first vehicle is traveling on the first alternate driving lane labeled 3 and is very likely to be turning left. At this time, the second vehicle can perform corresponding operations such as deceleration to avoid collision with the first vehicle, or perform emergency braking, etc., thereby improving driving comfort and smoothness, and thus making more reasonable and efficient driving decisions.

[0094] In another specific embodiment, if the traffic light data cannot be obtained, the pedestrian data on the crosswalk can be obtained, and then the traffic light data can be inferred based on the pedestrian data. For details, please refer to Figure 8 , which is a schematic diagram of a scenario for inference using pedestrian data provided in an embodiment of the present application. If there are pedestrians passing through crosswalk 1 and there are pedestrians waiting to pass through crosswalk 2, it can be determined that the second vehicle's forward direction is green and the signal light in the fork direction is red. Then, the first target driving lane data corresponding to the first vehicle can be determined based on the first candidate driving lane, the signal light data and the corresponding vehicle traffic flow on the first candidate driving lane.

[0095] In an optional embodiment, if Fig. 9 As shown, it is a flow chart of a method for determining second target driving lane data provided in an embodiment of the present application. Specifically, the method further includes:

[0096] S1061: When the area data indicates a second area, determine a second candidate driving lane corresponding to the first vehicle according to the driving topology data, and the second area is an area adjacent to the first area;

[0097] S1062: Acquire vehicle driving data of a third vehicle in the second candidate driving lane;

[0098] S1063: Determine the second target driving lane data corresponding to the first vehicle according to the second candidate driving lane, the vehicle driving data and the traffic light data.

[0099] Specifically, when the area data indicates the second area, the second candidate driving lane corresponding to the first vehicle may be determined according to the driving topology data. Specifically, Fig.10 As shown, it is a schematic diagram of a scenario of all possible second candidate driving lanes for the first vehicle provided by an embodiment of the present application, wherein the second vehicle is traveling in a direction close to a traffic light, and the first vehicle is traveling in a direction opposite to the driving direction of the second vehicle or is turning, thereby Fig.10 It can be seen that the second candidate driving lanes for the current first vehicle include three lanes, namely, numbered 1, numbered 2 and numbered 3. Then, the second target driving lane data corresponding to the first vehicle can be determined according to the second candidate driving lanes, vehicle driving data and traffic light data, so as to accurately determine the lane in which the first vehicle is traveling when the first vehicle is located in the second area and the second vehicle cannot obtain the historical trajectory data of the first vehicle, or when the length of the obtained historical trajectory data is less than the preset length.

[0100] In practical applications, such as Fig.11 As shown, it is a schematic diagram of a scenario of all possible second candidate driving lanes for the first vehicle when the first vehicle is in the second area provided by an embodiment of the present application. Fig.11 The determination of the second target driving lane data of the first vehicle is described in detail, specifically, Fig.11 As shown, the second candidate driving lane numbered 2 is not congested, and the traffic light data in front of the first vehicle is green, then the first vehicle will not stop in the Fig.11 The position shown in the figure, or the first vehicle is traveling at an extremely slow speed, and the possibility that the first vehicle continues to travel in the second candidate driving lane labeled 2 is extremely small, so it can be ruled out that the first vehicle is traveling in the second candidate driving lane labeled 2. Furthermore, there is a portion of the third vehicle to be driven in the second candidate driving lane labeled 3. If the first vehicle is traveling in the second candidate driving lane labeled 3, it will not stop at Fig.11 The first vehicle does not move in the position shown in the figure, but stays in the center of the intersection without obstructing traffic, thereby eliminating the possibility that the first vehicle is driving on the second alternate driving lane labeled 3, and further determining that the first vehicle is driving on the second alternate driving lane labeled 1 and is most likely turning right, so the first vehicle will not affect the driving of the second vehicle, and the second vehicle can drive at a normal speed.

[0101] Furthermore, if there are many third vehicles to be driven on the second candidate driving lane numbered 3, that is, because there are many third vehicles to be driven on the second candidate driving lane numbered 3, the first vehicle stops at Fig.11When the position shown is , it is impossible to determine whether the first vehicle is traveling in the second candidate driving lane numbered 3 or the second candidate driving lane numbered 1, and then the determination can be made based on the third target driving lane data determination method described below. Specifically, Fig.12 As shown, it is a flow chart of a method for determining the third target driving lane data provided in an embodiment of the present application. After step S1063, the method further includes:

[0102] S1064: When the second driving lane data indicates that the number of driving lanes is greater than a preset number threshold, obtaining a perception size corresponding to the first vehicle;

[0103] S1065: When the perceived size meets the preset size condition, determine the third target driving lane data corresponding to the first vehicle according to the distance data, and the second target driving lane data includes the third target driving lane data.

[0104] In an embodiment of the present application, the preset number threshold is 1, and the preset size condition is that the perceived size is greater than 80% of the complete size of the first vehicle. Then, the third target driving lane data corresponding to the first vehicle can be determined based on the distance data, thereby further reducing the number of second target driving lane data. It should be noted that the numerical value corresponding to the distance data includes positive and negative values. When located on the left side of the lane centerline, the calculated numerical value corresponding to the distance data is a negative value, and when located on the right side of the lane centerline, the calculated numerical value corresponding to the distance data is a positive value.

[0105] Furthermore, in a specific embodiment, step S1065 may include:

[0106] S10651: When the distance data indicates that the first vehicle is located on the left side of the lane centerline, determine the driving lane data close to the left side of the lane centerline in the second target driving lane data as the third target driving lane data;

[0107] S10652: When the distance data indicates that the first vehicle is located on the right side of the lane centerline, determine the driving lane data close to the right side of the lane centerline in the second target driving lane data as the third target driving lane data.

[0108] Specifically, when the distance data indicates that the center point of the first vehicle is located on the left side of the center line of the lane, it means that the calculated distance data is a negative value, and it can be determined that the first vehicle is very likely to be traveling on the second candidate driving lane labeled 1. Therefore, the first vehicle will not affect the driving of the second vehicle, and the second vehicle can travel at a normal speed. When the distance data indicates that the center point of the first vehicle is located on the right side of the center line of the lane, it means that the calculated distance data is a positive value, and it can be determined that the first vehicle is very likely to be traveling on the second candidate driving lane labeled 3. At this time, the second vehicle can perform corresponding deceleration and other operations to avoid collision with the first vehicle, or perform emergency braking and other situations, thereby improving driving comfort and smoothness, and making more reasonable and efficient driving decisions.

[0109] It can be seen from the above technical solutions of the embodiments of the present application that the following technical effects are achieved:

[0110] The present application determines the distance data and first angle data between the first vehicle and the lane centerline corresponding to the lane data through the vehicle driving status data and position data of the first vehicle, as well as the lane data in the map data, so as to determine the first driving lane data corresponding to the first vehicle based on the distance data and the first angle data, thereby being able to accurately judge the driving lanes of surrounding traffic participants in various complex environments, so as to reduce the occurrence of emergency braking, unnecessary lane changes, etc. caused by misjudgment of the driving lanes of surrounding traffic participants, and improve driving comfort and smoothness.

[0111] The present application also provides a vehicle lane identification device, such as Fig.13 As shown, it is a structural schematic diagram of a vehicle lane identification device provided in an embodiment of the present application, and the vehicle lane identification device includes:

[0112] The data acquisition module 10 is used to acquire vehicle driving status data of the first vehicle, location data of the first vehicle and map data within a preset range from the location of the first vehicle. The first vehicle is a vehicle in a preset area around the second vehicle, and the map data includes multiple lane data.

[0113] The distance data determination module 20 is used to determine the distance data between the first vehicle and the lane centerline corresponding to the lane data according to the position data and the lane data.

[0114] The first angle data determining module 30 is used to determine the first angle data between the first vehicle and the lane centerline corresponding to the lane data according to the vehicle driving state data, the position data and the lane data.

[0115] The first driving lane data determining module 40 is used to determine the first driving lane data corresponding to the first vehicle according to the distance data and the first angle data.

[0116] Furthermore, the vehicle driving state data includes vehicle speed data and historical trajectory data. Accordingly, Fig.14 As shown, it is a schematic diagram of the structure of the first angle data determination module provided in an embodiment of the present application. Specifically, the first angle data determination module 30 includes:

[0117] The direction data determining unit 301 is used to determine the direction data corresponding to the first vehicle according to the historical trajectory data when the vehicle speed data does not meet the preset speed condition.

[0118] The first angle data determining unit 302 is used to determine first angle data between the first vehicle and the lane centerline corresponding to the lane data according to the orientation data, the position data and the lane centerline corresponding to the lane data.

[0119] Furthermore, if Fig.15 As shown, it is a structural diagram of the first driving lane data determination module provided in an embodiment of the present application. Specifically, the first driving lane data determination module 40 includes:

[0120] The second driving lane data determining unit 401 is used to determine the second driving lane data corresponding to the first vehicle according to the distance data if the first angle data is greater than a preset angle threshold, and the second driving lane data is the driving lane data adjacent to the first driving lane data.

[0121] The second angle data determining unit 402 is used to determine the second angle data between the first vehicle and the lane center line corresponding to the second driving lane data according to the vehicle driving state data, the position data and the second driving lane data.

[0122] The first driving lane data determining unit 403 is used to determine the second driving lane data as the first driving lane data corresponding to the first vehicle when the second angle data meets a preset angle condition.

[0123] Furthermore, the vehicle driving state data includes vehicle speed data, and the map data includes traffic light data and driving topology data between a plurality of lane data. Accordingly, Fig.16 As shown, it is a structural schematic diagram of a first target driving lane data determination module provided in an embodiment of the present application. Specifically, the device further includes:

[0124] The area data determination module 501 is used to determine the area data where the first vehicle is located according to the position data when the vehicle speed data does not meet the preset speed condition.

[0125] The first candidate driving lane determining module 502 is used to determine a first candidate driving lane corresponding to the first vehicle according to the driving topology data when the area data indicates a first area, and the first area is an area where lanes overlap with each other.

[0126] The first target driving lane data determination module 503 is used to determine the first target driving lane data corresponding to the first vehicle according to the first candidate driving lane, the signal light data and the vehicle traffic flow corresponding to the first candidate driving lane.

[0127] Furthermore, if Fig.17 As shown, it is a structural schematic diagram of a second target driving lane data determination module provided in an embodiment of the present application. Specifically, the device further includes:

[0128] The second candidate driving lane determining module 601 is used to determine a second candidate driving lane corresponding to the first vehicle according to the driving topology data when the area data indicates a second area, and the second area is an area adjacent to the first area.

[0129] The vehicle driving data determining module 602 is used to obtain the vehicle driving data of the third vehicle on the second candidate driving lane.

[0130] The second target driving lane data determination module 603 is used to determine the second target driving lane data corresponding to the first vehicle according to the second candidate driving lane, the vehicle driving data and the signal light data.

[0131] Furthermore, the device also includes:

[0132] The perceived size determination module 604 is configured to obtain a perceived size corresponding to the first vehicle when the second driving lane data indicates that the number of driving lanes is greater than a preset number threshold.

[0133] The third target driving lane data determination module 605 is used to determine the third target driving lane data corresponding to the first vehicle according to the distance data when the sensed size meets the preset size condition, and the second target driving lane data includes the third target driving lane data.

[0134] Further, the third target driving lane data determination module 605 includes:

[0135] The first determination unit 6051 is used to determine the driving lane data close to the left side of the lane centerline in the second target driving lane data as the third target driving lane data when the distance data indicates that the first vehicle is located on the left side of the lane centerline.

[0136] The second determination unit 6052 is used to determine the driving lane data close to the right side of the lane centerline in the second target driving lane data as the third target driving lane data when the distance data indicates that the first vehicle is located on the right side of the lane centerline.

[0137] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0138] An embodiment of the present application provides a vehicle lane determination device, which includes a processor and a memory, in which at least one instruction, at least one program, a code set or an instruction set is stored. The at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the vehicle lane determination method provided in the above method embodiment.

[0139] The memory can be used to store software programs and modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, application programs required for functions, etc.; the data storage area can store data created according to the use of the device, etc. In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory can also include a memory controller to provide the processor with access to the memory.

[0140] The vehicle lane identification device may be a server. The present application embodiment also provides a schematic diagram of the structure of a server. Fig.18, the server 1800 is used to implement the data processing method provided in the above embodiment. The server 1800 may have relatively large differences due to different configurations or performances, and may include one or more processors 1810 (for example, one or more processors) and storage 1830, one or more storage media 1820 (for example, one or more mass storage devices) storing application programs 1823 or data 1822. Among them, the memory 1830 and the storage medium 1820 can be short-term storage or persistent storage. The program stored in the storage medium 1820 may include one or more modules, each of which may include a series of instruction operations in the server. Furthermore, the processor 1810 can be configured to communicate with the storage medium 1820 and execute a series of instruction operations in the storage medium 1820 on the server 1800. The server 1800 may also include one or more power supplies 1860, one or more wired or wireless network interfaces 1850, one or more input and output interfaces 1840, and / or one or more operating systems 1821, such as Windows Server TM, Mac OS X TM, Unix TM, Linux TM, FreeBSD TM, etc.

[0141] An embodiment of the present application also provides a computer-readable storage medium, which can be set in a server to store at least one instruction, at least one program, code set or instruction set related to a vehicle lane discrimination method in a method embodiment. The at least one instruction, the at least one program, the code set or instruction set is loaded and executed by the processor to implement the vehicle lane discrimination method provided in the above method embodiment.

[0142] Optionally, in this embodiment, the storage medium may be located in at least one of the multiple network servers of the computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to, various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk, or an optical disk.

[0143] It should be noted that the above-mentioned sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above-mentioned specific embodiments of this specification are described. Other embodiments are within the scope of the attached claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0144] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system and server embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0145] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for distinguishing a vehicle lane, characterized in that: The method comprises: Acquire vehicle driving state data of a first vehicle, location data of the first vehicle, and map data within a preset range from the location of the first vehicle, wherein the first vehicle is a vehicle in a preset area around the second vehicle, and the map data includes a plurality of lane data; Determining distance data between the first vehicle and a lane centerline corresponding to the lane data based on the position data and the lane data; Determine first angle data between the first vehicle and a lane centerline corresponding to the lane data according to the vehicle driving state data, the position data and the lane data; First driving lane data corresponding to the first vehicle is determined according to the distance data and the first angle data.

2. The method according to claim 1, characterized in that The vehicle driving state data includes vehicle speed data and historical trajectory data; Correspondingly, determining first angle data between the first vehicle and a lane centerline corresponding to the lane data according to the vehicle driving state data, the position data and the lane data includes: When the vehicle speed data does not meet a preset speed condition, determining the direction data corresponding to the first vehicle according to the historical trajectory data; Determine first angle data between the first vehicle and the lane centerline corresponding to the lane data based on the orientation data, the position data, and a lane centerline corresponding to the lane data.

3. The method according to claim 1, characterized in that The determining, according to the distance data and the first angle data, first driving lane data corresponding to the first vehicle includes: If the first angle data is greater than a preset angle threshold, determining second driving lane data corresponding to the first vehicle according to the distance data, where the second driving lane data is driving lane data adjacent to the first driving lane data; Determine second angle data between the first vehicle and a lane centerline corresponding to the second lane data according to the vehicle driving state data, the position data and the second driving lane data; When the second angle data satisfies a preset angle condition, the second driving lane data is determined as the first driving lane data corresponding to the first vehicle.

4. The method according to claim 1, characterized in that: The vehicle driving state data includes vehicle speed data, the map data includes signal light data and driving topology data between the plurality of lane data, and the method further includes: In a case where the vehicle speed data does not satisfy a preset speed condition, determining the area data where the first vehicle is located according to the position data; In the case where the area data indicates a first area, determining a first candidate driving lane corresponding to the first vehicle according to the driving topology data, the first area being an area where lanes overlap with each other; First target driving lane data corresponding to the first vehicle is determined according to the first candidate driving lane, the signal light data, and the vehicle traffic flow corresponding to the first candidate driving lane.

5. The method according to claim 4, characterized in that The method further comprises: In the case where the area data indicates a second area, determining a second candidate driving lane corresponding to the first vehicle according to the driving topology data, the second area being an area adjacent to the first area; Acquiring vehicle driving data of a third vehicle on the second candidate driving lane; Second target driving lane data corresponding to the first vehicle is determined according to the second candidate driving lane, the vehicle driving data, and the traffic light data.

6. The method according to claim 5, characterized in that After determining the second target driving lane data corresponding to the first vehicle according to the second candidate driving lane, the vehicle driving data and the signal light data, the method further includes: When the second driving lane data indicates that the number of driving lanes is greater than a preset number threshold, acquiring a perception size corresponding to the first vehicle; When the perceived size meets a preset size condition, third target driving lane data corresponding to the first vehicle is determined according to the distance data, and the second target driving lane data includes the third target driving lane data.

7. The method according to claim 6, characterized in that Determining third target driving lane data corresponding to the first vehicle according to the distance data includes: In a case where the distance data indicates that the first vehicle is located on the left side of the lane centerline, determining the driving lane data close to the left side of the lane centerline in the second target driving lane data as the third target driving lane data; When the distance data indicates that the first vehicle is located on the right side of a lane centerline, driving lane data close to the right side of the lane centerline in the second target driving lane data is determined as the third target driving lane data.

8. A vehicle lane identification device, characterized in that: The device comprises: a data acquisition module, configured to acquire vehicle driving state data of a first vehicle, location data of the first vehicle, and map data within a preset range from the location of the first vehicle, wherein the first vehicle is a vehicle in a preset area around the second vehicle, and the map data includes data of a plurality of lanes; a distance data determination module, configured to determine, based on the position data and the lane data, distance data between the first vehicle and a lane centerline corresponding to the lane data; A first angle data determination module, configured to determine first angle data between the first vehicle and a lane centerline corresponding to the lane data according to the vehicle driving state data, the position data and the lane data; The first driving lane data determination module is used to determine the first driving lane data corresponding to the first vehicle according to the distance data and the first angle data.

9. A vehicle lane identification device, characterized in that: The vehicle lane determination device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the vehicle lane determination method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The storage medium stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the vehicle lane identification method as described in any one of claims 1 to 7.

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