Vehicle positioning method, device, equipment, storage medium and product

Abstract

The invention discloses a vehicle positioning method, device and equipment, a storage medium and a product, and relates to the technical field of vehicle positioning. The method comprises the steps of determining a local map which corresponds to an original pose of a vehicle and is determined based on a high-precision map, and respectively determining a transverse distance between the vehicle and each map lane line in the local map and a transverse distance between the vehicle and each detection lane line in a lane detection result based on the original pose, the lane line polarity of each map lane line and the lane line polarity of each detection lane line are determined according to the determined transverse distance, and the lane line polarity is used for representing the side direction of the lane lines in the vehicle and the lane line sequence in the side direction; and matching the map lane line corresponding to the lane line polarity with the detection lane line to obtain target correction information, and correcting the original pose based on the target correction information to obtain a target pose of the vehicle. By adopting the technical scheme, the reliability of a positioning result can be improved.

Description

Technical Field

[0001] The present invention relates to the field of vehicle positioning technology, and in particular to a vehicle positioning method, device, equipment, storage medium and product. Background Art

[0002] Accurate positioning is very important for vehicle navigation and autonomous driving, especially for the safe and efficient operation of autonomous vehicles (AVs). In highway scenarios, high-speed driving and quick decision-making are particularly important, and accurate lane-level positioning becomes even more critical.

[0003] At present, the common lane-level positioning solution is the lane matching positioning solution. By integrating high-definition maps (HD maps) and lane detection, vehicle positioning in lane scenarios can be enhanced.

[0004] However, traditional lane matching positioning techniques usually rely on simple point-to-point or line-to-line matching methods, which are susceptible to inaccurate lane detection results, such as the difference in the number of detected lanes, resulting in trajectory jumps and unreliable positioning results. Summary of the invention

[0005] The present invention provides a vehicle positioning method, device, equipment, storage medium and product, which can solve the problem of poor reliability of positioning results in a lane matching positioning solution.

[0006] According to one aspect of the present invention, a vehicle positioning method is provided, comprising:

[0007] Determine a local map corresponding to the original posture of the vehicle, wherein the local map is determined based on the high-precision map;

[0008] Determine, based on the original position, a first lateral distance between the vehicle and each lane line in the local map, and a second lateral distance between the vehicle and each lane line detected in the lane detection result;

[0009] Determining a lane line polarity of each of the map lane lines according to the first lateral distance, and determining a lane line polarity of each of the detected lane lines according to the second lateral distance, wherein the lane line polarity is used to characterize the lateral direction of the lane line of the vehicle and the order of the lane lines in the lateral direction;

[0010] Matching the map lane line and the detection lane line corresponding to the lane line polarity to obtain target correction information;

[0011] The original posture is corrected based on the target correction information to obtain the target posture of the vehicle.

[0012] According to another aspect of the present invention, there is provided a vehicle positioning device, comprising:

[0013] A local map determination module, used to determine a local map corresponding to an original posture of the vehicle, wherein the local map is determined based on a high-precision map;

[0014] a lateral distance determination module, configured to determine, based on the original position, a first lateral distance between the vehicle and each lane line in the local map, and a second lateral distance between the vehicle and each lane line detected in the lane detection result;

[0015] a lane line polarity determination module, configured to determine the lane line polarity of each of the map lane lines according to the first lateral distance, and to determine the lane line polarity of each of the detection lane lines according to the second lateral distance, wherein the lane line polarity is used to characterize the lateral direction of the lane line of the vehicle and the order of the lane lines in the lateral direction;

[0016] A lane line matching module is used to match the map lane line and the detection lane line corresponding to the lane line polarity to obtain target correction information;

[0017] The posture correction module is used to correct the original posture based on the target correction information to obtain the target posture of the vehicle.

[0018] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0019] at least one processor; and

[0020] a memory communicatively connected to the at least one processor; wherein,

[0021] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the vehicle positioning method described in any embodiment of the present invention.

[0022] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the vehicle positioning method described in any embodiment of the present invention when executed.

[0023] According to another aspect of the present invention, a computer program product is provided. The computer program product comprises a computer program. When the computer program is executed by a processor, the vehicle positioning method according to any embodiment of the present invention is implemented.

[0024] The technical solution of the embodiment of the present invention is to first determine the corresponding lane line polarity according to the distance to the vehicle for the map lane lines and the detected lane lines to be matched, match the map lane lines and the detected lane lines corresponding to the lane line polarities, and then correct the original posture according to the correction information obtained by matching. In this way, a more accurate and reliable vehicle posture can be obtained, which can avoid trajectory jumping and unreliable positioning caused by the difference between the number of detected lanes and the number of lanes in the map, thereby improving the reliability of the positioning result.

[0025] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 is a flow chart of a vehicle positioning method provided according to an embodiment of the present invention;

[0028] Figure 2 is a schematic diagram of lane line sorting provided according to an embodiment of the present invention;

[0029] Figure 3 is a lane line polarization schematic diagram provided according to an embodiment of the present invention;

[0030] Figure 4 is a schematic diagram of a lane matching process provided according to an embodiment of the present invention;

[0031] Figure 5 is a flow chart of another vehicle positioning method provided according to an embodiment of the present invention;

[0032] Figure 6 is a schematic diagram of a vehicle lateral displacement error provided according to an embodiment of the present invention;

[0033] Figure 7 is a schematic diagram of lane width error and jitter provided according to an embodiment of the present invention;

[0034] Figure 8 is a schematic diagram of a loss function provided according to an embodiment of the present invention;

[0035] Fig. 9is a flow chart of another vehicle positioning method provided according to an embodiment of the present invention;

[0036] Fig.10 is a schematic diagram of a vehicle positioning solution architecture provided according to an embodiment of the present invention;

[0037] Fig.11 is a schematic diagram of a local map generation process provided according to an embodiment of the present invention;

[0038] Fig.12 is a structural schematic diagram of a vehicle positioning device provided according to an embodiment of the present invention;

[0039] Fig.13 It is a structural schematic diagram of an electronic device for implementing the vehicle positioning method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0041] It should be noted that the terms "first", "second", "original" and "target" in the specification and claims of the present invention 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 invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0042] To facilitate understanding of the embodiments of the present invention, the following is an introduction to the relevant technologies. For low-cost autonomous driving vehicles using basic sensors such as cameras and Global Navigation Satellite System (GNSS), it is quite challenging to achieve accurate lane-level positioning, especially on highways. These vehicles typically lack advanced hardware, such as high-resolution lidar or high-precision GNSS systems, which are typically used to generate detailed point cloud maps and perform complex positioning. Therefore, many low-cost systems rely on camera-based solutions to detect and match lane lines to keep the vehicle correctly positioned within the highway lane.

[0043] Lane matching on highways involves taking lane lines detected by cameras or sensors and matching them with high-precision map lines to place the vehicle in the correct lane, ensuring safe navigation at highway speeds and maintaining proper lane discipline during lane changes.

[0044] In the related art, there are three lane-level positioning schemes, including particle filter technology, iterative closest point (ICP) algorithm and traditional lane matching technology. Among them, particle filter technology performs positioning by generating multiple possible positions of the vehicle and scoring the detected lane lines with HD map features. However, this method requires highly detailed maps, including features such as dotted lines and curbs, which are expensive to create and maintain. In addition, inaccuracies in lane detection, such as missing or distorted lines, may lead to positioning errors; ICP algorithm can align the point cloud of detected lane lines with the map, but it will encounter difficulties in dealing with inconsistencies, such as mismatches in the number of detected lanes or differences in lane widths, which will lead to inaccurate positioning; traditional lane matching technology directly uses point-to-point or line-to-line matching to align detected lane lines with map data. Although simple, it is prone to errors. When the number of detected lanes is different from the number of lanes in the map, or environmental factors (such as lane lines under construction or obscured) interfere with accurate detection, it will lead to unstable positioning results.

[0045] Figure 1This is a flow chart of a vehicle positioning method provided according to an embodiment of the present invention. This embodiment is applicable to the case of lane-level positioning of a vehicle (especially an autonomous driving vehicle) when it is traveling on a road (especially a highway). The method can be executed by a vehicle positioning device, which can be implemented in the form of hardware and / or software, and the vehicle positioning device can be configured in an electronic device. The electronic device can be a vehicle, specifically a vehicle with autonomous driving capabilities, or a device capable of communicating with a vehicle. The vehicle in the embodiment of the present disclosure can be a low-cost autonomous driving system equipped with image acquisition devices including cameras and economical GNSS equipment, and does not need to be equipped with high-resolution lidar or high-precision GNSS equipment. Figure 1 As shown, the method includes:

[0046] Step 101: Determine a local map corresponding to an original posture of the vehicle, wherein the local map is determined based on a high-precision map.

[0047] Exemplarily, the original position of the vehicle can be determined based on GNSS data or fused output data. The fused output data can be, for example, data generated based on detection data of multiple sensors, which is not specifically limited. The position of the vehicle in the high-precision map can be determined based on the original position, and then the local map can be determined based on the map data within a certain range of the position. The high-precision map can be, for example, a segmented map, that is, the high-precision map includes multiple map segments. Taking the highway as an example, each map segment represents a specific part of the highway. Each lane in each map segment includes detailed information on its right boundary, left boundary and reference line (such as the center line). Exemplarily, the map segment in which the vehicle is located can be determined based on the original position, and then the local map can be determined. Using the entire high-precision map as the target of lane matching, the amount of calculation will be very large due to the huge scale of the point cloud. By using a local map, it is allowed to focus only on the map area related to the current position of the vehicle, such as focusing on the map portion closer to the vehicle position, rather than the entire high-precision map, which can speed up the matching process without affecting the accuracy and reduce the consumption of computing resources.

[0048] Step 102: Determine, based on the original position, a first lateral distance between the vehicle and each lane line in the local map, and a second lateral distance between the vehicle and each detected lane line in the lane detection result.

[0049] In an embodiment of the present invention, the lane lines included in the local map are recorded as map lane lines, and the lane lines included in the lane detection results are recorded as detected lane lines. The lane detection results can be obtained by detection by sensors such as cameras. Most lane line detection data from vehicle sensors can be represented in two ways: sampling points or curve parameters. Sampling points are points along the detected lane lines that can be used directly, while curve parameters (such as coefficients C0, C1, C2, and C3) describe the curvature of the lane in a continuous format. The curvature equation used is as follows:

[0050] y(x)=C 0 +C 1 x+C 2 x 2 +C 3 x 3

[0051] After obtaining the curve parameters, points along the curve can be generated. Both representations can be converted into point clouds, because the matching algorithm can be run on point cloud data to achieve higher efficiency. In an embodiment of the present invention, the lane detection result can include a detected lane line in a point cloud format.

[0052] Exemplarily, each map lane line in the local map can be converted to the reference system of the vehicle, so as to more accurately determine the lateral distance between the vehicle and the map lane line. Exemplarily, in the reference system of the vehicle, the longitudinal direction is upward as the longitudinal positive direction, and the lateral direction is leftward as the lateral positive direction. Calculate the difference between the lateral coordinate value of the map lane line and the lateral coordinate value in the original posture, and determine the first lateral distance based on the difference; calculate the difference between the lateral coordinate value of the detected lane line and the lateral coordinate value in the original posture, and determine the second lateral distance based on the difference.

[0053] Step 103: determine the lane line polarity of each map lane line based on the first lateral distance, and determine the lane line polarity of each detected lane line based on the second lateral distance, wherein the lane line polarity is used to characterize the lane line's location in the lateral direction of the vehicle and the order of the lane lines in the lateral direction.

[0054] In the related art, when trying to match all lane lines, if there is a difference between the number of detected lanes and the number of lanes in the map, it is difficult to accurately determine the lane lines that need to be matched, which in turn causes trajectory jumps and unreliable positioning.

[0055] In an embodiment of the present invention, the polarities of the map lane lines and the detection lane lines are first determined respectively, so as to subsequently accurately determine the map lane lines and the detection lane lines that need to be matched. The lateral direction includes the left side and the right side. For example, the polarity of the left side is positive and the polarity of the right side is negative. It can be determined according to the positive or negative of the lateral distance (the first lateral distance or the second lateral distance). The lane line order can specifically be the sorting position in the lane line sequence obtained by sorting the lane lines in the corresponding lateral direction according to the size of the lateral distance.

[0056] In some embodiments, the lane line polarity of each lane line is determined according to the lateral distance, including: grouping each lane line according to the positive or negative lateral distance to obtain a first lane line group and a second lane line group, wherein the lateral distance is the first lateral distance or the second lateral distance, and the lane line is the map lane line or the detection lane line; sorting the lane lines in the first lane line group from near to far from the vehicle according to the size of the lateral distance to obtain a first lane line sequence; sorting the lane lines in the second lane line group from near to far from the vehicle according to the size of the lateral distance to obtain a second lane line sequence; for each lane line, determining the lane line in the lateral direction of the vehicle according to the positive or negative lateral distance, determining the lane line order of the lane line in the lateral direction according to the sorting position of the lane line in the corresponding lane line sequence, and determining the lane line polarity of the lane line according to the lateral direction and the lane line order. In this way, the lane line polarity can be accurately determined, thereby improving the accuracy of subsequent lane line matching.

[0057] Optionally, taking the map lane line as an example, the lane line polarity of each map lane line is determined according to the first lateral distance, including: grouping the map lane lines according to the positive and negative of the first lateral distance to obtain a first map lane line group and a second map lane line group; sorting the map lane lines in the first map lane line group from near to far from the vehicle according to the size of the first lateral distance to obtain a first map lane line sequence; sorting the map lane lines in the second map lane line group from near to far from the vehicle according to the size of the first lateral distance to obtain a second map lane line sequence; for each map lane line, determining whether the map lane line is located in the lateral direction of the vehicle according to the positive and negative of the first lateral distance, determining the lane line order of the map lane line in the lateral direction according to the sorting position of the map lane line in the corresponding map lane line sequence, and determining the lane line polarity of the map lane line according to the lateral direction and the lane line order.

[0058] Optionally, taking the detection lane line as an example, the lane line polarity of each detection lane line is determined according to the first lateral distance, including: grouping the detection lane lines according to the positive or negative value of the first lateral distance to obtain a first detection lane line group and a second detection lane line group; sorting the detection lane lines in the first detection lane line group from near to far from the vehicle according to the size of the first lateral distance to obtain a first detection lane line sequence; sorting the detection lane lines in the second detection lane line group from near to far from the vehicle according to the size of the first lateral distance to obtain a second detection lane line sequence; for each detection lane line, determining whether the detection lane line is located in the lateral direction of the vehicle according to the positive or negative value of the first lateral distance, determining the lane line order of the detection lane line in the lateral direction according to the sorting position of the detection lane line in the corresponding detection lane line sequence, and determining the lane line polarity of the detection lane line according to the lateral direction and the lane line order.

[0059] Figure 2 is a schematic diagram of lane line sorting provided according to an embodiment of the present invention, such as Figure 2 As shown, taking the map lane lines as an example, for each lane line, the first point located in front of the vehicle (X coordinate is positive), and then the lateral distance (Y coordinate) of the found points is compared to determine the order. The lane lines on the right (Y coordinate is negative) are sorted in descending order, which means that the lane line closest to the vehicle has the highest priority. For the lane lines on the left (Y coordinate is positive), the lines are sorted in ascending order, giving priority to the lane lines closest to the vehicle. Assuming that there are 4 map lane lines with line names A, B, -C and -D, the map lane lines are grouped according to the positive and negative first lateral distances to obtain the first map lane line group (-C and -D) and the second map lane line group (A and B). For the first map lane line group, -C is closer to the vehicle than -D, so the first map lane line sequence is -C, -D; for the second map lane line group, B is closer to the vehicle than A, so the second map lane line sequence is B, A.

[0060] In some special cases (such as lane changes), the polarity of the detected lane lines may not match the polarity of the map lane lines. The polarity can be flipped and the left and right lanes can be swapped to maintain accuracy.

[0061] After sorting, polarization processing is performed to obtain the lane line polarity. Figure 3 is a lane line polarization schematic diagram provided according to an embodiment of the present invention, such as Figure 3As shown, the lane order of the map lane lines in the lateral direction is determined according to the sorting position of the map lane lines in the corresponding map lane line sequence. For example, in order to assign polarity, the negative lateral distance (right side) can be processed first, and the closest lane is assigned a polarity of -1, the next is -2, and so on. The same process is applied to the left side, using positive lateral distance. This consistent scoring method can ensure that the detected lane lines and map lane lines can be correctly aligned during the lane matching process. If there are more lane lines in the local map or lane detection results, only lanes with corresponding polarities can be matched to ensure accuracy even if the number of lane lines is different. The polarity of the lane line with positive polarity is a positive number, and the polarity of the lane line with negative polarity is a positive number. For example, after -C polarization, the lane line polarity (map line polarity) is -1; after -D polarization, the lane line polarity is -2; after B polarization, the lane line polarity is 1; after A polarization, the lane line polarity is 2. Correspondingly, the detected lane line is also polarized to obtain the lane line polarity of the detected lane line (detected line polarity).

[0062] Step 104: Match the map lane line and the detection lane line corresponding to the lane line polarity to obtain target correction information.

[0063] For example, the lane line polarities corresponding to each other may specifically be the same. Figure 3 As shown, the lane line polarity of the leftmost map lane line and the detection lane line are both 2. Therefore, the map lane line and the detection lane line with lane line polarity of 2 are matched. Similarly, the map lane line and the detection lane line with lane line polarity of 1 are matched, the map lane line and the detection lane line with lane line polarity of -1 are matched, and the map lane line and the detection lane line with lane line polarity of -2 are matched.

[0064] The specific matching process is not limited. For example, the matching can be performed based on the ICP algorithm or its variants (such as generalized IPC) and other algorithms. The matching process can be completed in the reference system of the vehicle, rather than in the global (world) coordinate system. This helps to avoid numerical precision problems that may occur when processing large numbers in global coordinates and ensure the accuracy and stability of the transformation. The specific matching order is also not limited. It can be parallel matching, or iterative matching, etc. For example, it can be from left to right, or from right to left, or from far to near according to the distance from the vehicle, etc., and the target correction information is determined according to each matching result. The correction information can be a transformation matrix.

[0065] Step 105: Correct the original posture based on the target correction information to obtain the target posture of the vehicle.

[0066] Exemplarily, correction based on target correction information is performed on the basis of the original posture, such as multiplying by the target transformation matrix, to obtain a posture correction result, that is, the target posture of the vehicle. The target posture represents the updated position and direction of the vehicle after aligning the detected lane line with the local map. Subsequently, the vehicle can be navigated and autonomous driving controlled based on the target posture.

[0067] The vehicle positioning method of an embodiment of the present invention, for the map lane lines and detection lane lines to be matched, first determines the corresponding lane line polarity according to the distance from the vehicle, matches the map lane lines and detection lane lines corresponding to the lane line polarities, and then corrects the original posture according to the correction information obtained by matching. A more accurate and reliable vehicle posture can be obtained, which can avoid trajectory jumping and unreliable positioning caused by the difference between the number of detected lanes and the number of lanes in the map, thereby improving the reliability of the positioning result.

[0068] In some embodiments, matching the map lane line corresponding to the lane line polarity with the detection lane line to obtain target correction information includes: matching the map lane line corresponding to the lane line polarity with the detection lane line within a preset distance range to obtain target correction information. Thus, matching efficiency can be improved.

[0069] For example, the left boundary of the preset distance range may be a first distance threshold on the left side of the vehicle, and the right boundary of the preset distance range may be a second distance threshold on the right side of the vehicle. The first distance threshold and the second distance threshold may be equal or different, and specific values ​​may be set according to actual needs, such as the width of 2 to 3 lanes, for example, 6 meters. For lane lines beyond the preset distance range, no matching may be performed, thereby improving matching efficiency.

[0070] Optionally, for the map lanes and detection lanes corresponding to the lane polarity within the preset distance range, iterative matching is performed in order from far to near from the vehicle to obtain target correction information. Thus, starting from the farthest matched lane line, gradually moving to the nearest one, is different from traditional methods such as normal distribution transform (NDT), which try Figure 1 The present invention matches all detected lane lines at once, while matching lane by lane in sequence, avoiding problems that may occur in distribution-based methods, such as during lane divergence or merging, or when the number of detected lanes does not match the number in the map, and can improve the accuracy of the correction information.

[0071] In some embodiments, the map lane lines and detection lane lines corresponding to the lane line polarity within the preset distance range are iteratively matched in order from far to near from the vehicle to obtain target correction information, including: for the map lane lines and detection lane lines corresponding to the lane line polarity within the preset distance range, in order from far to near from the vehicle, determining the current lane line pair to be matched, wherein the current lane line pair includes a map lane line and a detection lane line; if the current lane line pair is not the first lane line pair, using the reference correction information of the current lane line pair to match the detection lane line in the current lane line pair The lane line is transformed and the transformed detected lane line is determined as the target detected lane line, wherein the reference correction information is the correction information obtained from the last matching; if the current lane line pair is the first lane line pair, the detected lane line in the current lane line pair is determined as the target detected lane line; the target detected lane line in the current lane line pair is matched with the map lane line to obtain the correction information obtained from this matching; if the current lane line pair is not the last lane line pair, the current lane line pair to be matched is re-determined; if the current lane line pair is the last lane line pair, the correction information obtained from this matching is determined as the target correction information.

[0072] Exemplarily, every two map lane lines and detection lane lines with the same polarity within a preset distance range become a lane line pair, and the lane line pair closest to the vehicle is preferentially determined as the current lane line pair. For the first lane line pair, the reference correction information can be empty and matching can be performed directly. For the second lane line pair to the last lane line pair, the detection lane lines are first transformed according to the matching results of the previous lane line pair, and then matched. If there are no other unmatched lane line pairs after the current lane line pair is matched, the correction information obtained after the current lane line pair is matched is determined as the target correction information ultimately used to correct the original posture of the vehicle connection. If there are still unmatched lane line pairs, a new current lane line pair is selected from the unmatched lane line pairs in order from far to near from the vehicle, and subsequent iterative matching is performed.

[0073] Therefore, distance sorting is used because lane detections close to the vehicle are usually more reliable than those far away. Detections far away may be noisier and noisier. However, starting from the farthest lane line allows each subsequent match to use the previous result as an initial guess. Since the initial guess is good, the next match will be performed faster and more accurately, effectively reducing the amount of calculation. Polarity constraints ensure that detected lane lines with a certain polarity are only matched with lane lines of corresponding polarity in the local map, thereby improving robustness. Even if the number of detected lane lines is different from that in the map, each lane can be correctly aligned, thereby avoiding the situation where the positioning results of traditional vehicles jump back and forth between consecutive lanes, because the traditional algorithm does not know which specific lines to match with the lines in the map. The technical solution of the present invention avoids the situation where the vehicle jumps back and forth between consecutive lanes due to the algorithm not knowing which specific lines to match with the lines in the map. Using this cascade method, the number of iterations required for each stage is significantly reduced, because each new match is based on the previous match until the lane line closest to the vehicle is matched, and the transformation of the last match is used as the final correction of the vehicle's posture, because lane detection close to the vehicle is usually more reliable, which can ensure that the vehicle's positioning is as accurate as possible, taking into account lane detections far and near, while being consistent with the lane structure of the map, effectively improving the accuracy of the correction information.

[0074] Figure 4 is a schematic diagram of a lane matching process provided according to an embodiment of the present invention. Figure 4 As shown, based on the detected line N and the map line N and the initial guess, the matching algorithm is used to obtain the change, that is, the correction information. Specifically, the lane line pairs that need to be matched are determined according to the preset distance range (such as the range circled in dark blue in the figure). Since the rightmost lane line is too far away from the vehicle and exceeds the preset distance range, it is ignored and not matched. For polarity 2, there are only detected lane lines, no map lane lines, and no matching is performed. For the lane line pairs to be matched (lane line polarity is -1, 1 and -2), start matching from the farthest lane line pair (polarity -2), that is, stage 1, and get the corresponding transformation (which can be a transformation matrix), which is recorded as transformation 1 in the figure, that is, correction information. This transformation is used for matching in stage 2, that is, for the matching of lane line pairs with lane line polarity of 1, transform 1 is used to transform the detection lane line with lane line polarity of 1. After the transformation, the detection lane line with polarity of 1 is matched with the map lane line with polarity of 1 to get transformation 2. Transformation 2 is continued to be used for matching in stage 3 (that is, the nearest lane) to get the final transformation, that is, target correction information. The result of the last stage is used to correct the original position of the vehicle.

[0075] In some embodiments, after the original posture is corrected based on the target correction information to obtain the target posture of the vehicle, it also includes: calculating covariance based on the target posture, wherein the covariance is used to measure the confidence of the posture correction. Covariance can be used to evaluate the reliability of the target posture, that is, a measure of the confidence of the posture correction, and can be used by relevant modules of the vehicle (such as a fusion module or a navigation module, etc.). Combining covariance with target posture provides a more accurate and robust lane matching result, more accurately controls the autonomous driving vehicle, and ensures the safety of autonomous driving.

[0076] Optionally, the covariance includes a fitting metric and a loss based on a target error, wherein the target error includes at least one of a lateral displacement error, a lane width error, and a single line jitter error. Thus, the covariance is calculated by combining multiple factors for evaluating the accuracy and reliability of the matching process, thereby improving the accuracy of the covariance.

[0077] Optionally, the loss based on the target error may be a Huber loss based on the target error. The covariance may include a fit metric, a Huber loss based on a lateral displacement error, and a Huber loss based on a lane width error or a single line jitter error.

[0078] Figure 5 FIG. 1 is a flow chart of another vehicle positioning method provided according to an embodiment of the present invention. This embodiment is optimized based on the above-mentioned optional embodiments. Figure 5 As shown, the method includes:

[0079] Step 501: Determine a local map corresponding to the original position of the vehicle, wherein the local map is determined based on a high-precision map.

[0080] Step 502: Determine, based on the original position, a first lateral distance between the vehicle and each lane line in the local map, and a second lateral distance between the vehicle and each detected lane line in the lane detection result.

[0081] Step 503: determine the lane line polarity of each map lane line according to the first lateral distance, and determine the lane line polarity of each detection lane line according to the second lateral distance.

[0082] Step 504: for the map lane lines and the detection lane lines corresponding to the lane line polarities within the preset distance range, iterative matching is performed in order from far to near distance from the vehicle to obtain target correction information.

[0083] Step 505: Calculate the covariance based on the target pose, wherein the covariance is used to measure the confidence of the pose correction, the covariance includes a fitting metric and a loss based on a target error, and the target error includes at least one of a lateral displacement error, a lane width error, and a single-line jitter error.

[0084] For the fit metric, the fit metric can be used to determine the degree to which the lane line detected by the vehicle sensor matches the lane line in the map during the matching process, that is, to determine the degree to which the detected lane line matches the map lane line during the matching process. Specifically, the alignment degree of the detected lane line with the map data can be measured by calculating the distance between corresponding points.

[0085] To calculate the fit metric, points in the local map (points in the local map point cloud) are compared to points of detected lane lines (detected lane lines) (detection points), and the distance between each map point and its matching detection point is calculated. The smaller these distances are, the better the alignment of the detected lane lines with the map. The difference between this loss and the loss based on lane width error is that the fit metric can characterize how well the overall lane detection results match the HD map.

[0086] Exemplarily, the fit metric can be determined based on the average squared distance between the local map points and the matching points of the detected lane lines, that is, taking the distance between all matching points, squaring them (which can eliminate negative values ​​and emphasize large differences), and then calculating the average of these squared distances. The fit metric can be determined by the following expression:

[0087]

[0088] Where N represents the number of matching points. represents a point in the local map point cloud, Represents the corresponding points in the detected lane lines.

[0089] A lower fit metric indicates a more accurate match between the map and the detected lane markings, meaning that the vehicle's pose is well aligned with the map. Conversely, a higher fit metric indicates a significant misalignment, possibly indicating errors in the detection or matching process, reducing the confidence in the corrected pose.

[0090] For the lateral displacement error, the lateral displacement error can be specifically an average lateral displacement error. The lateral displacement difference is used to characterize the change of the lateral position of the vehicle over time, in order to determine whether the corrected position of the vehicle is continuously stable. This consistency is critical to evaluating the reliability of the corrected matching pose, because frequent lateral displacement or instability may indicate inaccurate positioning.

[0091] Exemplarily, a queue of the most recent M lateral displacements can be maintained to track the corrected lateral position of the vehicle. For each new lateral displacement, the difference (which can be an absolute value) between the current lateral position and the previously recorded position can be calculated, and the average lateral displacement difference can be calculated to indicate the degree of lateral movement of the vehicle. The lateral displacement error can be determined by the following expression:

[0092]

[0093] Figure 6 is a schematic diagram of a vehicle lateral displacement error provided according to an embodiment of the present invention, such as Figure 6 As shown, the dotted line behind the vehicle represents the vehicle trajectory, the red dot represents the posture after lane matching correction, that is, the target posture, and T represents the time point. For example, the difference between the lateral coordinate of the target posture of the vehicle at T=-3 and the lateral coordinate of the target posture of the vehicle at T=-4 is calculated (recorded as the first difference, D3+D4), and so on, the second difference (D2+D3) between the lateral coordinate at T=-2 and the lateral coordinate at T=-3 is calculated, the third difference (D1-D2) between the lateral coordinate at T=-1 and the lateral coordinate at T=-2 is calculated, and the fourth difference (D1) between the lateral coordinate at T=0 and the lateral coordinate at T=-1 is calculated. Finally, the average of the first difference, the second difference, the third difference and the fourth difference is calculated.

[0094] If the lateral displacement is relatively consistent, for example, approximately 1 meter for multiple consecutive readings, the difference in lateral displacement will be low, indicating stability in the vehicle's position. Therefore, holding other losses constant in the covariance, the covariance will also be low, reflecting higher confidence in the vehicle's position. However, if the vehicle's position changes significantly, with more lateral movement, the average lateral displacement error will be higher, and this higher value indicates that the vehicle is not maintaining a stable position, which may mean that there are problems with the accuracy of the positioning system. Therefore, holding other losses constant in the covariance, an increase in the covariance value indicates lower confidence in the current pose estimate.

[0095] For lane width error, lane width error is used to characterize the difference between the lane width specified from the local map and the lane width calculated based on the detected lane lines. This error is critical to determining the accuracy of matching the lane lines detected by the vehicle with the actual lane structure in the map, with particular attention paid to the left and right boundaries of the lane in which the vehicle is located (called polarity 1 and -1). To calculate the lane width error, the distances of the detected left and right lane lines relative to the vehicle can be first obtained using the stored data. These distances represent how close each lane line is to the current position of the vehicle. Similarly, the distances of the corresponding left and right lane boundaries are obtained from the local map data.

[0096] The detected lane width can be calculated by adding the absolute distances of the detected lane lines:

[0097] Detected lane width = |left lane line distance| + |right lane line distance|

[0098] Similarly, the map lane width is calculated as:

[0099] Map lane width = |left map lane line distance|+|right map lane line distance|

[0100] The lane width error is then determined by finding the difference between the detected lane width and the mapped lane width:

[0101] Lane width error = |detected lane width - mapped lane width|

[0102] Figure 7 is a schematic diagram of lane width error and line jitter provided according to an embodiment of the present invention, such as Figure 7 As shown in the upper middle section, the lane width error is the detected lane width minus the mapped lane width. The lane width error highlights how much the detected lane lines deviate from the lane structure defined in the map. This error can be calculated if both left and right lane polarities are available. However, if one of the lane lines is missing, meaning that the detection only captured one side, the lane width error calculation can be skipped to avoid inaccuracies. By monitoring the lane width error, the accuracy of the lane detection relative to the known map can be assessed, and this value contributes to the calculation of the final covariance. A smaller lane width error means a better match to the map, while a larger error indicates a possible mismatch or detection error.

[0103] For single-line jitter error, the single-lane jitter mechanism handles the situation where the vehicle sensor detects only one lane line instead of the usual two or more. This situation may occur due to various anomalies, such as lane obstacles, poor lighting conditions, or sensor limitations. When the left and right lane lines are detected, the lane width error is calculated by measuring the distance of these lane lines from the vehicle to evaluate the accuracy of the vehicle's positioning within the lane. However, when only one lane line is detected, the lane width error cannot be calculated in the aforementioned manner. In order to maintain consistency and reliability in evaluating the accuracy of vehicle positioning, an embodiment of the present invention uses jitter displacement calculation instead of lane width error calculation.

[0104] Optionally, the single-line jitter error is determined in the following manner: in the case where a single lane line is detected on one side of the vehicle this time, if double lane lines were detected on both sides of the vehicle last time, then a first lateral distance between the single lane line and the vehicle is determined, and a second lateral distance between the cached single lane line and the vehicle is determined, and the single-line jitter error is determined based on the difference between the first distance and the second distance.

[0105] If single lane jitter is to be calculated, the lane line input data needs to first have complete left and right lane lines. If the lane detection only gives a single lane from the beginning, the single lane matching mechanism can be not triggered. When the left (polarity 1) and right (polarity -1) lane lines are detected, the lane width error is calculated by calculating the distance of these lane lines from the vehicle. These distance values ​​can be stored for future reference, representing the previously established lane width. This saved data provides a benchmark for subsequent detection comparisons. In the case where only a single lane line is detected, the jitter displacement is calculated using the saved distance of the lane width distance of the previous lane width error. This involves comparing the position of the currently detected single lane line with the stored distance value. The jitter displacement is determined by finding the difference between the current lane line distance and the distance previously saved when two lanes were detected:

[0106] Jitter displacement = |current lane distance - saved lane distance|

[0107] like Figure 7 As shown in the lower part, the single lane line detected this time is located on the right side of the vehicle. The current lane distance is the distance between the vehicle and the detected lane line (the first distance). The saved lane distance is the distance from the most recently stored vehicle to the nearest lane line on the right (the second distance). The absolute value of the difference between the first distance and the second distance is the single-line jitter error (the line jitter shown in the figure).

[0108] If the jitter displacement remains small, it means that the detected single lane line has not moved significantly relative to its previous position, indicating that the vehicle's positioning is still reliable. Conversely, a larger jitter displacement indicates that the detected lane line has moved significantly, which may indicate potential inaccuracies in positioning.

[0109] By using jitter displacement instead when the lane width error cannot be calculated, the system can still match and evaluate the covariance of the vehicle's position within the lane. If single lane matching is not used, the corrected matching pose will not be calculated, which may further affect the overall positioning position.

[0110] Optionally, the loss based on the target error is determined in the following manner: when the target error is less than a first preset threshold, the loss based on the target error is zero; when the target error is greater than or equal to the first preset threshold and less than a second preset threshold, the loss based on the target error is expressed as a linear function, specifically the difference between the target error and a first preset value, wherein the first preset value is determined based on the product of the first preset threshold and a first preset coefficient, and the first preset coefficient is less than 1; when the target error is greater than or equal to the second preset threshold, the loss based on the target error is expressed as a quadratic function, specifically the quotient of the square of the target error and a second preset value, wherein the second preset value is determined based on the product of the second preset threshold and a second preset coefficient, and the second preset coefficient is greater than 1. Thus, a smoother value can be obtained at the transition point, the system becomes more resilient to outliers and noise data, tolerates small fluctuations that have little effect on positioning accuracy, and severely punishes larger errors that may undermine reliability, thereby ensuring that positioning performance remains stable and reliable under various driving conditions, especially in real-world environments where sensor noise and detection changes are common.

[0111] Exemplarily, after obtaining the target error, the target error is processed using the Huber Epsilon loss function in the embodiment of the present invention to calculate a robust error value. The Huber Epsilon loss in the embodiment of the present invention is a special loss function that combines the advantages of the Mean Absolute Error (MAE) and the Mean Squared Error (MSE). This makes it particularly effective in dealing with outliers because it strikes a balance between penalizing small errors and effectively managing large errors. Basically, the loss function ignores minor deviations that may be caused by sensor noise or minor changes, while still being sensitive to significant differences that require attention. This balance ensures that the system remains stable even when noisy data or major errors occur occasionally. Among them, the behavior of the Huber Epsilon loss varies according to the size of the error, guided by two thresholds: ε (epsilon, a first preset threshold) and δ (delta, a second preset threshold), the first preset threshold corresponds to the threshold for ignoring the error, and the second preset threshold corresponds to the point where the error is considered a significant error and penalized in a quadratic manner. The first preset coefficient may be 0.5, for example, and the second preset coefficient may be 2, for example.

[0112] For small errors (those below a first preset threshold): the loss is set to zero, effectively ignoring small deviations, ensuring that insignificant fluctuations do not distort the results. This feature is critical to prevent small errors that may be caused by sensor noise from affecting the overall result.

[0113] For medium errors (errors between the first preset threshold and the second preset threshold): the loss transitions to a linear function. Within this range, medium errors are penalized but not as harshly as large errors. This linear response makes the system more tolerant than the squared error method used by MSE, allowing it to be less sensitive when dealing with medium differences.

[0114] For moderate errors (errors above a second preset threshold): the loss function becomes quadratic so that significant deviations are severely penalized, ensuring focus on these key differences. Within this range, the quadratic nature of the loss amplifies the impact of large errors, making them more influential in the overall loss calculation.

[0115] The Huber Epsilon loss function (Loss) in the embodiment of the present invention can be determined by the following expression:

[0116]

[0117] Wherein, x represents the target error, ∈ represents the first preset threshold, and δ represents the second preset threshold.

[0118] Figure 8 is a schematic diagram of a loss function provided according to an embodiment of the present invention, Figure 8 The Huber Epsilon loss is visualized, with the horizontal axis representing the error and the vertical axis representing the loss.

[0119] The vehicle positioning method provided by the embodiment of the present invention first determines the corresponding lane line polarity according to the distance from the vehicle for the map lane lines and detection lane lines to be matched, and iteratively matches the map lane lines and detection lane lines corresponding to the lane line polarities within a preset distance range in order from far to near from the vehicle, and then after correcting the original posture according to the correction information obtained by matching, a more accurate and reliable vehicle posture can be obtained, which can avoid trajectory jumping and unreliable positioning caused by the difference between the number of detected lanes and the number of lanes in the map, improve the reliability of the positioning result, combine multiple factors for evaluating the accuracy and reliability of the matching process to calculate the covariance used to measure the confidence of the posture correction, provide a more accurate and robust lane matching result, and more accurately control the autonomous driving vehicle to ensure the safety of autonomous driving.

[0120] In some embodiments, the high-precision map is a segment-based map, and there is at least one reference line in a point cloud format in each map segment. This step may include: using a preset search algorithm, searching for a target reference line corresponding to the original posture of the vehicle in the rightmost reference line of the map segment in the high-precision map; determining the map segment to which the target reference line belongs as the target map segment; and determining the local map corresponding to the original posture based on the target map segment. Thus, storing the reference line in a point cloud format can optimize the calculation, and selecting the rightmost reference line can effectively reduce the amount of calculation, thereby efficiently generating a local map.

[0121] Fig. 9 It is a flow chart of another vehicle positioning method provided according to an embodiment of the present invention. This embodiment is optimized based on the above optional embodiments. Fig.10 1 is a schematic diagram of a vehicle positioning solution architecture provided according to an embodiment of the present invention. The architecture of the vehicle positioning solution can improve the positioning accuracy of autonomous vehicles on highways, especially when GNSS data is unreliable or unavailable. Fig.10 As shown, three main inputs are used: the current original position, the high-precision map, and the detected lane lines. These inputs are processed by the vehicle positioning method provided by the embodiment of the present invention (such as the processing including the local map module, matching module, and covariance module in the figure), and the corrected position is output. Fig. 9 As shown, the method includes:

[0122] Step 901: Use a preset search algorithm to search for a target reference line corresponding to the original posture of the vehicle in the rightmost reference line of the map segment in the high-precision map.

[0123] Exemplarily, the preset search algorithm may be, for example, a K-dimensional tree (KDTree) algorithm. Fig.11 is a schematic diagram of a local map generation process provided according to an embodiment of the present invention, such as Fig.11 As shown, the relevant map segment is found according to the current position of the vehicle. This is achieved by using KDTree search to find the point closest to the vehicle in the center line of the point cloud of the map segment. Each map segment contains multiple reference lines (such as center lines), and the embodiment of the present invention can only select the rightmost one for this process. KDTree search helps to find the segment ID by matching the position of the vehicle in all selected center lines, because each point in the center line contains the information of the segment ID. Fig.11 In the Current Segment Search section, the target reference line shown is the center line of segment 1.

[0124] Step 902: Determine the map segment to which the target reference line belongs as the target map segment.

[0125] Exemplarily, the target map segment is determined according to information of the segment ID included in the target reference line.

[0126] Step 903: Determine an initial map point in the target map segment that corresponds to the starting point of the detected lane line.

[0127] Exemplarily, after determining the current segment ID (target map segment), the starting points of all detected lane lines in the segment in the map can be tracked. Optionally, a KDTree search can be used to quickly and reliably obtain all starting points.

[0128] Step 904: Starting from the initial map point, move along the map points in the target map segment toward the front of the vehicle, and accumulate the map points passed through during the movement until the line formed by the accumulated map points reaches the target length.

[0129] Exemplarily, for each of these initial map points, a local map is created by moving along the map point toward the front of the vehicle, accumulating distance until a predefined threshold (i.e., a target length, which can be, for example, the length of a detected lane line) is reached. As the local map grows from the starting point, the accumulated lines can be saved in an array. If the distance in front of the target map segment is insufficient, the next map segment can be continued until the target length is collected, which helps to handle the situation where the vehicle moves from one map segment to the next.

[0130] Step 905: Add a preset offset in front of and behind the line to obtain a map lane line, and generate a local map based on the map lane line.

[0131] For example, after collecting the required front length (target length), a static offset can be added to ensure that lane matching accurately aligns the detected lane lines with the local map. Static offsets (preset offsets) are added for both the front and rear of the vehicle. Fig.11 In the local map creation part, add a front offset in front of the lane line length (target length), and add a rear offset after the lane line length. The values ​​of the preset offsets added in front and behind can be the same, that is, the front offset and the rear offset can be the same. The result after adding the offset can be a point cloud array, where each index represents the local map data of a lane line, and a local map is generated based on all the generated map lane line data.

[0132] Step 906: Determine, based on the original position, a first lateral distance between the vehicle and each lane line in the local map, and a second lateral distance between the vehicle and each detected lane line in the lane detection result.

[0133] Step 907: determine the lane line polarity of each map lane line according to the first lateral distance, and determine the lane line polarity of each detection lane line according to the second lateral distance.

[0134] Step 908: For the map lane lines and the detection lane lines corresponding to the lane line polarities within the preset distance range, iterative matching is performed in order from far to near distance from the vehicle to obtain target correction information.

[0135] Step 909: Calculate the covariance based on the target pose, where the covariance is used to measure the confidence of the pose correction. The covariance includes a fitting metric and a loss based on a target error. The target error includes a lateral displacement error, and a lane width error or a single-line jitter error.

[0136] Among them, the loss based on the target error is the loss determined based on the Huber Epsilon loss function described above, which will not be repeated here.

[0137] The vehicle positioning method provided by the embodiment of the present invention stores the reference lines of the lanes in the high-precision map in a point cloud format, which can optimize the calculation and select the rightmost reference line, which can effectively reduce the amount of calculation and quickly find the target map segment. When the vehicle speed is too fast, if the front of the detected lane line exceeds the length of the local map, no matching points can be found. By adding a preset offset, more points can be added to the map to handle this situation, thereby improving the robustness of lane line matching and efficiently generating a local map. For the map lane lines and detection lane lines to be matched, the corresponding lane line polarity is first determined according to the distance from the vehicle, and the map lane lines and detection lane lines corresponding to the lane line polarity within the preset distance range are iteratively matched in order from far to near from the vehicle. This distance-polarity-based cascade lane-by-lane matching technology ensures that each lane line is matched separately according to its position and side (left or right), and can achieve more accurate matching in complex scenes and maintain effectiveness when the number of detected lanes is inconsistent with the map. Noisy lane detection may lead to poor pose correction. The embodiment of the present invention also introduces the Huber Epsilon loss function to calculate the covariance indicating the accuracy and reliability of the correction result. The loss function can effectively handle outliers, ignore small fluctuations and severely punish major errors and large fluctuations. By calculating the error based on the lateral displacement difference and the lane width error, the loss function ensures reliable pose correction, and maintains high confidence in the positioning results even in challenging and noisy environments. In addition, in traditional methods, missing or incomplete lane detection may lead to large positioning errors. The embodiment of the present invention solves this problem by implementing a single lane jitter compensation mechanism. When only one lane line is detected instead of two, the previously stored lane width information is used to compensate for the missing data, ensuring that the positioning remains stable and accurate even if the lane detection is incomplete or inconsistent. The embodiment of the present invention introduces a new heuristic-based lane-level positioning method for autonomous vehicles in highway scenarios, providing a robust and efficient solution for lane-level positioning on highways, and can be applied to low-cost autonomous vehicle systems using cameras and economical GNSS equipment. This progress improves the safety and performance of autonomous vehicles under real conditions and promotes the wider application of autonomous driving technology.

[0138] Fig.12 Schematic diagram of a vehicle positioning device provided according to an embodiment of the present invention. Fig.12As shown, the device includes: a local map determination module 1201, used to determine the local map corresponding to the original posture of the vehicle, wherein the local map is determined based on the high-precision map; a lateral distance determination module 1202, used to determine the first lateral distance between the vehicle and each map lane line in the local map, and the second lateral distance between the vehicle and each detected lane line in the lane detection result, based on the original posture; a lane line polarity determination module 1203, used to determine the lane line polarity of each map lane line according to the first lateral distance, and determine the lane line polarity of each detected lane line according to the second lateral distance, wherein the lane line polarity is used to characterize the lane line located in the lateral direction of the vehicle and the order of the lane lines in the lateral direction; a lane line matching module 1204, used to match the map lane line and the detected lane line corresponding to the lane line polarity to obtain target correction information; a posture correction module 1205, used to correct the original posture based on the target correction information to obtain the target posture of the vehicle.

[0139] The vehicle positioning device provided by the embodiment of the present invention, for the map lane lines and detection lane lines to be matched, first determines the corresponding lane line polarity according to the distance from the vehicle, matches the map lane lines and detection lane lines corresponding to the lane line polarities, and then corrects the original posture according to the correction information obtained by matching. A more accurate and reliable vehicle posture can be obtained, which can avoid trajectory jumping and unreliable positioning caused by the difference between the number of detected lanes and the number of lanes in the map, thereby improving the reliability of the positioning result.

[0140] Optionally, the lane line matching module is used to iteratively match the map lane lines and detection lane lines corresponding to the lane line polarity within a preset distance range in order from far to near from the vehicle to obtain target correction information.

[0141] Optionally, the lane line matching module includes: a current lane line pair determination unit, used to determine the current lane line pair to be matched in order from far to near for the map lane line and the detection lane line corresponding to the lane line polarity within a preset distance range, wherein the current lane line pair includes a map lane line and a detection lane line; a target detection lane line determination unit, used to determine the detection lane line in the current lane line pair as the target detection lane line if the current lane line pair is the first lane line pair; if the current lane line pair is not the first lane line pair, using the reference calibration lane line of the current lane line pair to determine the detection lane line in the current lane line pair as the target detection lane line The positive information transforms the detected lane line in the current lane line pair, and determines the transformed detected lane line as the target detected lane line, wherein the reference correction information is the correction information obtained in the last matching; a matching unit is used to match the target detected lane line in the current lane line pair with the map lane line to obtain the correction information obtained in this matching; a target correction information determination unit is used to redetermine the current lane line pair to be matched if the current lane line pair is not the last lane line pair; if the current lane line pair is the last lane line pair, the correction information obtained in this matching is determined as the target correction information.

[0142] Optionally, the device also includes: a covariance calculation module, which is used to calculate the covariance based on the target posture after correcting the original posture based on the target correction information to obtain the target posture of the vehicle, wherein the covariance is used to measure the confidence of the posture correction, and the covariance includes a fitting metric and a loss based on a target error, and the target error includes at least one of a lateral displacement error, a lane width error, and a single-line jitter error.

[0143] Optionally, the lane line polarity determination module is used to: group each lane line according to the positive or negative sign of the lateral distance to obtain a first lane line group and a second lane line group, wherein the lateral distance is the first lateral distance or the second lateral distance, and the lane line is the map lane line or the detection lane line; sort the lane lines in the first lane line group from near to far from the vehicle according to the size of the lateral distance to obtain a first lane line sequence; sort the lane lines in the second lane line group from near to far from the vehicle according to the size of the lateral distance to obtain a second lane line sequence; for each lane line, determine whether the lane line is located in the lateral direction of the vehicle according to the positive or negative sign of the lateral distance, determine the lane line order of the lane line in the lateral direction according to the sorting position of the lane line in the corresponding lane line sequence, and determine the lane line polarity of the lane line according to the lateral direction and the lane line order.

[0144] Optionally, the high-precision map is a segment-based map, and there is at least one reference line in point cloud format in each map segment; wherein the local map determination module includes: a target reference line search unit, used to use a preset search algorithm to search for a target reference line corresponding to the original posture of the vehicle in the rightmost reference line of the map segment in the high-precision map; a target map segment determination unit, used to determine the map segment to which the target reference line belongs as a target map segment; and a local map determination unit, used to determine the local map corresponding to the original posture based on the target map segment.

[0145] Optionally, a local map determination unit is used to determine an initial map point in the target map segment corresponding to the starting point of the detection lane line; starting from the initial map point, move toward the front of the vehicle along the map points in the target map segment, and accumulate the map points passed through during the movement until the line formed by the accumulated map points reaches the target length; add a preset offset in front and behind the line to obtain a map lane line, and generate a local map based on the map lane line.

[0146] The vehicle positioning device provided in the embodiment of the present invention can execute the vehicle positioning method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0147] Fig.13 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0148] like Fig.13As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to 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.

[0149] A number 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 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.

[0150] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated 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 executes the various methods and processes described above, such as a vehicle positioning method.

[0151] In some embodiments, the vehicle positioning method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the vehicle positioning method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the vehicle positioning method in any other appropriate manner (e.g., by means of firmware).

[0152] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0153] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0154] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0155] To provide interaction with a user, the systems and techniques described herein may 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 trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0156] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may 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.

[0157] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may 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 to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0158] The embodiments of the present disclosure provide a computer program product, including a computer program, which, when executed by a processor, implements the vehicle positioning method provided in the above embodiments.

[0159] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0160] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A vehicle positioning method, characterized in that: include: Determine a local map corresponding to the original posture of the vehicle, wherein the local map is determined based on the high-precision map; Determine, based on the original position, a first lateral distance between the vehicle and each lane line in the local map, and a second lateral distance between the vehicle and each lane line detected in the lane detection result; Determining a lane line polarity of each of the map lane lines according to the first lateral distance, and determining a lane line polarity of each of the detected lane lines according to the second lateral distance, wherein the lane line polarity is used to characterize the lateral direction of the lane line of the vehicle and the order of the lane lines in the lateral direction; Matching the map lane line and the detection lane line corresponding to the lane line polarity to obtain target correction information; The original posture is corrected based on the target correction information to obtain the target posture of the vehicle.

2. The method according to claim 1, characterized in that The matching of the map lane line and the detection lane line corresponding to the lane line polarity to obtain target correction information includes: For the map lane lines and the detection lane lines corresponding to the lane line polarity within the preset distance range, iterative matching is performed in order from far to near distance from the vehicle to obtain target correction information.

3. The method according to claim 2, characterized in that The map lane lines and the detection lane lines corresponding to the polarity of the lane lines within the preset distance range are iteratively matched in order from far to near from the vehicle to obtain target correction information, including: For the map lane lines and the detection lane lines corresponding to the lane line polarity within the preset distance range, determine the current lane line pair to be matched in order from far to near from the vehicle, wherein the current lane line pair includes a map lane line and a detection lane line; If the current lane line pair is the first lane line pair, the detected lane line in the current lane line pair is determined as the target detected lane line; if the current lane line pair is not the first lane line pair, the detected lane line in the current lane line pair is transformed using the reference correction information of the current lane line pair, and the transformed detected lane line is determined as the target detected lane line, wherein the reference correction information is the correction information obtained in the last matching; Matching the target detected lane line in the current lane line pair with the map lane line to obtain correction information obtained from this matching; If the current lane line pair is not the last lane line pair, the current lane line pair to be matched is re-determined; if the current lane line pair is the last lane line pair, the correction information obtained from this matching is determined as the target correction information.

4. The method according to claim 1, characterized in that After the original posture is corrected based on the target correction information to obtain the target posture of the vehicle, the method further includes: A covariance is calculated based on the target pose, wherein the covariance is used to measure the confidence of the pose correction, the covariance includes a fitting metric and a loss based on a target error, and the target error includes at least one of a lateral displacement error, a lane width error, and a single-line jitter error.

5. The method according to claim 4, characterized in that The target error-based loss is determined as follows: When the target error is less than a first preset threshold, the loss based on the target error is zero; When the target error is greater than or equal to the first preset threshold and less than a second preset threshold, the loss based on the target error is the difference between the target error and a first preset value, wherein the first preset value is determined based on the product of the first preset threshold and a first preset coefficient, and the first preset coefficient is less than 1; When the target error is greater than or equal to the second preset threshold, the loss based on the target error is the quotient of the square of the target error and a second preset value, wherein the second preset value is determined based on the product of the second preset threshold and a second preset coefficient, and the second preset coefficient is greater than 1.

6. The method according to claim 4, characterized in that The single-line jitter error is determined by: In the case where a single lane line is detected on one side of the vehicle this time, if double lane lines were detected on both sides of the vehicle last time, a first lateral distance between the single lane line and the vehicle is determined, a second lateral distance between the cached single lane line and the vehicle is determined, and a single-line jitter error is determined based on the difference between the first distance and the second distance.

7. The method according to claim 1, characterized in that Determine the lane polarity of each lane based on the lateral distance, including: Grouping each lane line according to the positive or negative value of the lateral distance to obtain a first lane line group and a second lane line group, wherein the lateral distance is the first lateral distance or the second lateral distance, and the lane line is the map lane line or the detection lane line; According to the size of the lateral distance, the lane lines in the first lane line group are sorted from near to far from the vehicle to obtain a first lane line sequence; according to the size of the lateral distance, the lane lines in the second lane line group are sorted from near to far from the vehicle to obtain a second lane line sequence; For each lane line, the lane line is determined to be located in the lateral direction of the vehicle based on the positive or negative value of the lateral distance, the lane line order of the lane line in the lateral direction is determined based on the sorting position of the lane line in the corresponding lane line sequence, and the lane line polarity of the lane line is determined based on the lateral direction and the lane line order.

8. The method according to claim 1, characterized in that: The high-precision map is a segment-based map, and each map segment contains at least one reference line in a point cloud format; wherein the local map corresponding to the original position of the vehicle is determined, including: Using a preset search algorithm, searching for a target reference line corresponding to the original posture of the vehicle in the rightmost reference line of the map segment in the high-precision map; Determine the map segment to which the target reference line belongs as the target map segment; A local map corresponding to the original posture is determined based on the target map segment.

9. The method according to claim 8, characterized in that Determining a local map corresponding to the original posture based on the target map segment includes: Determining an initial map point in the target map segment corresponding to a starting point of a detected lane line; Starting from the initial map point, moving along the map points in the target map segment toward the front of the vehicle, and accumulating the map points passed through during the movement until a line formed by the accumulated map points reaches a target length; A preset offset is added in front of and behind the line to obtain a map lane line, and a local map is generated based on the map lane line.

10. A vehicle positioning device, characterized in that: include: A local map determination module, used to determine a local map corresponding to an original posture of the vehicle, wherein the local map is determined based on a high-precision map; a lateral distance determination module, configured to determine, based on the original position, a first lateral distance between the vehicle and each lane line in the local map, and a second lateral distance between the vehicle and each lane line detected in the lane detection result; a lane line polarity determination module, configured to determine the lane line polarity of each of the map lane lines according to the first lateral distance, and to determine the lane line polarity of each of the detection lane lines according to the second lateral distance, wherein the lane line polarity is used to characterize the lateral direction of the lane line of the vehicle and the order of the lane lines in the lateral direction; A lane line matching module is used to match the map lane line and the detection lane line corresponding to the lane line polarity to obtain target correction information; The posture correction module is used to correct the original posture based on the target correction information to obtain the target posture of the vehicle.

11. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; 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 so that the at least one processor can execute the vehicle positioning method according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the vehicle positioning method according to any one of claims 1 to 9 when executed.

13. A computer program product, characterized in that The computer program product comprises a computer program, which, when executed by a processor, implements the vehicle positioning method according to any one of claims 1 to 9.

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