Lane line data detection method and apparatus, and device and medium

Abstract

A lane line data detection method and apparatus, and a device and a medium. The method comprises: acquiring data of the current road section where an autonomous vehicle is located, and data to be searched for, wherein the data of the current road section is acquired on the basis of a high-precision map, and the data to be searched for comprises a lane to be searched for; on the basis of the data of the current road section and the data to be searched for, determining a lane line set, wherein the lane line set comprises coordinate points of a plurality of lane lines; on the basis of the coordinate points of the plurality of lane lines in the lane line set, determining a projection coordinate point and a target coordinate point of the autonomous vehicle on each lane line; and on the basis of the projection coordinate point, the target coordinate point and the lane line set, determining a lane line data list of the lane to be searched.

Description

Lane line data detection method, device, equipment and medium

[0001] The present application claims priority to the Chinese patent application No. 202411379682.1 filed on September 30, 2024 with the China Patent Office, the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0002] The present application relates to the technical field of autonomous driving, for example to a lane line data detection method, device, equipment and medium. BACKGROUND

[0003] With the development of autonomous driving technology, autonomous driving technology and advanced driving assistance systems are increasingly applied in vehicle driving process. In order to ensure the determination of the lane position of the vehicle during driving, lane line recognition and prediction on the driving path of the vehicle become particularly important.

[0004] Conventional lane line recognition technology relies on vehicle-mounted sensors such as cameras, radars, etc. These methods may have problems of inaccurate recognition or delay in complex environments. For example, in severe weather conditions, the camera may not be able to clearly capture the lane line; and in the case of night or insufficient light, the recognition of the lane line will also be affected. In addition, the recognition range of the vehicle-mounted sensor is limited, and it is difficult to predict the lane line situation of a long distance in front of the vehicle, which limits the path planning ability of the autonomous driving system in complex traffic environments. SUMMARY

[0005] The present application provides a lane line data detection method, device, equipment and medium, which can improve the accuracy and reliability of lane line data detection of autonomous driving vehicles in complex traffic environments and severe weather conditions, and provide strong support for decision-making, path planning and safe driving of autonomous driving vehicles.

[0006] According to an aspect of the present application, a lane line data detection method is provided, comprising:

[0007] obtaining current road section data of an autonomous driving vehicle and to-be-searched data; wherein the current road section data is obtained based on a high-definition map, and the to-be-searched data includes a to-be-searched lane;

[0008] determining a lane line set according to the current road section data and the to-be-searched data; wherein the lane line set includes a plurality of lane line coordinate points;

[0009] determining a projection coordinate point and a target coordinate point of the autonomous driving vehicle on each lane line based on the coordinate points of the plurality of lane lines in the lane line set;

[0010] Determine a lane line data list of a lane to be searched according to the projection coordinate point, the target coordinate point and the lane line set.

[0011] Optionally, the to-be-searched data further includes a search distance and a search direction.

[0012] Determine a lane line set according to the current road segment data and the to-be-searched data, including:

[0013] Starting from the current road segment, traverse each road segment and lane in the high-definition map based on the search distance to obtain a target path.

[0014] Determine each lane line coordinate point on the high-definition map according to the search direction and the target path, and take each lane line coordinate point as the lane line set.

[0015] Optionally, determine the projection coordinate point and the target coordinate point of the autonomous vehicle on each lane line based on the coordinate points of multiple lane lines in the lane line set, including:

[0016] Obtain a self-vehicle coordinate point of the autonomous vehicle.

[0017] Traverse the lane line set, and determine the target coordinate point of the autonomous vehicle and the projection coordinate point of the autonomous vehicle on each lane line based on each lane line coordinate point and the self-vehicle coordinate point.

[0018] Optionally, traverse the lane line set, and determine the target coordinate point of the autonomous vehicle and the projection coordinate point of the autonomous vehicle on each lane line based on each lane line coordinate point and the self-vehicle coordinate point, including:

[0019] For each lane line coordinate point, obtain a first lane line coordinate point and a second lane line coordinate point; wherein the first lane line coordinate point is a currently traversed lane line coordinate point, and the second lane line coordinate point is a coordinate point after the first lane line coordinate point.

[0020] Determine a projection parameter according to the first lane line coordinate point, the second lane line coordinate point and the self-vehicle coordinate point.

[0021] Determine a target coordinate point according to the projection parameter and a set data range.

[0022] Determine the projection coordinate point of the autonomous vehicle on each lane line according to the projection parameter, the first lane line coordinate point and the self-vehicle coordinate point.

[0023] Optionally, determine a projection parameter according to the first lane line coordinate point, the second lane line coordinate point and the self-vehicle coordinate point, including:

[0024] determining first vector data according to the first lane line coordinate point and the self-vehicle coordinate point;

[0025] determining second vector data according to the second lane line coordinate point and the self-vehicle coordinate point;

[0026] determining projection parameters according to the first vector data and the second vector data.

[0027] Optionally, the setting data range includes a first setting data range and a second setting data range;

[0028] determining target coordinate points according to the projection parameters and the setting data range, including:

[0029] if the projection parameters are in the first setting range, taking the first lane line coordinate point as the target coordinate point;

[0030] if the projection parameters are in the second setting range, taking a third lane line coordinate point as the target coordinate point; wherein the third lane line coordinate point is a previous coordinate point of the first lane line coordinate point.

[0031] Optionally, the lane line data list includes lane line type, lane line length and lane line start point distance data;

[0032] determining a lane line data list of a to-be-searched lane according to the projection coordinate point, the target coordinate point and the lane line set, including:

[0033] determining initial current traversal distance, initial lane line type and initial lane line start point distance based on the projection coordinate point and the target coordinate point;

[0034] starting traversal from a next lane line coordinate point of the target coordinate point, and updating the current traversal distance, the lane line type and the lane line start point distance data based on the traversed lane line coordinate point;

[0035] determining each lane line length data based on the current traversal distance;

[0036] recording and forming a lane line data list of the lane line type, the lane line length data and the lane line start point distance data obtained each time.

[0037] According to another aspect of the present application, a lane line data detection device is provided, including:

[0038] a data acquisition module configured to acquire current road section data of an autonomous vehicle and to-be-searched data; wherein the current road section data is acquired based on a high-definition map, and the to-be-searched data includes a to-be-searched lane;

[0039] a lane line set determination module configured to determine a lane line set according to the current road segment data and the to-be-searched data; wherein the lane line set includes a plurality of lane line coordinate points;

[0040] a coordinate point determination module configured to determine, based on the coordinate points of the plurality of lane lines in the lane line set, a projection coordinate point and a target coordinate point of the autonomous vehicle on each lane line;

[0041] a lane line data list determination module configured to determine, according to the projection coordinate point, the target coordinate point and the lane line set, a lane line data list of the to-be-searched lane.

[0042] According to another aspect of the present application, an electronic device is provided, which includes:

[0043] at least one processor; and

[0044] a memory in communication connection with the at least one processor; wherein

[0045] the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the lane line data detection method according to any one of the embodiments of the present application.

[0046] According to another aspect of the present application, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to implement the lane line data detection method according to any one of the embodiments of the present application when executed by the processor.

[0047] In the embodiments of the present application, the current road segment data and the to-be-searched data of the autonomous vehicle are obtained; wherein the current road segment data is obtained based on a high-definition map, and the to-be-searched data includes a to-be-searched lane; a lane line set is determined according to the current road segment data and the to-be-searched data; wherein the lane line set includes a plurality of lane line coordinate points; a projection coordinate point and a target coordinate point of the autonomous vehicle on each lane line are determined based on the coordinate points of the plurality of lane lines in the lane line set; and a lane line data list of the to-be-searched lane is determined according to the projection coordinate point, the target coordinate point and the lane line set. The embodiments of the present application can improve the lane line data detection accuracy and reliability of the autonomous vehicle in complex traffic environment and severe weather conditions, and provide strong support for the decision, path planning and safe driving of the autonomous vehicle. BRIEF DESCRIPTION OF DRAWINGS

[0048] FIG. 1 is a flowchart of a lane line data detection method according to an embodiment of the present application;

[0049] FIG. 2 is a flowchart of a lane line data detection method according to an embodiment of the present application;

[0050] FIG. 3 is a structural schematic diagram of a lane line data detection apparatus according to an embodiment of the present application;

[0051] FIG. 4 is a structural schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0052] In order to make the personnel in the technical field better understand the scheme of the present application, the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. The described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by the person of ordinary skill in the art without creative labor should be within the scope of protection of the present application.

[0053] The terms "first", "second", and "target" and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, 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 "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0054] Embodiment One

[0055] FIG. 1 is a flowchart of a lane line data detection method according to an embodiment of the present application. The present embodiment can be applicable to the case that an automatic driving vehicle detects lane line data of an invisible road section. The method can be executed by a lane line data detection apparatus which can be realized in the form of hardware and / or software, and can be configured in an electronic device having data processing capability. As shown in FIG. 1, the method comprises:

[0056] The method of the embodiment can run on a domain controller (Advanced Driver Assistance System Domain Controller, ADAS), based on the future lane line discrimination method of the ADAS domain controller and the high-definition map, the real-time positioning information of the vehicle and the high-definition map data can be used to predict the lane line type on the future path of the vehicle, and output the distance, length, and whether it can be crossed, to provide decision support for the autonomous driving system. The ADAS domain controller is a core component in a car for implementing an advanced driver assistance system (ADAS). It integrates various sensors, actuators, and control algorithms to handle key tasks such as perception, decision-making, execution, safety, and communication. Future lanes can be understood as using high-definition maps to predict lane line data on planned routes outside the recognized range of the current vehicle, and can also identify lanes within one kilometer of the set navigation route.

[0057] S110, acquiring current road section data and to-be-searched data of the autonomous driving vehicle.

[0058] The current road section data is obtained based on a high-definition map, and the to-be-searched data includes a to-be-searched lane. The ADAS domain controller in the embodiment includes a high-definition map and a fusion positioning module capable of outputting the current UTM coordinate information of the vehicle. The current road section data can include the high-definition map road section identifier of the current autonomous driving vehicle. The road section identifier in the embodiment can be represented by a road section ID. The high-definition map (HD map) is a high-precision map for autonomous driving, which represents road, lane, roadside traffic signs, and ground markings, etc. The map accuracy can reach centimeter level. The to-be-searched lane can be understood as the high-precision map lane identifier that needs to be searched. The lane identifier in the embodiment can be represented by a lane ID. The to-be-searched lane in the embodiment can be a lane that cannot be recognized by the current camera of the current autonomous driving vehicle, or a to-be-searched lane in the future road section that will be passed through in the navigation path. It can be understood that the high-precision map in the embodiment can include various road section IDs, and each road section includes multiple lane IDs. The to-be-searched data can include the to-be-searched lane, and can also include search distance and search direction data.

[0059] In the embodiment, the high-definition map road section ID of the autonomous driving vehicle and the to-be-searched lane ID can be obtained, and the to-be-searched distance and the to-be-searched direction data can also be included.

[0060] S120, determining a lane line set according to the current road section data and the to-be-searched data.

[0061] The lane line set includes a plurality of lane line coordinate points. The plurality of lane line coordinate points included in the lane line set can be road segment information given by the high-definition map, and each lane line coordinate point data determined based on the to-be-searched data. In this embodiment, each lane line coordinate point data determined based on the to-be-searched data can be obtained by traversing the road segment information given by the high-definition map from the current road segment of the ego vehicle.

[0062] In this embodiment, the to-be-searched data further includes a search distance and a search direction; and the lane line set is determined according to the current road segment data and the to-be-searched data, including: traversing each road segment and lane in the high-definition map based on the search distance to obtain a target path; and determining each lane line coordinate point on the high-definition map according to the search direction and the target path, and taking each lane line coordinate point as the lane line set.

[0063] The search distance can be a distance range preset based on the current autonomous vehicle starting to search. In this embodiment, the search distance can be a search distance range not exceeding two kilometers with the current autonomous vehicle as the starting point, or a distance range given by the high-definition map, which can be set according to actual needs, and this embodiment does not limit the search direction. The search direction can be understood as the direction of the left and right sides of the current autonomous vehicle. The target path can be a complete path obtained by splicing the lanes obtained by traversing each road segment and lane in the high-definition map based on the search distance.

[0064] Specifically, in this embodiment, the specific manner of traversing each road segment and lane in the high-definition map based on the search distance to obtain the target path can be starting from the road segment where the current vehicle is located, traversing the subsequent road segments and lanes according to the connection relationship of the road segments and lanes given by the high-definition map until the traversal stopping condition is met, and splicing the traversed lanes to obtain the target path. It can be understood that in this embodiment, each complete path in the high-definition map is divided into many path segments; the lane ID included under each road segment is different, so if a continuous complete lane is needed, the lanes in the traversed road segments need to be spliced according to the connection relationship of the high-definition map to obtain the target path. The traversal stopping condition can include reaching a specified search distance or encountering a road segment bifurcation. In this embodiment, the specific manner of determining each lane line coordinate point on the high-definition map according to the search direction and the target path can be selecting the lane line in the corresponding direction of the target path according to the search direction (left or right side of the current autonomous vehicle) to obtain each lane line coordinate point in the target path, and taking each lane line coordinate point as the lane line set. Further, in this embodiment, the lane line of the high-definition map is composed of coordinate points, so that a point list P = {p1, p2, …, pn} composed of n coordinate points is obtained.n}, i.e. a lane line set, wherein each point p i contains (x i , y i , t i ), x i is the horizontal coordinate of the point p i in the UTM coordinate system, y i is the vertical coordinate of the point p i in the UTM coordinate system, and t i is the lane line type of the point p i . The lane line type can include original lane line data provided by the high-definition map, and can include lane line type (such as dashed line, solid line, or fishbone line), triangular area, and the like.

[0065] In this embodiment, by such a setting, the high-definition map section and the lane can be traversed, and the coordinate points of each lane line are determined as a lane line set based on the data to be searched, so as to determine the data information of each lane line based on the lane line set.

[0066] S130, determining the projection coordinate point and the target coordinate point of the autonomous vehicle on each lane line based on the coordinate points of the plurality of lane lines in the lane line set.

[0067] The projection coordinate point can be understood as the projection coordinate point of the autonomous vehicle on the lane line. In this embodiment, the projection coordinate point of the autonomous vehicle on each lane line can be determined according to the coordinate points of each lane line in the lane line set and the coordinate point of the ego vehicle. Specifically, in this embodiment, the first lane line coordinate point in the lane line set can be started, and the vector calculation is performed on the first lane line coordinate point and the adjacent next lane line coordinate point and the coordinate point of the ego vehicle, respectively, so as to determine the projection coordinate point of the autonomous vehicle on each lane line based on the data obtained by the vector calculation.

[0068] The target coordinate point can be understood as the lane line coordinate point behind the autonomous vehicle and closest to the autonomous vehicle. In this embodiment, the first lane line coordinate point in the lane line set can be started, and the vector calculation is performed on the first lane line coordinate point and the adjacent next lane line coordinate point and the coordinate point of the ego vehicle, respectively, so as to determine the target coordinate point of the autonomous vehicle based on the data range in which the data obtained by the vector calculation is located.

[0069] S140, determining the lane line data list of the lane to be searched based on the projection coordinate point, the target coordinate point, and the lane line set.

[0070] The lane line data list can include lane line types, lane line start point distances from the ego vehicle, and lane line lengths of a plurality of lane line coordinate points. It can be understood that each lane line coordinate point in the embodiment can correspond to data information including a lane line type, a lane line start point distance from the ego vehicle, and a lane line length, and data information corresponding to a plurality of lane line coordinate points respectively constitutes a lane line data list.

[0071] In the embodiment, the lane line type, the lane line start point distance from the ego vehicle, and the lane line length can be determined according to the projection coordinate point and the target coordinate point, and then the lane line data is updated based on the lane line set to determine each lane line data list in the to-be-searched lane.

[0072] In the embodiment, the current road segment data and the to-be-searched data of the ego vehicle are obtained, wherein the current road segment data is obtained based on a high-definition map, and the to-be-searched data includes a to-be-searched lane. The lane line set is determined according to the current road segment data and the to-be-searched data, wherein the lane line set includes a plurality of lane line coordinate points. The projection coordinate point and the target coordinate point of the ego vehicle on each lane line are determined based on the coordinate points of the plurality of lane lines in the lane line set. The lane line data list of the to-be-searched lane is determined according to the projection coordinate point, the target coordinate point, and the lane line set. The embodiment can improve the lane line data detection accuracy and reliability of the ego vehicle in complex traffic environments and severe weather conditions, and provide strong support for the decision, path planning, and safe driving of the ego vehicle.

[0073] Embodiment Two

[0074] FIG. 2 is a flowchart of a lane line data detection method according to Embodiment Two of the present application, which is optimized based on the above-mentioned embodiments. Specifically, the projection coordinate point and the target coordinate point of the ego vehicle on each lane line are determined based on the coordinate points of the plurality of lane lines in the lane line set, including: obtaining the ego coordinate point of the ego vehicle; and traversing the lane line set to determine the target coordinate point of the ego vehicle and the projection coordinate point of the ego vehicle on each lane line based on each lane line coordinate point and the ego coordinate point. As shown in FIG. 2, the method includes:

[0075] S210, obtaining current road segment data and to-be-searched data of an ego vehicle.

[0076] The current road segment data is obtained based on a high-definition map, and the to-be-searched data includes a to-be-searched lane.

[0077] S220, determining a lane line set according to the current road segment data and the to-be-searched data.

[0078] The lane line set comprises a plurality of lane line coordinate points.

[0079] S230, obtaining a self-vehicle coordinate point of the automatic driving vehicle.

[0080] The self-vehicle coordinate point can be understood as a self-vehicle rear axle center point of the automatic driving vehicle at a current position. In this embodiment, the self-vehicle coordinate point of the automatic driving vehicle can be directly obtained according to fusion positioning in the ADAS domain controller.

[0081] S240, traversing the lane line set, and determining a target coordinate point of the automatic driving vehicle and a projection coordinate point on each lane line based on each lane line coordinate point and the self-vehicle coordinate point.

[0082] In this embodiment, each lane line coordinate point in the lane line set can be traversed, the projection parameter can be determined according to the currently traversed lane line coordinate point and the next adjacent lane line coordinate point in combination with the self-vehicle coordinate point, the target coordinate point that is the coordinate point farthest behind the self-vehicle and closest to the self-vehicle can be determined according to the data range in which the projection parameter is located, the projection coordinate point can be determined according to the projection parameter and the currently traversed lane line coordinate point, or the projection coordinate point can also be determined according to the projection parameter and the next adjacent lane line coordinate point of the currently traversed lane line coordinate point.

[0083] In this embodiment, optionally, traversing the lane line set, and determining a target coordinate point of the automatic driving vehicle and a projection coordinate point on each lane line based on each lane line coordinate point and the self-vehicle coordinate point, comprises: for each lane line coordinate point, obtaining a first lane line coordinate point and a second lane line coordinate point; determining a projection parameter according to the first lane line coordinate point, the second lane line coordinate point and the self-vehicle coordinate point; determining a target coordinate point according to the projection parameter and a set data range; and determining a projection coordinate point of the automatic driving vehicle on each lane line according to the projection parameter, the first lane line coordinate point and the self-vehicle coordinate point.

[0084] The first lane line coordinate point is the currently traversed lane line coordinate point, and the second lane line coordinate point is the next coordinate point of the first lane line coordinate point. It can be understood that, in this embodiment, for each lane line coordinate point, the currently traversed lane line coordinate point can be obtained as the first lane line coordinate point, the next lane line coordinate point of the currently traversed lane line coordinate point can be obtained as the second lane line coordinate point, and the same way is used to determine the first lane line coordinate point and the second lane line coordinate point for each lane line coordinate point. The projection parameter can be understood as a projection value obtained by performing an operation on the first lane line coordinate point, the second lane line coordinate point and the self-vehicle coordinate point.

[0085] The specific manner of determining the projection parameter according to the first lane line coordinate point, the second lane line coordinate point and the ego vehicle coordinate point in this embodiment can be that two vector data are obtained through vector calculation according to the first lane line coordinate point and the second lane line coordinate point and the ego vehicle coordinate point, and the projection parameter is obtained through formula calculation according to the two vector data. The specific manner of determining the target coordinate point according to the projection parameter and the set data range in this embodiment can be that different target coordinate points are respectively determined according to whether the projection parameter is in the set data range.

[0086] In this embodiment, the projection coordinate point corresponding to the first lane coordinate point currently traversed by the ego vehicle can be set as p proj (x proj ,y proj ), the first lane line coordinate point can be p i (x i ,y i ,t i ), the ego vehicle coordinate is (x veh ,y veh ), and τ is the projection parameter. The specific manner of determining the projection coordinate point p proj (x proj ,y proj ) of the autonomous vehicle on each lane line according to the projection parameter, the first lane line coordinate point and the ego vehicle coordinate point in this embodiment can be that x proj =x i +τ*(x veh -x i ); y proj =y i +τ*(y veh -y i ).

[0087] Further, the specific manner of determining the projection coordinate point p proj (x proj ,y proj ) of the autonomous vehicle on each lane line according to the projection parameter, the second lane line coordinate point p i+1 (x i+1 ,y i+1 ,t i+1 ) and the ego vehicle coordinate point in this embodiment can be that x proj =x i+1 +(1-τ)*(x veh -x i+1 ); y proj =y i+1 +(1-τ)*(y veh -y i+1 ).

[0088] It can be understood that the target coordinate point p j in the embodiment is the point farthest from the ego vehicle and closest to the ego vehicle. The projection coordinate point p proj (x proj ,y proj ) is a coordinate point between the points p j and p j+1 .

[0089] In the embodiment, by such a setting, the projection point of the autonomous vehicle on each lane line can be calculated according to each lane line coordinate point in the lane line set, so as to facilitate subsequent analysis and determination of the data information of each lane line.

[0090] In the embodiment, optionally, the projection parameter is determined according to the first lane line coordinate point, the second lane line coordinate point and the ego vehicle coordinate point, including: determining first vector data according to the first lane line coordinate point and the ego vehicle coordinate point; determining second vector data according to the second lane line coordinate point and the ego vehicle coordinate point; and determining the projection parameter according to the first vector data and the second vector data.

[0091] The first vector data can be data obtained by vector calculation of the currently traversed lane line coordinate point in the lane line set and the ego vehicle coordinate point. The second vector data can be understood as data obtained by vector calculation of the adjacent next lane line coordinate point of the currently traversed lane line coordinate point in the lane line set and the ego vehicle coordinate point.

[0092] Specifically, in the embodiment, the ego vehicle coordinate point output by the fusion positioning can be (x veh , y veh ), which can be denoted as C. Starting from the first lane line coordinate point p1 in the lane line set P, each lane line coordinate point p i (i), which can be denoted as A, is traversed. The current traversed lane line coordinate point p i (x i ,y i ,t i ) is the first lane line coordinate point; B can be the adjacent next point p i+1 (x i+1 ,y i+1 ,t i+1 ) of the currently traversed lane line coordinate point, which is the second lane line coordinate point.

[0093] In the embodiment, the first vector data AC calculated according to the first lane line coordinate point A and the ego vehicle coordinate point C can be (x veh -x i , y veh -y i ); and the second vector data BC calculated according to the second lane line coordinate point B and the ego vehicle coordinate point C can be (xveh x i+1 y veh -y i+1 );

[0094] The specific manner of determining the projection parameter τ according to the first vector data AC and the second vector data BC in this embodiment can be as follows:

[0095] In this embodiment, by such a setting, the projection parameter can be calculated according to the coordinate points of each lane line in the lane line set and the ego vehicle coordinate point, so as to determine the target coordinate point and the projection coordinate point based on the projection parameter, thereby improving the data reliability.

[0096] In this embodiment, optionally, the data range includes a first set data range and a second set data range; and the target coordinate point is determined according to the projection parameter and the set data range, including: if the projection parameter is in the first set range, the first lane line coordinate point is taken as the target coordinate point; and if the projection parameter is in the second set range, the third lane line coordinate point is taken as the target coordinate point.

[0097] The third lane line coordinate point is a coordinate point before the first lane line coordinate point. The first set data range can be a data range greater than or equal to 0 and less than or equal to 1; and the second data range can be a data range greater than 1.

[0098] In this embodiment, the target coordinate point can be a lane line coordinate point at the end of the traversal, and can be any lane line coordinate point in the lane line set. In this embodiment, the specific manner of determining the target coordinate point according to the projection parameter and the set data range can be as follows: if 0≤τ≤1, the first lane line coordinate point is taken as the target coordinate point, that is, the index i is recorded as j, at this time it is indicated that the ego vehicle coordinate point C is between the first lane line coordinate point A and the second lane line coordinate point B; if τ>1, the coordinate point before the first lane line coordinate point is taken as the target coordinate point, and the index i-1 is recorded as j, at this time it is indicated that the ego vehicle coordinate point C is after the first lane line coordinate point A and the second lane line coordinate point B. In this embodiment, the traversal can be stopped after the target coordinate point is determined, that is, the index j is determined. Further, if the projection parameter τ<0, the lane line set needs to be continuously traversed, at this time it is indicated that the ego vehicle coordinate point C is before the first lane line coordinate point A and the second lane line coordinate point B.

[0099] In this embodiment, by such a setting, different target coordinate points can be determined according to the data range in which the projection parameter value is located, so that the coordinate point behind the ego vehicle and closest to the ego vehicle can be determined, thereby providing the accuracy of the data.

[0100] S250, determining a lane line data list of the to-be-searched lane according to the projection coordinate point, the target coordinate point and the lane line set.

[0101] In this embodiment, the lane line data list includes lane line type, lane line length and lane line start point distance data. The lane line data list of the to-be-searched lane is determined according to the projection coordinate point, the target coordinate point and the lane line set, including: determining initial current traversal distance, initial lane line type and initial lane line start point distance based on the projection coordinate point and the target coordinate point; starting traversal from the next lane line coordinate point of the target coordinate point, updating current traversal distance, lane line type and lane line start point distance data based on the traversed lane line coordinate point; determining each lane line length data based on the current traversal distance; recording and forming the lane line data list for the lane line type, the lane line length data and the lane line start point distance data obtained each time.

[0102] The lane line type can include lane line type (such as dashed line, solid line or fishbone line), triangular area and the like. The lane line length can be understood as length data corresponding to each lane line. The lane line start point distance data can be understood as the distance data of the lane line start point distance from the vehicle.

[0103] In this embodiment, the specific manner of determining initial current traversal distance d cur , initial lane line type t and initial lane line start point distance d start based on the projection coordinate point p proj (x proj ,y proj ) and the target coordinate point p j (x j ,y j ,t j ) can be initializing current traversal distance d Initializing lane line type t = t j , and initializing current lane line start point distance d start = 0. It can be understood that the initialized lane line type is the lane line type corresponding to the current projection point, and then the initial current traversal distance d cur and the initial lane line start point distance d start are determined according to the projection point coordinate point and the target coordinate point.

[0104] This embodiment can start traversal from the next lane line coordinate point of the target coordinate point, and update current traversal distance, lane line type and lane line start point distance data based on the traversed lane line coordinate point. Specifically, it can start traversal from index pj+1 to point column P. Each time of traversal, the current traversal distance d In the embodiment, when the lane line type changes and is different from the initial lane line type or the lane line type recorded last time, lane line data is recorded once, that is, if the current traversal point p i i ≠t, the lane line type t, the distance d start from the lane line start point to the vehicle, and the lane line length d cur are recorded in the lane line data list. In the embodiment, the current traversal distance data is an accumulated value, which is updated each time and accumulates the traversal distance recorded last time. Therefore, the current traversal data is always increasing, while the distance from the lane line start point to the vehicle is real-time data recorded each time the lane line type changes.

[0105] In the embodiment, the specific way of determining the length of each lane line according to the current traversal distance can be to subtract the distance of the last traversal from the current traversal distance to obtain the length of the current lane line.

[0106] In the embodiment, each time the lane line changes, the lane line type, the lane line length data, and the lane line start point distance data obtained by each traversal are recorded. After recording, the lane line type t is updated to t i , the current lane line start point distance d start is updated to d cur (this can be understood as an assignment operation), and the next traversal is continued until all lane line coordinate points in the point set P of the lane line set are traversed. Then, a list containing the future lane line type, distance, and length can be output, that is, a lane line data list is formed. In the embodiment, each lane line coordinate point in the lane line set is traversed according to the projection coordinate point and the target coordinate point, and the lane line data list of the unknown lane is obtained. The list can be provided to the automatic driving decision module for reference, which can improve the lane line type detection accuracy and reliability of the automatic driving vehicle in complex traffic environments and adverse weather conditions, provide strong support for the decision, path planning, and safe driving of the vehicle, and improve the safety and reliability of the automatic driving vehicle.

[0107] ​The embodiment of the application obtains current road section data and to-be-searched data of an autonomous vehicle, wherein the current road section data is obtained based on a high-definition map, and the to-be-searched data includes a to-be-searched lane; a lane line set is determined according to the current road section data and the to-be-searched data, wherein the lane line set includes a plurality of lane line coordinate points; a self-vehicle coordinate point of the autonomous vehicle is obtained; the lane line set is traversed, and a target coordinate point of the autonomous vehicle and a projection coordinate point on each lane line are determined based on each lane line coordinate point and the self-vehicle coordinate point; and lane line data list of the to-be-searched lane is determined according to the projection coordinate point, the target coordinate point and the lane line set. The embodiment of the application can improve the lane line data detection accuracy and reliability of the autonomous vehicle in a complex traffic environment and under adverse weather conditions, and provide strong support for decision-making, path planning and safe driving of the autonomous vehicle.

[0108] Embodiment three

[0109] FIG. 3 is a structural schematic diagram of a lane line data detection device according to the embodiment three of the application. As shown in FIG. 3, the device includes:

[0110] The data acquisition module 310 is configured to obtain current road section data and to-be-searched data of an autonomous vehicle, wherein the current road section data is obtained based on a high-definition map, and the to-be-searched data includes a to-be-searched lane;

[0111] The lane line set determination module 320 is configured to determine a lane line set according to the current road section data and the to-be-searched data, wherein the lane line set includes a plurality of lane line coordinate points.

[0112] The coordinate point determination module 330 is configured to determine a projection coordinate point of the autonomous vehicle on each lane line and a target coordinate point based on the coordinate points of the plurality of lane lines in the lane line set.

[0113] The lane line data list determination module 340 is configured to determine a lane line data list of the to-be-searched lane according to the projection coordinate point, the target coordinate point and the lane line set.

[0114] Optionally, the to-be-searched data further includes a search distance and a search direction.

[0115] The lane line set determination module 320 is configured to traverse each road section and lane in the high-definition map based on the search distance to obtain a target path, starting from the current road section, and determine each lane line coordinate point on the high-definition map according to the search direction and the target path, and take each lane line coordinate point as the lane line set.

[0116] Optionally, the coordinate point determination module 330 includes:

[0117] The self-vehicle coordinate point acquisition unit is configured to acquire a self-vehicle coordinate point of the autonomous vehicle;

[0118] The traversal unit is configured to traverse the lane line set, and determine a target coordinate point of the autonomous vehicle and a projection coordinate point on each lane line based on each lane line coordinate point and the self-vehicle coordinate point.

[0119] Optionally, the traversal unit comprises:

[0120] The lane line coordinate point acquisition subunit is configured to, for each lane line coordinate point, acquire a first lane line coordinate point and a second lane line coordinate point; wherein the first lane line coordinate point is a currently traversed lane line coordinate point, and the second lane line coordinate point is a coordinate point after the first lane line coordinate point.

[0121] The projection parameter determination subunit is configured to determine the projection parameter according to the first lane line coordinate point, the second lane line coordinate point and the self-vehicle coordinate point.

[0122] The target coordinate point determination subunit is configured to determine the target coordinate point according to the projection parameter and the set data range.

[0123] The projection coordinate point determination subunit is configured to determine the projection coordinate point of the autonomous vehicle on each lane line according to the projection parameter, the first lane line coordinate point and the self-vehicle coordinate point.

[0124] Optionally, the projection parameter determination subunit is configured to determine first vector data according to the first lane line coordinate point and the self-vehicle coordinate point; determine second vector data according to the second lane line coordinate point and the self-vehicle coordinate point; and determine the projection parameter according to the first vector data and the second vector data.

[0125] Optionally, the set data range comprises a first set data range and a second set data range.

[0126] The target coordinate point determination subunit is configured to, if the projection parameter is in the first set range, take the first lane line coordinate point as the target coordinate point; and if the projection parameter is in the second set range, take a third lane line coordinate point as the target coordinate point; wherein the third lane line coordinate point is a coordinate point before the first lane line coordinate point.

[0127] Optionally, the lane line data list comprises lane line types, lane line lengths and lane line start point distance data.

[0128] The lane line data list determination module 340 is configured to determine an initial current traversal distance, an initial lane line type, and an initial lane line starting point distance based on the projection coordinate point and the target coordinate point; start traversal from a next lane line coordinate point of the target coordinate point, update the current traversal distance, the lane line type, and the lane line starting point distance data based on the traversed lane line coordinate point; determine lane line length data of each lane line based on the current traversal distance; and record and form lane line data list data of the lane line type, the lane line length data, and the lane line starting point distance data obtained each time.

[0129] The lane line data detection device provided in the embodiments of the present application can perform the lane line data detection method provided in any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of performing the method.

[0130] Embodiment Four

[0131] FIG. 4 is a structural schematic diagram of an electronic device according to the fourth embodiment of the present application. The electronic device 10 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (such as headsets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present application described and / or claimed in this document.

[0132] As shown in FIG. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11, wherein the memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0133] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0134] The processor 11 can be various general and / or special purpose processing components with processing and computing capabilities. The processor 11 includes a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the lane line data detection method.

[0135] In some embodiments, the lane line data detection method can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded onto the RAM 13 and executed by the processor 11, one or more steps of the lane line data detection method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the lane line data detection method by any other appropriate means, such as by means of firmware.

[0136] The various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (PLD), a computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0137] Computer programs used to practice the methods of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer program

[0138] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store computer programs for use by or in connection with an instruction execution system, apparatus, or device. Computer-readable storage media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. A machine-readable signal medium can include an electrical, optical, acoustical, or other form of propagated signals, e.g., carrier waves, infrared signals, digital signals, etc., in one or more wireline or wireless network channels, physically transferable computer program from one place to another place. Machine-readable signal media can also include any suitable combination of these.

[0139] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0140] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0141] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, and solves the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

Claims

1. A lane line data detection method, comprising: obtaining current road section data and to-be-searched data of an autonomous vehicle; wherein the current road section data is obtained based on a high-definition map, and the to-be-searched data comprises a to-be-searched lane; determining a lane line set according to the current road section data and the to-be-searched data; wherein the lane line set comprises a plurality of lane line coordinate points; determining a projection coordinate point and a target coordinate point of the autonomous vehicle on each lane line based on the coordinate points of the plurality of lane lines in the lane line set; determining a lane line data list of the to-be-searched lane according to the projection coordinate point, the target coordinate point, and the lane line set.

2. The method of claim 1, wherein, The to-be-searched data further comprises a search distance and a search direction; The determination of the lane line set according to the current road section data and the to-be-searched data comprises: starting from the current road section, traversing each road section and lane in the high-definition map based on the search distance to obtain a target path; determining each lane line coordinate point on the high-definition map according to the search direction and the target path, and taking each lane line coordinate point as the lane line set.

3. The method of claim 1, wherein, The determination of the projection coordinate point and the target coordinate point of the autonomous vehicle on each lane line based on the coordinate points of the plurality of lane lines in the lane line set comprises: obtaining a self-vehicle coordinate point of the autonomous vehicle; traversing the lane line set, and determining the target coordinate point and the projection coordinate point of the autonomous vehicle on each lane line based on each lane line coordinate point and the self-vehicle coordinate point.

4. The method of claim 3, wherein, The traversal of the lane line set and the determination of the target coordinate point and the projection coordinate point of the autonomous vehicle on each lane line based on each lane line coordinate point and the self-vehicle coordinate point comprise: for each lane line coordinate point, obtaining a first lane line coordinate point and a second lane line coordinate point; wherein the first lane line coordinate point is a currently traversed lane line coordinate point, and the second lane line coordinate point is a coordinate point after the first lane line coordinate point; determining a projection parameter according to the first lane line coordinate point, the second lane line coordinate point, and the self-vehicle coordinate point; determining a target coordinate point according to the projection parameter and a set data range; determining the projection coordinate point of the autonomous vehicle on each lane line according to the projection parameter, the first lane line coordinate point, and the self-vehicle coordinate point.

5. The method of claim 4, wherein, The determination of the projection parameter according to the first lane line coordinate point, the second lane line coordinate point, and the self-vehicle coordinate point comprises: determining first vector data according to the first lane line coordinate point and the self-vehicle coordinate point; determining second vector data according to the second lane line coordinate point and the self-vehicle coordinate point; determining the projection parameter according to the first vector data and the second vector data.

6. The method of claim 4, wherein, The set data range comprises a first set data range and a second set data range; The determination of the target coordinate point according to the projection parameter and the set data range comprises: if the projection parameter is in the first set range, taking the first lane line coordinate point as the target coordinate point. If the projection parameter is in the second setting range, a third lane line coordinate point is taken as a target coordinate point; wherein the third lane line coordinate point is a previous coordinate point of the first lane line coordinate point.

7. The method of claim 1, wherein, The lane line data list comprises lane line type, lane line length and lane line start point distance data; The lane line data list of the lane to be searched is determined according to the projection coordinate point, the target coordinate point and the lane line set, comprising: An initial current traversal distance, an initial lane line type and an initial lane line start point distance are determined based on the projection coordinate point and the target coordinate point; Starting from a next lane line coordinate point of the target coordinate point, the current traversal distance, the lane line type and the lane line start point distance data are updated based on the traversed lane line coordinate point; The lane line length data of each lane line is determined based on the current traversal distance; The lane line type, the lane line length data and the lane line start point distance data obtained by each traversal are recorded and formed into a lane line data list. 8.A lane line data detection apparatus, comprising: a data acquisition module configured to acquire current road section data and to-be-searched data of an autonomous vehicle; wherein the current road section data is acquired based on a high-definition map, and the to-be-searched data comprises a to-be-searched lane; a lane line set determination module configured to determine a lane line set according to the current road section data and the to-be-searched data; wherein the lane line set comprises a plurality of lane line coordinate points; a coordinate point determination module configured to determine a projection coordinate point and a target coordinate point of the autonomous vehicle on each lane line based on coordinate points of the plurality of lane lines in the lane line set; a lane line data list determination module configured to determine a lane line data list of the to-be-searched lane according to the projection coordinate point, the target coordinate point and the lane line set. 9.An electronic device, comprising: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the lane line data detection method in any one of claims 1-7. 10.A computer readable storage medium storing computer instructions for enabling a processor to execute the lane line data detection method in any one of claims 1-7 when executed by the processor.

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