A real-time map matching method based on geometric features

Abstract

The present invention discloses a real-time map matching method based on geometric features. The method establishes the geometric features of a target vehicle based on gridding processing of a high-precision map, determines the current candidate matching road set of the target vehicle according to the global UTM coordinates of the target vehicle, traverses the set, sequentially calculates the similarity between the current state of the target vehicle and all lanes in each candidate road, selects the lane with the highest similarity as the matching result, and then calculates the coordinates of the target vehicle relative to the matching road in a coordinate system established based on a road reference line based on the matching result. The method comprehensively considers two types of information, distance and direction, to measure the similarity between the current state of the vehicle and the center line of the lane. The map matching algorithm based on geometric features effectively narrows the range of candidate lanes for map matching, thereby improving the map matching efficiency and ensuring the real-time performance of the matching process.

Description

Technical Field

[0001] The present invention belongs to the technical field of autonomous driving map matching and positioning, and specifically relates to a real-time map matching method based on geometric features. Background Art

[0002] Map matching is a key technology based on the Geographic Information System (GIS) and plays a vital role in autonomous vehicles. Its core concept is to determine the vehicle's current road, lane, and position within the lane by comparing the vehicle's sensor data (such as GPS or LiDAR) with electronic map data. Map matching is of great significance in the field of autonomous driving, specifically including the following:

[0003] Map matching algorithms can be used to correct GPS or LiDAR positioning errors and improve positioning accuracy. Generally speaking, vehicles travel on roads. However, due to factors such as bad weather or measurement errors in the positioning module itself, the vehicle's positioning may deviate from the road. In such cases, map matching algorithms can effectively correct for these deviations.

[0004] Map matching algorithms can significantly enhance the perception capabilities of autonomous vehicles. Typically, a vehicle's sensors only provide global location information (such as latitude and longitude or UTM coordinates). Map matching algorithms can help the vehicle further acquire a wealth of static scene information about its surroundings, such as lane markings, intersections, traffic lights, and stop signs. This information can be used to supplement sensor data and enhance the perception capabilities of autonomous vehicles.

[0005] Map matching algorithms can also provide a large amount of prior information for subsequent modules of autonomous driving, such as trajectory prediction and decision planning, to improve the intelligence of the system.

[0006] Due to the importance of map matching, this algorithm has become a research hotspot in the field of autonomous driving. Geometric feature-based map matching algorithms have been widely studied and applied in real-world production environments due to their strong interpretability, stability, and low computational complexity. The core idea of ​​this method is to perform a similarity match between the vehicle's current state information and the centerlines of all candidate lanes, selecting the lane with the highest similarity as the matching result. Despite these advantages, when deploying the algorithm on a real vehicle, since most maps contain a large number of lanes, performing global map matching across all lanes alone cannot meet the real-time requirements of autonomous driving systems. Summary of the Invention

[0007] In view of this, the present invention provides a real-time map matching method based on geometric features, which establishes the geometric features of the target vehicle based on grid processing of high-precision maps to achieve real-time matching of the target vehicle to the map.

[0008] The present invention provides a real-time map matching method based on geometric features, which specifically includes the following steps:

[0009] Step 1: Divide the high-precision map into multiple square grids. The area composed of the grids completely covers all roads in the map. The grid borders are parallel to the UTM coordinate system of the map. The lower left corner of the map is used as the reference point P. r , P r The global UTM coordinates are (x r ,y r ), the grid where any point P on the map is located and its index in the grid are recorded as the grid index of P according to the reference point. The global UTM coordinates of point P are (x, y). The grid index includes the horizontal index x index and the vertical index y index They are: and d is the grid width;

[0010] Step 2: Determine the grid road index table of the grid, which is used to record the numbers of all roads contained in the grid; establish a candidate matching road set for the vehicle to store optional roads that match the vehicle, and initialize the candidate matching road set to an empty set;

[0011] Step 3: Get the real-time UTM coordinates of the target vehicle to be matched. If the UTM coordinates exist in the candidate matching road set of the target vehicle, execute step 4; otherwise, execute step 5.

[0012] Step 4: Obtain the matching road corresponding to the UTM coordinates in the candidate matching road set, obtain the predecessor and successor roads adjacent to the matching road from the high-precision map, and form the current candidate matching road set corresponding to the UTM coordinates from the matching road and its predecessor and successor roads, and then execute step 6;

[0013] Step 5: Calculate the grid index of the target vehicle's grid based on the real-time UTM coordinates obtained in step 3, and save all roads contained in the grid and other grids adjacent to the grid into the candidate matching road set of the UTM coordinates;

[0014] Step 6. Calculate the similarity between the centerline of each lane in the candidate matching road set and the vehicle's current position. Use the lane with the highest similarity as the final matching result. Obtain the lane ID corresponding to the final matching result. Then, obtain the corresponding road ID based on the lane ID. Convert the target vehicle's global UTM coordinates to ST coordinates to complete real-time matching between the vehicle and the map. The ST coordinate system is based on the road reference line, with the direction along the road reference line as the S direction and the direction perpendicular to the road reference line as the T direction.

[0015] Furthermore, the grid road index table of the grid is determined by sequentially calculating the grid index to which each point on all road reference lines in the map belongs using horizontal and vertical index calculation formulas. If a discrete point on a road reference line is located within a grid, the grid is determined to contain the road, and the grid road index table of the grid is obtained, which records the road IDs of all roads contained in each grid cell.

[0016] Furthermore, the grid road index table is obtained in an offline manner and saved in the form of a file.

[0017] Furthermore, in step 6, the similarity between the center line of each lane in the candidate matching road set and the current position of the vehicle is calculated, and the similarity function used is shown in the following formula:

[0018] S i =w1Δd i +w2Δθ i

[0019] Among them, S i is the similarity between the current position of the target vehicle and the centerline of the i-th lane in the candidate matching road set, Δd i is the vertical distance between the current position of the target vehicle and the projection point of the lane centerline, Δθ i is the heading deviation between the current position of the target vehicle and the projection point on the lane centerline. w1 and w2 are both weight coefficients. The projection point is the point closest to the current position of the target vehicle among all discrete points on the lane centerline.

[0020] Furthermore, the projection point is obtained through binary search.

[0021] Furthermore, the method of obtaining the corresponding road ID according to the lane ID in step 6 is: querying the OpenDrive map according to the lane ID to obtain the road ID to which it belongs.

[0022] Beneficial effects:

[0023] The present invention establishes the geometric features of the target vehicle based on the gridding processing of the high-precision map, determines the current candidate matching road set of the target vehicle according to the global UTM coordinates of the target vehicle, traverses the set, and sequentially calculates the similarity between the current state of the target vehicle and all lanes in each candidate road. The lane with the highest similarity is selected as the matching result. Based on the matching result, the coordinates of the target vehicle relative to the matching road in the coordinate system established based on the road reference line are calculated. The similarity between the current state of the vehicle and the center line of the lane is measured by comprehensively considering two types of information, distance and direction. The map matching algorithm based on geometric features effectively narrows the range of map matching candidate lanes, thereby improving map matching efficiency and ensuring the real-time performance of the matching process. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of the processing flow of a real-time map matching method based on geometric features provided by the present invention.

[0025] Figure 2 This is a schematic diagram of a real-time map matching method based on geometric features provided by the present invention after gridding a high-precision map.

[0026] Figure 3 A schematic diagram of variables involved in a similarity function used in a real-time map matching method based on geometric features provided by the present invention.

[0027] Figure 4 A schematic diagram of converting UTM coordinates to ST coordinates used in a real-time map matching method based on geometric features provided by the present invention. DETAILED DESCRIPTION

[0028] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0029] The present invention provides a real-time map matching method based on geometric features. The basic idea is to determine the current candidate matching road set of the target vehicle based on the global UTM coordinates of the target vehicle, traverse the set, calculate the similarity between the current state of the target vehicle and all lanes in each candidate road in turn, select the lane with the highest similarity as the matching result, and then calculate the coordinates of the target vehicle relative to the matching road in a coordinate system established based on the road reference line based on the matching result.

[0030] The map used in the present invention is a high-precision map in the OpenDrive format, and the map in this format contains the centerline information of all lanes. The ST coordinate system is the coordinate system relative to the road reference line in the OpenDrive map, where the S direction is along the road reference line and the T direction is perpendicular to the road reference line. Existing map matching algorithms usually rely on global searches, or perform complex calculations through a large amount of sensor data to determine the matching relationship between the vehicle and the road, resulting in low matching efficiency, especially in autonomous driving scenarios with high real-time requirements. The performance is particularly poor. Therefore, in order to improve the real-time performance of map matching, the present invention proposes a real-time map matching strategy based on a map gridding strategy to determine a set of candidate roads, so as to narrow the scope of matching roads and improve the efficiency and accuracy of the algorithm.

[0031] The present invention provides a real-time map matching method based on geometric features. The overall process is as follows: Figure 1 As shown, the specific steps include:

[0032] Step 1: Grid the high-precision map, that is, divide the high-precision map into multiple grids, such as Figure 2 As shown, the grids are all squares. The area composed of all grids can completely cover all roads in the map. The grid borders are parallel to the UTM coordinate system of the map. The lower left corner of the map is used as the reference point P. r , P r The global UTM coordinates are (x r ,y r ), for any point P on the map, the grid where the point is located and its index in the grid can be determined based on the reference point, which is recorded as the grid index of P. The grid index includes the horizontal index and the vertical index, which are respectively:

[0033] Horizontal Index

[0034] Vertical Index

[0035] Where (x, y) is the global UTM coordinate of point P, and d is the grid width. This yields the index of the grid to which each point on all road reference lines belongs. If a discrete point on a road reference line falls within a grid, that grid is considered to contain the road. This yields a grid road index table for each grid, which records the numbers of all roads contained within each grid. A candidate matching road set for the vehicle is established, which stores candidate matching roads for the selected road based on the vehicle's current location and is initialized to an empty set.

[0036] The grid index of the grid to which each point on all road reference lines belongs is obtained by sequentially substituting each point on all road reference lines in the map into the horizontal index and vertical index calculation formulas to calculate the grid index to which it belongs. If a discrete point on a road reference line is located within a grid, the grid is considered to contain the road. The above operation results in a grid road index table, which records the road IDs of all roads contained in each grid unit.

[0037] To further improve the real-time performance of the algorithm, the grid road index table is obtained offline and saved in the form of a file to reduce its impact on the operation of each cycle of the autonomous driving system.

[0038] Step 2: Get the real-time UTM coordinates of the target vehicle to be matched. If the UTM coordinates exist in the candidate matching road set, execute step 3; otherwise, execute step 4.

[0039] Step 3: Obtain the matching road corresponding to the UTM coordinate in the candidate matching road set. Based on the matching road, obtain the predecessor and successor roads adjacent to it from the high-precision map. The matching road and its predecessor and successor roads constitute the current candidate matching road set corresponding to the UTM coordinate, and execute step 5.

[0040] This invention uses a strategy for constructing candidate matching roads based on historical information, effectively narrowing the matching range and improving algorithm efficiency. Specifically, when the target vehicle has been previously detected by the sensor, the algorithm can leverage the results of the previous map matching to optimize candidate road selection. Because the sensor's detection frequency is high and the vehicle's range of motion is limited within a continuous detection cycle, the algorithm can use the matching results of the previous frame as a reference, selecting the road matched by the vehicle in the previous frame and its successors as the current candidate road set.

[0041] Step 4: Calculate the grid index of the grid to which the target vehicle belongs based on the real-time UTM coordinates of the target vehicle to be matched obtained in step 2, and save all roads contained in the grid to which it belongs and the eight grids adjacent to the grid into the candidate matching road set of the UTM coordinates, such as Figure 2 The dotted area is shown.

[0042] Because the grid road index table obtained in advance by the algorithm is not completely accurate, some grid cells may be omitted. Therefore, the present invention not only includes roads in the grid to which the target vehicle belongs, but also expands the candidate matching road set to roads contained in surrounding adjacent grids. Furthermore, because actual roads are two-dimensional entities with width, while road reference lines are one-dimensional entities with only length, indirectly determining which grid a road belongs to based on discrete road reference line coordinates is inaccurate. Therefore, the present invention also appropriately expands the matching grid area to compensate for this issue.

[0043] Step 5: Calculate the similarity between the center line of each lane in the candidate matching road set and the current position of the vehicle. Similarity matching involves variables such as Figure 3 As shown, the lane with the highest similarity is taken as the final matching result, the lane ID corresponding to the final matching result is obtained, and then the corresponding road ID is obtained according to the lane ID.

[0044] In order to ensure the accuracy of the measurement, the present invention uses a similarity function for measurement, and the similarity function is shown as follows:

[0045] S i =w1Δd i +w2Δθ i (3)

[0046] Among them, S i is the similarity between the current position of the target vehicle and the centerline of the i-th lane in the candidate matching road set, Δd i is the vertical distance between the current position of the target vehicle and the projection point of the lane centerline, Δθ i is the heading deviation between the current position of the target vehicle and the projection point on the lane centerline, w1 and w2 are weight coefficients, and the projection point is the point closest to the current position of the target vehicle among all discrete points on the lane centerline. According to the definition of formula (3), S i The smaller the value, the higher the similarity between the vehicle's current position and the lane.

[0047] In order to ensure the accuracy of the map matching algorithm in a more complex lane overlap environment (intersection scenario), Equation (3) comprehensively considers the distance deviation factor and the heading deviation factor when measuring the similarity between the vehicle and the lane. In addition, the projection point can be obtained by binary search, such as Figure 4 shown.

[0048] The process of selecting the lane with the highest similarity as the matching result can be expressed as follows:

[0049]

[0050] Among them, i ★ is the final matching result, which means the lane ID with the highest similarity to the vehicle among all lanes in the candidate matching road set.

[0051] Furthermore, the lane ID can be further queried in the OpenDrive map to obtain the road ID to which it belongs.

[0052] Step 6: Convert the target vehicle's global UTM coordinates into ST coordinates.

[0053] To facilitate the calculation of subsequent modules of the autonomous driving system (such as prediction, planning, and decision-making), after obtaining the lane ID and road ID to which the vehicle belongs, the map matching algorithm needs to further convert the vehicle's global UTM coordinates into the ST coordinate system. The ST coordinate system is essentially a Frenet coordinate system. In the OpenDrive format high-precision map, the ST coordinate system is based on the road reference line, with the direction along the road reference line as the S direction and the direction perpendicular to the road reference line as the T direction. The conversion process is shown in formulas (5) and (6):

[0054]

[0055] Where p is the target vehicle position to be converted, and its UTM coordinates (x, y) are known. Point p1 is the projection point of p on the centerline of its lane. The UTM coordinates (x1, y1) and ST coordinates (s1, t1) of this point can be parsed from the OpenDrive high-precision map as known quantities. is the vector from the projection point to the vehicle position. Since the UTM coordinates of the projection point p1 and the vehicle position p are both known, this vector is also known and can be expressed as (x-x1, y-y1); is the unit direction vector at the projection point, which can be parsed from the OpenDrive map.

[0056] In summary, the above are only preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A real-time map matching method based on geometric features, characterized in that: The specific steps include: Step 1: Divide the high-precision map into multiple square grids. The area composed of the grids completely covers all roads in the map. The grid borders are parallel to the UTM coordinate system of the map. The lower left corner of the map is used as the reference point P. r , P r The global UTM coordinates of the point P on the map are (xr, yr). The grid where the point P is located and its index in the grid are recorded as the grid index of P according to the reference point. The global UTM coordinates of the point P are (x, y). The grid index includes the horizontal index x index and the vertical index y index They are: and d is the grid width; Step 2: Determine the grid road index table of the grid, which is used to record the numbers of all roads contained in the grid; establish a candidate matching road set for the vehicle to store optional roads that match the vehicle, and initialize the candidate matching road set to an empty set; Step 3: Get the real-time UTM coordinates of the target vehicle to be matched. If the UTM coordinates exist in the candidate matching road set of the target vehicle, execute step 4; otherwise, execute step 5. Step 4: Obtain the matching road corresponding to the UTM coordinates in the candidate matching road set, obtain the predecessor and successor roads adjacent to the matching road from the high-precision map, and form the current candidate matching road set corresponding to the UTM coordinates from the matching road and its predecessor and successor roads, and then execute step 6; Step 5: Calculate the grid index of the target vehicle's grid based on the real-time UTM coordinates obtained in step 3, and save all roads contained in the grid and other grids adjacent to the grid into the candidate matching road set of the UTM coordinates; Step 6. Calculate the similarity between the centerline of each lane in the candidate matching road set and the vehicle's current position. Use the lane with the highest similarity as the final matching result. Obtain the lane ID corresponding to the final matching result. Then, obtain the corresponding road ID based on the lane ID. Convert the target vehicle's global UTM coordinates to ST coordinates to complete real-time matching between the vehicle and the map. The ST coordinate system is based on the road reference line, with the direction along the road reference line as the S direction and the direction perpendicular to the road reference line as the T direction.

2. The real-time map matching method according to claim 1, characterized in that: The method for determining the grid road index table of a grid is: using horizontal index and vertical index calculation formulas to sequentially calculate the grid index to which each point on all road reference lines in the map belongs. If a discrete point on a road reference line is located within a grid, the grid is determined to contain the road, and the grid road index table of the grid is obtained, which records the road IDs of all roads contained in each grid cell.

3. The real-time map matching method according to claim 1, characterized in that: The grid road index table is obtained in an offline manner and saved in the form of a file.

4. The real-time map matching method according to claim 1, characterized in that: In step 6, the similarity between the center line of each lane in the candidate matching road set and the current position of the vehicle is calculated, and the similarity function used is shown in the following formula: S i =w1Δd i +w2Δθ i Among them, S i is the similarity between the current position of the target vehicle and the centerline of the i-th lane in the candidate matching road set, Δd i is the vertical distance between the current position of the target vehicle and the projection point of the lane centerline, Δθ i is the heading deviation between the current position of the target vehicle and the projection point on the lane centerline. w1 and w2 are both weight coefficients. The projection point is the point closest to the current position of the target vehicle among all discrete points on the lane centerline.

5. The real-time map matching method according to claim 4, characterized in that: The projection points are obtained through binary search.

6. The real-time map matching method according to claim 1, characterized in that: The method of obtaining the corresponding road ID according to the lane ID in step 6 is: querying the OpenDrive map according to the lane ID to obtain the road ID to which it belongs.

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