Line punching detection method and device, medium, program product and vehicle

Abstract

The invention provides a line punching detection method and device, a medium, a program product and a vehicle, and relates to the technical field of data processing. The line punching detection method comprises the following steps: acquiring driving data of a vehicle in a racing track; the predicted position of the vehicle in the racing track at the next predicted moment is determined according to the driving data, and the interval between the adjacent predicted moments is smaller than the set duration; and when the predicted position exceeds the start-stop line of the racing track, determining the line punching time of the vehicle according to the driving data. According to the invention, high-frequency prediction is carried out on the position of the vehicle through the real-time driving data of the vehicle, the line-punching time can be accurately recorded when the line-punching time of the vehicle is predicted, the accuracy and real-time performance of detection of the line-punching time of the vehicle are remarkably improved, and timing errors caused by human factors or equipment delay are effectively avoided.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a line crossing detection method, device, medium, program product, and vehicle. Background Art

[0002] In related technologies, such as racing and intelligent driving tests, accurately recording vehicle finish times is a key step in measuring vehicle performance and race results. In the racing field, in particular, higher requirements are placed on vehicle performance and race timing accuracy. Summary of the Invention

[0003] The present disclosure provides a line crossing detection method, device, medium, program product and vehicle to improve the accuracy and real-time performance of line crossing detection.

[0004] According to a first aspect of an embodiment of the present disclosure, a line crossing detection method is provided, comprising: Obtaining the vehicle's driving data on the track; determining a predicted position of the vehicle within the track at a next predicted moment based on the driving data, wherein an interval between adjacent predicted moments is less than a set time duration; When the predicted position exceeds the start and end lines of the track, a finish line time of the vehicle is determined based on the driving data.

[0005] Optionally, the driving data includes an actual position, speed data, and driving direction of the vehicle, and determining a predicted position of the vehicle within the track at a next predicted moment based on the driving data includes: determining a first projection position of the actual position on a reference line of the racetrack; determining a first distance between the actual position and the start and end lines according to the first projection position; A finish line time of the vehicle is determined based on the vehicle speed data, the driving direction, and the first distance.

[0006] Optionally, determining a first distance between the actual position and the start and end lines according to the first projection position includes: A first distance between the actual position and the start-end lines is determined according to the first projection position and an intersection point between the start-end lines and the reference line.

[0007] Optionally, determining a first projection position of the actual position on a reference line of the track includes: Determine a track segment to be matched on the reference line corresponding to the actual position; The first projected position is determined according to a projected distance between the actual position and the track segment to be matched.

[0008] Optionally, determining a first projection position of the actual position on a reference line of the track includes: Determine a track segment to be matched on the reference line corresponding to the actual position; determining a target track segment among the track segments to be matched based on a positional relationship among the start and end lines, the actual position, the predicted position of the vehicle at the next predicted moment, and the track segments to be matched; The first projection position is determined according to the actual position and the target track segment.

[0009] Optionally, determining a target track segment from the track segments to be matched based on a positional relationship among the start and end lines, the actual position, the predicted position of the vehicle at the next predicted moment, and the track segments to be matched includes: Determining a second distance between the point to be matched and the actual position, a third distance between the start and end reference points and the point to be matched, a fourth distance between the actual position and the predicted position, a fifth distance between the predicted position and the point to be matched, a first tangent angle difference between the start and end reference points and the point to be matched, and a second tangent angle difference between the actual position and the point to be matched, wherein the start and end reference points are intersections of the start and end lines and the reference line, and the point to be matched is a second projection position of the actual position on the track segment to be matched; determining a degree of matching between the to-be-matched track segment and the actual position according to the second distance, the third distance, the fourth distance, the fifth distance, the first tangent angle difference, and the second tangent angle difference; A target track segment matching the actual position among the track segments to be matched is determined according to the matching degrees of the track segments to be matched.

[0010] Optionally, when the predicted position exceeds the start and end lines of the track, before determining the vehicle's finish line time based on the driving data, the method further includes: determining a third projection position of the predicted position on a reference line of the track; Whether the predicted position exceeds the start and end lines of the track is determined based on the third projection position and the start and end reference points, where the start and end reference points are intersections of the start and end lines and the reference line.

[0011] Optionally, determining a predicted position of the vehicle within the track at a next predicted moment based on the driving data includes: When the vehicle is in the finish line buffer area of ​​the track, the predicted position of the vehicle in the track at the next predicted moment is determined based on the driving data, wherein the distance between the first boundary of the finish line buffer area and the start and end lines is the sixth distance, and the second boundary of the finish line buffer area is the start and end lines.

[0012] Optionally, before determining the predicted position of the vehicle within the track at the next predicted moment based on the driving data when the vehicle is in the finish line buffer area of ​​the track, the method further includes: Determine whether the vehicle is in a finish line buffer area of ​​the track based on a first projection position of the actual position of the vehicle on a reference line of the track, and start and end reference points, where the start and end reference points are intersections of the start and end lines with the reference line.

[0013] Optionally, the driving data includes an actual position of the vehicle within the track, and obtaining the driving data of the vehicle within the track includes: Obtaining positioning information of the vehicle; acquiring track data of a track within a set range around the vehicle according to the positioning information; The positioning information of the vehicle is associated with the track data of the track to obtain the actual position of the vehicle within the track.

[0014] Optionally, associating the positioning information of the vehicle with track data of the track to obtain the actual position of the vehicle within the track includes: Determining grid data corresponding to the track data, the grid data including a first mapping relationship between track segments of the track and grids; performing coordinate conversion on the positioning information of the vehicle according to the grid data to obtain a second mapping relationship between the position of the vehicle and the grid; The actual position of the vehicle within the track is obtained according to the first mapping relationship and the second mapping relationship.

[0015] Optionally, acquiring track data of a track within a set range around the vehicle according to the positioning information includes: determining, based on the positioning information, whether the positioning position of the vehicle is within a track-associated area corresponding to the track, the track-associated area being obtained by expanding the set distance by a grid boundary of grid data corresponding to the track, the grid data including a first mapping relationship between track segments and grids of the track; When the positioning position of the vehicle is within the track associated area corresponding to the track, track data of the track is acquired.

[0016] According to a second aspect of an embodiment of the present disclosure, there is provided a line crossing detection device, comprising: an acquisition module, configured to acquire driving data of the vehicle on the track; a position prediction module configured to determine a predicted position of the vehicle within the track at a next predicted moment based on the driving data, wherein an interval between adjacent predicted moments is less than a set time duration; The time prediction module is configured to determine the vehicle's finishing time based on the driving data when the predicted position exceeds the start and end lines of the track.

[0017] According to a third aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored. When the program instructions are executed by a processor, the line cross detection method described in the first aspect of the embodiment of the present disclosure is implemented.

[0018] According to a fourth aspect of an embodiment of the present disclosure, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the line cross detection method described in the first aspect of the embodiment of the present disclosure.

[0019] According to a fifth aspect of an embodiment of the present disclosure, a vehicle is provided, comprising: a storage device for storing a computer program; An execution device is used to execute the computer program to implement the line crossing detection method described in the first aspect of the embodiment of the present disclosure.

[0020] The technical solutions provided by the embodiments of the present disclosure may have the following beneficial effects: The present disclosure uses a vehicle's driving data on a track to determine the vehicle's predicted position on the track at the next predicted time. When the predicted position exceeds the track's start and finish lines, the system determines the vehicle's finish time based on the driving data, with the interval between adjacent predicted times being less than a set duration. This high-frequency prediction of the vehicle's position based on real-time driving data allows accurate recording of the finish time when the vehicle is predicted to cross the finish line. This significantly improves the accuracy and real-time nature of vehicle finish time detection and effectively avoids timing errors caused by human factors or equipment delays.

[0021] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0023] Figure 1 The figure is a flow chart showing a line crossing detection method according to an exemplary embodiment.

[0024] Figure 2 The figure is a schematic diagram of a scenario showing a line crossing detection method according to an exemplary embodiment.

[0025] Figure 3 The figure is a schematic diagram of a scenario showing a line crossing detection method according to an exemplary embodiment.

[0026] Figure 4 The figure is a schematic diagram of a scenario showing a line crossing detection method according to an exemplary embodiment.

[0027] Figure 5 The figure is a schematic diagram of a scenario showing a line crossing detection method according to an exemplary embodiment.

[0028] Figure 6 The figure is a schematic diagram of a scenario showing a line crossing detection method according to an exemplary embodiment.

[0029] Figure 7 The figure is a schematic diagram of a scenario showing a line crossing detection method according to an exemplary embodiment.

[0030] Figure 8 The figure is a block diagram of a line crossing detection device according to an exemplary embodiment.

[0031] Figure 9 is a block diagram of a vehicle according to an exemplary embodiment. DETAILED DESCRIPTION

[0032] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.

[0033] Among related technologies, finish line detection is not only applicable to racing but also has broad application prospects in various scenarios, such as performance testing of intelligent driving vehicles. Traditional finish line detection relies on manual or mechanical recording of finish line times or simple time calculations, which suffer from poor accuracy and lack of real-time performance. This is especially true in high-speed racing events, where vehicles travel at extremely high speeds and the finish line moment is extremely brief. Traditional equipment struggles to accurately capture this moment, leading to increased timing errors.

[0034] With advancements in intelligent driving technology and the rapid development of vehicle sensor and data processing technologies, finish line detection methods based on vehicle driving data have become a research hotspot. By acquiring real-time vehicle driving data and utilizing advanced prediction algorithms to perform high-frequency predictions of vehicle positions, it is possible to accurately record the finish line time as the vehicle crosses the finish line, significantly improving the accuracy and real-time performance of finish line detection.

[0035] Reference Figure 1 , Figure 1 FIG. 1 is a flow chart showing a line crossing detection method according to an exemplary embodiment. Figure 1 As shown, the line crossing detection method includes the following steps.

[0036] In step S101 , the driving data of the vehicle on the track is obtained.

[0037] In step S102, a predicted position of the vehicle within the track at the next predicted moment is determined based on the driving data, wherein the interval between adjacent predicted moments is less than a set duration.

[0038] In step S103, when the predicted position exceeds the start and end lines of the track, the finish line time of the vehicle is determined based on the driving data.

[0039] For example, the vehicle can be a racing car or any other vehicle. The track is the racing track on which the vehicles compete. Driving data refers to various data collected while the vehicle is driving on the track, including speed, position, acceleration, direction, and so on. Driving data can be acquired in real time through sensors on the vehicle. These sensors may include global positioning systems, such as GPS, BeiDou, or other positioning systems, as well as inertial measurement units (IMUs), lidars, millimeter-wave radars, ultrasonic radars, and camera sensors.

[0040] For example, the vehicle's position is predicted at a certain time interval, with the prediction time interval being less than the set time interval. The set time interval can be set and adjusted according to actual needs. For example, the set time interval can be 100ms, 10ms, etc., which can control the error range to the millisecond level.

[0041] For example, the predicted position is the vehicle's position at a certain point in the future, as predicted by a preset algorithm. The accuracy of the predicted position may depend on the accuracy of the algorithm and the integrity of the data. The preset algorithm can be set according to actual needs and may, for example, be a mathematical model such as a Kalman filter or trajectory extrapolation algorithm, or a neural network model such as a recurrent neural network or a long short-term memory network. The method for determining the predicted position is not limited herein.

[0042] As an example, when the set time is short, such as 10ms, the prediction frequency is high, the predicted position does not need to be particularly accurate, and a relatively simple algorithm can be used to predict the vehicle position. When the set time is long, a more accurate algorithm can be used to predict the vehicle position.

[0043] As an example, driving data includes driving speed, acceleration, driving direction, and actual position. Based on the driving speed, acceleration, driving direction, and the actual interval duration between predictions, a predicted displacement in the driving direction within the interval duration can be determined. Based on the actual position and the predicted displacement in the driving direction, a predicted position of the vehicle after the actual interval duration can be obtained.

[0044] As an example, driving data includes driving speed, driving direction, and actual position. Based on the driving speed, driving direction, and the actual interval duration at the prediction moment, a predicted displacement in the driving direction within the interval duration can be determined. Based on the actual position and the predicted displacement in the driving direction, a predicted position of the vehicle after the actual interval duration can be obtained.

[0045] For example, the start and finish lines of a track are key reference points for vehicle crossing the finish line. The finish time is the specific time at which the vehicle passes the start and finish lines of the track. The finish time can be determined by whether the vehicle's position exceeds the start and finish lines.

[0046] The track is usually circular, with the starting and finishing lines being the same, meaning there is only one starting and finishing line. In special cases, if the track is not circular, the starting and finishing lines include both the starting and finishing lines. When a vehicle's position exceeds the starting line, the vehicle's first time is counted, and when the vehicle's position exceeds the finish line, the vehicle's second time is counted. The vehicle's race time is the duration between the first and second times.

[0047] For example, by acquiring real-time vehicle driving data on the track, the vehicle's position at the next predicted time is predicted. The interval between predictions is shorter than a set time interval, ensuring real-time prediction accuracy. When the predicted position exceeds the start and finish lines of the track, the vehicle's driving data is further analyzed to determine the vehicle's finish line time. This finish line time can then be recorded and output for use by race timing systems or vehicle performance analysis systems.

[0048] The present disclosure uses a vehicle's driving data on a track to determine the vehicle's predicted position on the track at the next predicted moment. When the predicted position exceeds the track's start and finish lines, the system determines the vehicle's finish time based on the driving data, with the interval between adjacent predicted moments being less than a set duration. This high-frequency prediction of the vehicle's position based on real-time driving data allows for accurate recording of the finish time when the vehicle is predicted to cross the finish line. This significantly improves the accuracy and real-time nature of vehicle finish time detection, effectively avoiding timing errors caused by human factors or equipment delays.

[0049] As an optional embodiment, the driving data includes the actual position, speed data, and driving direction of the vehicle, and determining the predicted position of the vehicle within the track at the next predicted time based on the driving data includes: determining a first projection position of the actual position on a reference line of the racetrack; determining a first distance between the actual position and the start and end lines according to the first projection position; A finish line time of the vehicle is determined based on the vehicle speed data, the driving direction, and the first distance.

[0050] For example, the actual position is the vehicle's real-time position on the track, which can be obtained via GPS or other positioning systems. The speed is the vehicle's real-time speed, which can be obtained via the vehicle's speed sensor or calculated from GPS data. The direction of travel is the vehicle's real-time direction of movement, which can be calculated from the vehicle's inertial measurement unit or GPS data.

[0051] For example, the vehicle speed data may be vehicle speed, or may include vehicle speed and vehicle acceleration. For example, when the vehicle is traveling at a constant speed, the vehicle's finish line time may be determined based on the vehicle speed, driving direction, and the first distance. For example, when the vehicle is accelerating, the vehicle's finish line time may be determined simultaneously based on the vehicle speed, vehicle acceleration, driving direction, and the first distance.

[0052] For example, the reference line of the track is used to assist in calculating the position and motion state of the vehicle in order to determine the relative position of the vehicle to the track. For example, the reference line of the track can be the center line of the track or a preset track trajectory line, or it can be the boundary line on the left and right sides of the track, or other reference lines that can be used to characterize the relative position of the vehicle on the track, which can be set based on actual conditions. The first projected position is the vertical projection point of the vehicle's actual position on the track reference line, which can be used to determine the relative position of the vehicle to the track. The vehicle does not drive entirely according to the shape of the track, so the position of the vehicle on the track reference line, i.e., the first projected position, can be determined based on the track reference line. The first distance is the distance between the vehicle's actual position and the start and end lines of the track, which can be used to determine the time to cross the finish line.

[0053] For example, the actual position of the vehicle is projected onto a reference line of the track to obtain a first projected position, a first distance between the actual position of the vehicle and the start and end lines of the track is determined, and the vehicle's finishing time is determined based on the vehicle's speed, driving direction and the first distance.

[0054] For example, the vehicle's direction of travel is typically consistent with the direction of travel corresponding to the track. If the vehicle's direction of travel is inconsistent with the track direction, such as when the vehicle is reversing or off the track, the finish line time may not be determined to avoid misjudgment. If the vehicle's direction of travel is consistent with the track direction, the finish line time may be determined based on the vehicle's directional angle, vehicle speed, and first distance.

[0055] As an example, it can be expressed by the calculation formula: T=L / (v×cosθ), where T is the predicted finishing time, that is, it is predicted that the vehicle will cross the finishing line after T time at the current moment; L is the first distance, v is the vehicle speed, θ can represent the angle between the vehicle's driving direction and the tangent corresponding to the first projection position, or it can represent the angle between the vehicle's driving direction and the target reference line, the target reference line is the line between the first projection position and the start and end reference points, and the start and end reference points are the intersection between the start and end lines of the track and the reference line.

[0056] Traditional methods can lead to misjudgments due to environmental interference such as lighting changes and occlusions. This method uses driving data such as the vehicle's actual position, speed, and direction to determine the finish time, reducing the impact of external factors on finish time detection and improving system robustness. Furthermore, using real-time vehicle driving data, it can dynamically adapt to changes in the vehicle's movement on the track, accurately predicting the finish time even if the vehicle's speed changes or its direction deviates from the track.

[0057] As an optional embodiment, determining a first distance between the actual position and the start and end lines according to the first projection position includes: A first distance between the actual position and the start-end lines is determined according to the first projection position and an intersection point between the start-end lines and the reference line.

[0058] For example, the intersection of the start-end line and the reference line is the position of the start-end line on the reference line. The distance between the first projected position and the intersection can be used as the first distance between the actual position and the start-end line. If the first projected position is before the intersection, meaning the vehicle has not yet reached the start-end line, the first distance is the distance between the intersection and the first projected position. If the first projected position is after the intersection, meaning the vehicle has already crossed the start-end line, the first distance is a negative value, indicating the vehicle has crossed the finish line.

[0059] Based on this, the present disclosure performs distance calculations by using the projection position and the intersection of the start and end lines with the reference line, which can simplify the complex three-dimensional space into two-dimensional geometry, thereby improving calculation efficiency and the real-time performance of determining the finish line time.

[0060] As an optional embodiment, determining a first projection position of the actual position on a reference line of the track includes: Determine a track segment to be matched on the reference line corresponding to the actual position; The first projected position is determined according to a projected distance between the actual position and the track segment to be matched.

[0061] For example, the track segment to be matched is a track segment on the reference line of the track that closely matches the actual position of the vehicle. The target track segment is a track segment that best matches the vehicle, wherein the first projection position is on the target track segment.

[0062] For example, if there are a large number of curves in a track, a track segment can be divided into multiple track segments, and the actual position can be projected onto each track segment to be matched. By determining the target track segment, the first projection position of the actual position on the reference line can be determined.

[0063] For example, the actual position of the vehicle can be used to determine the track segments to be matched that correspond to the actual position on reference line segments within a preset range around the vehicle. For example, all reference line segments within a 5-meter distance from the vehicle can be determined as track segments to be matched.

[0064] In some specific examples, the track segment to be matched that has the shortest distance to the actual position can be determined as the target track segment. Figure 2As shown, a reference line 200 on a track within a preset range from vehicle 100 can be divided into three continuous segments, each of which is identified as a track segment to be matched. The distance between vehicle 100 and the first segment is s1, the distance between vehicle 100 and the second segment is s2, and the distance between vehicle 100 and the third segment is s3. Since s1 is the shortest, the first segment can be identified as the target track segment, and the projection of vehicle 100 onto the first segment is the first projection position of the vehicle on the reference line.

[0065] As an optional embodiment, determining a first projection position of the actual position on a reference line of the track includes: Determine a track segment to be matched on the reference line corresponding to the actual position; determining a target track segment among the track segments to be matched based on a positional relationship among the start and end lines, the actual position, the predicted position of the vehicle at the next predicted moment, and the track segments to be matched; The first projection position is determined according to the actual position and the target track segment.

[0066] For example, there may be track segments to be matched in front of the vehicle and on both the left and right sides in the direction of travel. In this case, determining the target track segment based solely on the distance between the vehicle and the track segment to be matched is inaccurate.

[0067] In other specific examples, the target track segment can be determined in the track segments to be matched based on the positional relationship between the start and end lines, the actual position, the predicted position and the track segment to be matched, and the first projection position can be determined based on the actual position and the target track segment.

[0068] Specifically, determining a target track segment in the track segments to be matched based on a positional relationship among the start and end lines, the actual position, the predicted position of the vehicle at the next predicted moment, and the track segments to be matched includes: Determining a second distance between the point to be matched and the actual position, a third distance between the start and end reference points and the point to be matched, a fourth distance between the actual position and the predicted position, a fifth distance between the predicted position and the point to be matched, a first tangent angle difference between the start and end reference points and the point to be matched, and a second tangent angle difference between the actual position and the point to be matched, wherein the start and end reference points are intersections of the start and end lines and the reference line, and the point to be matched is a second projection position of the actual position on the track segment to be matched; determining a degree of matching between the to-be-matched track segment and the actual position according to the second distance, the third distance, the fourth distance, the fifth distance, the first tangent angle difference, and the second tangent angle difference; A target track segment matching the actual position among the track segments to be matched is determined according to the matching degrees of the track segments to be matched.

[0069] For example, the start and end reference points are the intersections of the start and end lines of the track and the reference line. The to-be-matched point is the second projection of the vehicle's actual position on the track segment to be matched, and is used to calculate the geometric relationship between the vehicle and the track segment. Based on the positional relationship between the start and end lines, the actual position, the predicted position, and the track segment to be matched, the projection point of the vehicle's actual position on the track reference line can be more accurately determined, thereby more precisely determining the first distance between the vehicle's actual position and the start and end lines.

[0070] For example, the second distance is the distance between the actual position of the vehicle and the reference line. Figure 3 As shown, the second distance between the actual position of vehicle 100 and the reference line 200 of the track is D1. The third distance is the distance between the starting and ending reference points and the point to be matched, which can be used to represent the relative position of the point to be matched and the starting and ending lines. The fourth distance is the distance between the actual position of the vehicle and the predicted position of the vehicle. The fifth distance is the relative position between the predicted position of the vehicle and the point to be matched.

[0071] For example, the first tangent angle difference is the tangent angle difference between the starting and ending reference points and the point to be matched, which can represent the directional relationship between the point to be matched and the starting and ending lines. The second tangent angle difference is the tangent angle difference between the actual position of the vehicle and the point to be matched, which can represent the directional relationship between the vehicle's current direction of movement and the track segment.

[0072] For example, based on the relative positional relationship between the vehicle's actual position, predicted position, start and end lines, and reference lines, including the distance relationship and the direction relationship, a comprehensive evaluation is performed on the matching degree between the track segment to be matched and the vehicle's actual position. Based on the evaluation result of the matching degree, a target track segment that best matches the vehicle's actual position is selected from the track segments to be matched.

[0073] As a specific example, the degree of match between the vehicle's actual position and the track segment to be matched can be calculated using the following weighting model: S = D × d / (D + H + P) + H × h / (D + H + P) + P × p / (D + H + P). D is the second distance between the vehicle's actual position and the point to be matched; d is the third distance between the starting and ending reference points and the point to be matched; H is the second tangent angle difference between the actual position and the point to be matched; P is the fifth distance between the predicted position and the point to be matched; h is the first tangent angle difference between the starting and ending reference points and the point to be matched; and p is the fourth distance between the actual position and the predicted position.

[0074] Specifically, within the distance factor, a smaller distance indicates a higher degree of match between the vehicle and the track segment. For example, smaller values ​​for D and P indicate that both the vehicle's current and predicted positions are close to the track segment. Within the direction factor, a smaller tangent angle difference indicates a higher degree of consistency between the vehicle's direction of motion and the track segment's direction. For example, a smaller value for H indicates that the vehicle's current direction of motion is consistent with the track segment's direction of motion.

[0075] Based on this, by normalizing the distance factor and the direction factor and determining the weight of each factor in the comprehensive factors, it is ensured that the weight of each factor is relatively fair, avoiding the excessive impact of a single factor's excessive value on the matching degree, and being able to comprehensively evaluate the matching degree between the vehicle and the track segment. Therefore, the evaluation method disclosed in the present invention is more reliable than a single-factor evaluation and can adapt to complex track environments and vehicle motion states.

[0076] As an optional embodiment, when the predicted position exceeds the start and end lines of the track, before determining the vehicle's finish line time based on the driving data, the method further includes: determining a third projection position of the predicted position on a reference line of the track; Whether the predicted position exceeds the start and end lines of the track is determined based on the third projection position and the start and end reference points, where the start and end reference points are intersections of the start and end lines and the reference line.

[0077] For example, Figure 7 As shown, third projected position 120 is the projection point of the predicted position of vehicle 100 on reference line 200 of the track, and can be used to represent the relative relationship between the predicted position and reference line 200 of the track. Third projected position 120 of the predicted position of vehicle 100 on the reference line of the track can be determined by determining first projected position 110 of the actual position of vehicle 100 on reference line 200 of the track as described above.

[0078] For example, the line segment to be matched corresponding to the predicted position on the reference line can be determined, and the target line segment can be determined in the line segment to be matched based on the positional relationship between the start and end lines, the actual position, the predicted position and the line segment to be matched, and the third projection position can be determined based on the predicted position and the target line segment.

[0079] For example, the projection distance between the predicted position and multiple to-be-matched line segments can be determined, and the to-be-matched line segment with the smallest projection distance among the multiple to-be-matched line segments can be determined as the target line segment, and the third projection position can be determined based on the projection point of the predicted position projected onto the target line segment.

[0080] For example, before determining whether a vehicle has crossed the finish line, the predicted position can be projected onto a reference line on the track to obtain a third projected position. Based on the positional relationship between the third projected position and the start and end reference points, a determination is made as to whether the predicted position has crossed the finish line. For example, if the third projected position is ahead of the start and end reference points, the vehicle is considered to be about to cross the finish line or has already crossed it. If the third projected position is behind the start and end reference points, the vehicle is considered to have not yet crossed the finish line.

[0081] It is understood that the track direction may be fixed, or the track direction corresponding to the track may be pre-determined based on the track data corresponding to the track. Therefore, the third projection position may be determined to be before or after the start and end reference points based on the track direction and the relative positions of the third projection point and the start and end reference points.

[0082] Based on this, by projecting the vehicle's predicted position onto the reference line of the track and combining it with the start and end reference points of the track to determine whether the vehicle has crossed the finish line, the relative position relationship between the vehicle and the start and end lines can be determined more accurately, avoiding misjudgments caused by the vehicle's high speed or drastic position changes, and improving the accuracy of the finish line judgment.

[0083] As an optional embodiment, determining the predicted position of the vehicle within the track at the next predicted moment based on the driving data includes: When the vehicle is in the finish line buffer area of ​​the track, the predicted position of the vehicle in the track at the next predicted moment is determined based on the driving data, wherein the distance between the first boundary of the finish line buffer area and the start and end lines is the sixth distance, and the second boundary of the finish line buffer area is the start and end lines.

[0084] For example, the finish line buffer area is an area on the track near the start and finish lines. When the vehicle is in the finish line buffer area, it can be determined that the vehicle is about to cross the finish line. The first boundary of the finish line buffer area is at a sixth distance from the start and finish lines, and the second boundary of the finish line buffer area is the start and finish lines.

[0085] For example, when a vehicle enters the finish line buffer area, the vehicle's actual position is between the first boundary and the second boundary. The vehicle can perform finish line detection at a higher frequency. Through the real-time acquisition of vehicle driving data, the predicted position of the vehicle is judged at each prediction moment, and when it is determined that the predicted position exceeds the start and end lines, the vehicle's finish line time is determined based on the vehicle's actual position, speed and driving direction.

[0086] like Figure 4 As shown, the distance between the first boundary 230 and the second boundary 220 of the finish line buffer area of ​​the track is D2, i.e., the sixth distance. When the actual position of vehicle 100 is between first boundary 230 and second boundary 220, it can be determined that vehicle 100 is in the finish line buffer area. When vehicle 100 crosses second boundary 220, it indicates that vehicle 100 has completed the finish line.

[0087] Based on this, a finish line buffer zone is set up to determine whether a vehicle is about to cross the finish line. When the vehicle is in the finish line buffer zone, the predicted position on the track at the next predicted moment is calculated more frequently. When the vehicle has not entered the finish line buffer zone, the predicted position calculation is not performed, saving significant computing resources. Furthermore, the predicted position is calculated after the vehicle enters the finish line buffer zone, providing a specific finish line detection range for the vehicle, preventing misjudgments of the finish line status due to the vehicle's movement elsewhere on the track.

[0088] As an optional embodiment, when the vehicle is in the finish buffer area of ​​the track, before determining the predicted position of the vehicle within the track at the next predicted time based on the driving data, the method further includes: Determine whether the vehicle is in a finish line buffer area of ​​the track based on a first projection position of the actual position of the vehicle on a reference line of the track, and start and end reference points, where the start and end reference points are intersections of the start and end lines with the reference line.

[0089] For example, a target track segment can be determined among the track segments to be matched based on the positional relationship between the start and end lines, the actual position, the predicted position, and the track segment to be matched; and a first projection position of the vehicle's actual position on the track reference line can be determined based on the actual position and the target track segment.

[0090] Specifically, please refer to Figure 4Based on the actual position of the vehicle 100, a first projection position 110 of the actual position of the vehicle 100 on the reference line 200 of the track is determined. Furthermore, based on the positions of the start-end reference points 210 on the second boundary 220, a determination is made as to whether the actual position of the vehicle 100 is within the finish line buffer area. When the distance between the first projection position 110 of the vehicle 100 and the start-end reference points 210 is less than a sixth distance D2, it can be determined that the actual position of the vehicle 100 is between the first boundary 230 and the second boundary 220, i.e., the vehicle is within the finish line buffer area of ​​the track.

[0091] As an optional embodiment, the driving data includes an actual position of the vehicle within the track, and obtaining the driving data of the vehicle within the track includes: Obtaining positioning information of the vehicle; acquiring track data of a track within a set range around the vehicle according to the positioning information; The positioning information of the vehicle is associated with the track data of the track to obtain the actual position of the vehicle within the track.

[0092] For example, vehicle positioning information can be used to determine the vehicle's position, further determining the vehicle's actual location within the track. Positioning information can be obtained using a GPS positioning system or other positioning technology. Track data includes track geometry information, including track shape, boundaries, reference lines, start and end lines, track direction, track position, etc. Track data can be included in map data.

[0093] For example, the set range can be set based on actual needs. For example, it can be set to a range of 500 meters around the vehicle, etc. When a track exists within 500 meters of the vehicle's location, track data corresponding to that track can be obtained. The distance between the vehicle and the track can be the distance between the track's location point on the map data and the vehicle's location point, or the distance between the closest location point on the track to the vehicle and the vehicle's location point, without limitation.

[0094] For example, after obtaining track data, the vehicle's positioning information can be correlated with the track data to determine the vehicle's actual position within the track. The vehicle's positioning information may include longitude and latitude coordinates, which can be converted to a position in the track coordinate system through vehicle coordinate transformation to obtain the vehicle's actual position within the track.

[0095] As an optional embodiment, associating the positioning information of the vehicle with the track data of the track to obtain the actual position of the vehicle within the track includes: Determining grid data corresponding to the track data, the grid data including a first mapping relationship between track segments of the track and grids; performing coordinate conversion on the positioning information of the vehicle according to the grid data to obtain a second mapping relationship between the position of the vehicle and the grid; The actual position of the vehicle within the track is obtained according to the first mapping relationship and the second mapping relationship.

[0096] For example, a track can be divided into multiple grids, each of which contains a first mapping relationship between track segments and the grid. The first mapping relationship is a mapping relationship between track segments and grids, which can be used to determine the grid to which each track segment belongs. The second mapping relationship is a mapping relationship between the vehicle's position and the grid, which can be used to determine the grid in which the vehicle is located.

[0097] For example, a second mapping relationship can be obtained by performing coordinate conversion on the vehicle's positioning information based on the grid data. The vehicle can obtain longitude and latitude coordinates during driving via its GPS module, and these longitude and latitude coordinates are spherical coordinates. The longitude and latitude coordinates of the vehicle in the lower left corner of the grid area in the grid data corresponding to the track can be used as a reference point. Using a coordinate conversion method such as Mercator projection, the longitude and latitude coordinates of the vehicle can be converted into planar coordinates within the grid to obtain the second mapping relationship.

[0098] For example, based on the establishment of the first and second mapping relationships, the actual position of a vehicle within a track can be quickly determined. The grid in which the vehicle is located can be determined based on the vehicle's position and the second mapping relationship, and the track segment corresponding to the grid can be determined based on the grid and the first mapping relationship. This allows the vehicle to be determined to be near the track segment.

[0099] For example, refer to Figure 5 , the track data can be gridded. The width and height of each grid can be set to preset values, forming a grid with boundaries. The preset value can be set based on actual search requirements. In one example, the preset value can be set to an empirical value with the highest search efficiency, such as 20m.

[0100] For example, Figure 5 As shown, all track segments on the track can be traversed, and the grid number of the track segment can be determined based on the segment boundary. All track segments on the track are mapped and associated with the corresponding grids to obtain a first mapping relationship. Coordinate conversion is performed on the vehicle's positioning information to obtain a second mapping relationship between the vehicle's position and the grid. Traversing the grids can determine the relationship between the vehicle and the grid, and further determine the positional relationship between the vehicle and the track.

[0101] Based on this, by gridding the track data and determining the first mapping relationship and the second mapping relationship based on the grid data, the grid where the vehicle is located can be determined through the vehicle's positioning information, the first mapping relationship and the second mapping relationship, and by retrieving the boundary of the grid, the track segment where the vehicle is located can be quickly determined without having to determine the positional relationship between the vehicle and each point on the track, thereby improving the efficiency of position calculation.

[0102] As an optional embodiment, obtaining track data of a track within a set range around the vehicle according to the positioning information includes: determining, based on the positioning information, whether the positioning position of the vehicle is within a track-associated area corresponding to the track, the track-associated area being obtained by expanding the set distance by a grid boundary of grid data corresponding to the track, the grid data including a first mapping relationship between track segments and grids of the track; When the positioning position of the vehicle is within the track associated area corresponding to the track, track data of the track is acquired.

[0103] For example, a set distance can be used to determine whether a vehicle is close to a track. The set distance value can be set based on actual conditions. The track-associated area can be determined based on the grid boundaries of the grid data corresponding to the vehicle on the track. The grid boundaries of the grid data corresponding to the vehicle track are expanded by a set range to obtain the track-associated area corresponding to the track.

[0104] For example, based on the vehicle positioning information and the track-associated area corresponding to the track, it is determined whether the vehicle's positioning position is within the track-associated area. When the vehicle's positioning position is within the track-associated area, it is determined that the track is a track within a set range around the vehicle.

[0105] For example, Figure 6 As shown, when the grids occupied by the reference line 200 of the track include grid data corresponding to numbers 1 to 16, the track associated area can be determined by expanding the grid boundaries corresponding to the grid data by a set distance using the grid data corresponding to numbers 1 to 16, that is, Figure 6 The area within the outermost frame line is defined as follows. For example, the four boundaries outside the grid areas corresponding to grids 1 through 16 are expanded by a set distance D3 to obtain a track-associated area. The track-associated area may or may not include the grid areas corresponding to grids 1 through 16. When vehicle 100 is within the track-associated area, meaning the distance between the vehicle and the track is less than the set distance D3, the vehicle's positioning information is then associated with the track data.

[0106] Based on this, by setting the distance to trigger the association of the vehicle's positioning information with the track data, unnecessary calculations for vehicles far away from the track can be avoided, thereby saving computing resources and improving overall efficiency.

[0107] As a specific embodiment, refer to Figure 2-7 , the method for detecting a finish line disclosed in the present invention is described with an example, wherein the map data includes track data.

[0108] Specifically, when the vehicle 100 is traveling, the vehicle 100 can automatically search for a track within a set range. When the vehicle 100 obtains track data corresponding to the track, the track data can be gridded and associated based on empirical values.

[0109] For example, the track data can be divided into multiple rectangles with a width and height of 20m. Then, all track segments on the track can be traversed, and the grid numbers of the segments can be determined according to the boundaries of the track segments and mapped and associated to obtain grid data. The grid data includes the first mapping relationship between the track segments and the corresponding grids. Figure 5 As shown, the grids in the grid data can be numbered from 1 to 16.

[0110] Specifically, when the distance between vehicle 100 and the track is less than a set distance D3, the positioning information of vehicle 100 is associated with the track's grid data, and the vehicle's 100 identifier is displayed on the track's reference line 200. The identifier on reference line 200 is the projection of the vehicle's 100 actual position onto reference line 200. Reference line 200 can be the trackline or centerline of the track.

[0111] For example, the set distance D3 can be added to the four boundaries of the above grid data to obtain the following Figure 6 The track-related area is shown in FIG. Wherein, the distance of the track-related area in the X direction is x0, and the distance of the track-related area in the Y direction is y0, then the boundary coordinates of the track-related area can be expressed as (x0, y0).

[0112] For example, while the vehicle 100 is traveling, the longitude and latitude can be obtained in real time through the GPS module of the vehicle 100, and the coordinates of the position of the vehicle 100 can be converted. The longitude and latitude coordinates of the lower left corner of the track-related area can be used as the reference point (0, 0), and the longitude and latitude coordinates of the vehicle 100 can be converted into the position coordinates (x1, y1) of the vehicle 100 within the track-related area using a Mercator projection.

[0113] Exemplarily, when the vehicle 100 moves, the grid boundaries corresponding to the track associated area can be retrieved. When the position of the vehicle 100 is within the track associated area and the distance between the vehicle 100 and the track is less than the set distance D3, the grid data corresponding to the vehicle 100 and the track is associated. For example, when the position coordinates (x1, y1) of the vehicle 100 satisfy the condition that x1 < x0 and y1 < y0, the position of the vehicle 100 is within the track associated area; when x1 > x0 or y1 > y0, the position of the vehicle 100 is not within the track associated area.

[0114] Exemplarily, as Figure 2 shown, the track is a curve composed of multiple points, and a section of the track can be split into multiple straight lines. Therefore, based on the multiple straight lines, the first projection position 110 of the vehicle 100 on the track is determined. Among them, according to the positioning information of the vehicle 100, the grid data can be retrieved to determine the number of one or more grids closest to the position of the vehicle 100, or determine the number of grids within a distance of 10 m from the vehicle 100; and according to the grid number, find all the line segments corresponding in the grid; then, project all the line segments in the grid respectively to determine the projection distances s1, s2, s3..... between the actual position of the vehicle 100 and the line segments; finally, determine the target line segment that best matches the actual position of the vehicle 100, and determine the first projection position 110 of the actual position of the vehicle 100 on the target line segment as the projection point of the vehicle 100 on the track.

[0115] For example, based on the projection distance between the actual position of the vehicle 100 and the line segment, the line segment with the smallest projection distance can be determined as the target line segment, and the intersection point between the target line segment and the track reference line 200 is the projection point of the vehicle 100 on the track.

[0116] For example, the matching degree between the actual position of the vehicle 100 and the track line segment to be matched can be calculated through the following weight model: S = D×d / (D + H + P) + H×h / (D + H + P) + P×p / (D + H + P). Among them, D is the second distance between the actual position of the vehicle 100 and the point to be matched; d is the third distance between the start and end reference points and the point to be matched; H is the difference in the second tangent angle between the actual position and the point to be matched; P is the fifth distance between the predicted position and the point to be matched; h is the difference in the first tangent angle between the start and end reference points and the point to be matched; p is the fourth distance between the actual position and the predicted position.

[0117] Specifically, when the distance between the projection of the vehicle's actual position and the start / finish line is less than a set distance D3, meaning the vehicle is within the finish line buffer zone, the system can predict the next projected position of the vehicle on the track—the third projected position 120 of the predicted vehicle position—at a frequency of 10ms to detect the vehicle's finish line crossing. When the vehicle's finish line crossing is detected or predicted, the actual finish line crossing time is recorded.

[0118] For example, when the direction of movement of the vehicle 100 is the same as the direction of the track, the projection point of the vehicle 100 on the track is detected every 10ms. If the projection point has crossed the start-end line, the crossing time of the vehicle 100 is determined and recorded; if the projection point has not crossed the start-end line, the next projection position of the vehicle 100 on the reference line 200 of the track is predicted. When the predicted next projection position crosses the start-end line, the crossing time is predicted by T=L / (v×cosθ), where T is the predicted crossing time, that is, it is predicted that the vehicle 100 will cross the finish line after T time at the current moment; L is the first distance, v is the vehicle speed, and θ can represent the angle between the driving direction of the vehicle 100 and the tangent corresponding to the first projection position 110, or it can represent the angle between the driving direction of the vehicle 100 and the target reference line, the target reference line is the line between the first projection position 110 and the start-end reference points, and the start-end reference points are the intersections between the start-end lines of the track and the reference line 200. If the direction of movement of the vehicle 100 is different from the direction of the track, it is considered as a reversing scene, and the vehicle 100 will not be detected for crossing the finish line.

[0119] Based on this, the present invention uses the vehicle's real-time driving data to make high-frequency predictions of the vehicle's position, and can accurately record the finishing time when the vehicle is predicted to cross the finish line, significantly improving the accuracy and real-time performance of the vehicle's finishing time detection, and effectively avoiding timing errors caused by human factors or equipment delays.

[0120] Reference Figure 8 , Figure 8 FIG. 8 is a block diagram of a wire crossing detection device 800 according to an exemplary embodiment. Figure 8 As shown, the line crossing detection device 800 includes an acquisition module 801 , a position prediction module 802 and a time prediction module 803 .

[0121] An acquisition module 801 is configured to acquire driving data of a vehicle on a track; a position prediction module 802 configured to determine a predicted position of the vehicle within the track at a next predicted moment based on the driving data, wherein an interval between adjacent predicted moments is less than a set time duration; The time prediction module 803 is configured to determine the vehicle's finishing time based on the driving data when the predicted position exceeds the start and end lines of the track.

[0122] As an optional embodiment, the driving data includes the actual position, speed data and driving direction of the vehicle, and the position prediction module 802 is configured to: determining a first projection position of the actual position on a reference line of the racetrack; determining a first distance between the actual position and the start and end lines according to the first projection position; A finish line time of the vehicle is determined based on the vehicle speed data, the driving direction, and the first distance.

[0123] As an optional embodiment, the location prediction module 802 is configured to: A first distance between the actual position and the start-end lines is determined according to the first projection position and an intersection point between the start-end lines and the reference line.

[0124] As an optional embodiment, the location prediction module 802 is configured to: Determine a track segment to be matched on the reference line corresponding to the actual position; The first projected position is determined according to a projected distance between the actual position and the track segment to be matched.

[0125] As an optional embodiment, the location prediction module 802 is configured to: Determine a track segment to be matched on the reference line corresponding to the actual position; determining a target track segment among the track segments to be matched based on a positional relationship among the start and end lines, the actual position, the predicted position of the vehicle at the next predicted moment, and the track segments to be matched; The first projection position is determined according to the actual position and the target track segment.

[0126] As an optional embodiment, the location prediction module 802 is configured to: Determining a second distance between the point to be matched and the actual position, a third distance between the start and end reference points and the point to be matched, a fourth distance between the actual position and the predicted position, a fifth distance between the predicted position and the point to be matched, a first tangent angle difference between the start and end reference points and the point to be matched, and a second tangent angle difference between the actual position and the point to be matched, wherein the start and end reference points are intersections of the start and end lines and the reference line, and the point to be matched is a second projection position of the actual position on the track segment to be matched; determining a degree of matching between the to-be-matched track segment and the actual position according to the second distance, the third distance, the fourth distance, the fifth distance, the first tangent angle difference, and the second tangent angle difference; A target track segment matching the actual position among the track segments to be matched is determined according to the matching degrees of the track segments to be matched.

[0127] As an optional embodiment, the cross-line detection device 800 is further configured to: determining a third projection position of the predicted position on a reference line of the track; Whether the predicted position exceeds the start and end lines of the track is determined based on the third projection position and the start and end reference points, where the start and end reference points are intersections of the start and end lines and the reference line.

[0128] As an optional embodiment, the location prediction module 802 is configured to: When the vehicle is in the finish line buffer area of ​​the track, the predicted position of the vehicle in the track at the next predicted moment is determined based on the driving data, wherein the distance between the first boundary of the finish line buffer area and the start and end lines is the sixth distance, and the second boundary of the finish line buffer area is the start and end lines.

[0129] As an optional embodiment, the cross-line detection device 800 is further configured to: Determine whether the vehicle is in a finish line buffer area of ​​the track based on a first projection position of the actual position of the vehicle on a reference line of the track, and start and end reference points, where the start and end reference points are intersections of the start and end lines with the reference line.

[0130] As an optional embodiment, the driving data includes the actual position of the vehicle within the track, and the acquisition module 801 is further configured to: Obtaining positioning information of the vehicle; acquiring track data of a track within a set range around the vehicle according to the positioning information; The positioning information of the vehicle is associated with the track data of the track to obtain the actual position of the vehicle within the track.

[0131] As an optional embodiment, the acquisition module 801 is configured to: Determining grid data corresponding to the track data, the grid data including a first mapping relationship between track segments of the track and grids; performing coordinate conversion on the positioning information of the vehicle according to the grid data to obtain a second mapping relationship between the position of the vehicle and the grid; The actual position of the vehicle within the track is obtained according to the first mapping relationship and the second mapping relationship.

[0132] As an optional embodiment, the obtaining module 801 is further configured to: determining, based on the positioning information, whether the positioning position of the vehicle is within a track-associated area corresponding to the track, the track-associated area being obtained by expanding the set distance by a grid boundary of grid data corresponding to the track, the grid data including a first mapping relationship between track segments and grids of the track; When the positioning position of the vehicle is within the track associated area corresponding to the track, track data of the track is acquired.

[0133] Regarding the cross-line detection device 800 in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the cross-line detection method, and will not be elaborated here.

[0134] Based on the same inventive concept, the present disclosure further provides a computer-readable storage medium having computer program instructions stored thereon. When the program instructions are executed by a processor, the steps of the line cross detection method provided by the present disclosure are implemented.

[0135] Based on the same inventive concept, the present disclosure further provides a vehicle, comprising: a storage device for storing a computer program; An execution device is used to execute the computer program to implement the line crossing detection method described in the present disclosure.

[0136] In another exemplary embodiment, a computer program product is further provided. The computer program product includes a computer program executable by a programmable device, and the computer program has a code portion for executing the above-mentioned line cross detection method when executed by the programmable device.

[0137] Figure 9 FIG1 is a block diagram illustrating a vehicle 900 according to an exemplary embodiment. For example, vehicle 900 may be a hybrid vehicle, a non-hybrid vehicle, an electric vehicle, a fuel cell vehicle, or another type of vehicle. Vehicle 900 may be an autonomous vehicle or a semi-autonomous vehicle.

[0138] Reference Figure 9Vehicle 900 may include various subsystems, such as an infotainment system 910, a perception system 920, a decision-making and control system 930, a drive system 940, and a computing platform 950. Vehicle 900 may also include more or fewer subsystems, and each subsystem may include multiple components. Furthermore, each subsystem and each component of vehicle 900 may be interconnected via wired or wireless means.

[0139] In some embodiments, the infotainment system 910 may include a communication system, an entertainment system, a navigation system, and the like.

[0140] Perception system 920 may include several sensors for sensing information about the environment surrounding vehicle 900. For example, perception system 920 may include a global positioning system (which may be a GPS system, a BeiDou system, or another positioning system), an inertial measurement unit (IMU), a laser radar, a millimeter-wave radar, an ultrasonic radar, and a camera.

[0141] The decision control system 930 may include a computing system, a vehicle controller, a steering system, a throttle, and a braking system.

[0142] The drive system 940 may include components that provide power to the vehicle 900. In one embodiment, the drive system 940 may include an engine, a power source, a transmission system, and wheels. The engine may be an internal combustion engine, an electric motor, an air compression engine, or a combination thereof. The engine is capable of converting energy provided by the power source into mechanical energy.

[0143] Some or all functions of the vehicle 900 are controlled by a computing platform 950. The computing platform 950 may include at least one processor 951 and a memory 952. The processor 951 may execute instructions 953 stored in the memory 952.

[0144] The processor 951 can be any conventional processor, such as a commercially available CPU. The processor can also include a graphics processor (GPU), a field programmable gate array (FPGA), a system on chip (SOC), an application specific integrated circuit (ASIC), or a combination thereof.

[0145] The memory 952 can be implemented by any type of volatile or nonvolatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0146] In addition to instructions 953 , memory 952 may also store data, such as road maps, route information, and vehicle location, direction, speed, etc. The data stored in memory 952 may be used by computing platform 950 .

[0147] In the embodiment of the present disclosure, the processor 951 may execute the instruction 953 to complete all or part of the steps of the above-mentioned line crossing detection method.

[0148] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the present disclosure. This disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the following claims.

[0149] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A line crossing detection method, characterized in that: include: Obtaining the vehicle's driving data on the track; determining a predicted position of the vehicle within the track at a next predicted moment based on the driving data, wherein an interval between adjacent predicted moments is less than a set time duration; When the predicted position exceeds the start and end lines of the track, a finish line time of the vehicle is determined based on the driving data.

2. The method according to claim 1, characterized in that The driving data includes the actual position, speed data, and driving direction of the vehicle, and determining the predicted position of the vehicle within the track at the next predicted moment based on the driving data includes: determining a first projection position of the actual position on a reference line of the racetrack; determining a first distance between the actual position and the start and end lines according to the first projection position; A finish line time of the vehicle is determined based on the vehicle speed data, the driving direction, and the first distance.

3. The method according to claim 2, characterized in that Determining a first distance between the actual position and the start and end lines according to the first projection position includes: A first distance between the actual position and the start-end lines is determined according to the first projection position and an intersection point between the start-end lines and the reference line.

4. The method according to claim 2, characterized in that Determining a first projection position of the actual position on a reference line of the track includes: Determine a track segment to be matched on the reference line corresponding to the actual position; The first projected position is determined according to a projected distance between the actual position and the track segment to be matched.

5. The method according to claim 2, characterized in that Determining a first projection position of the actual position on a reference line of the track includes: Determine a track segment to be matched on the reference line corresponding to the actual position; determining a target track segment among the track segments to be matched based on a positional relationship among the start and end lines, the actual position, the predicted position of the vehicle at the next predicted moment, and the track segments to be matched; The first projection position is determined according to the actual position and the target track segment.

6. The method according to claim 5, characterized in that The determining of a target track segment in the track segments to be matched based on a positional relationship among the start and end lines, the actual position, the predicted position of the vehicle at the next predicted moment, and the track segments to be matched includes: Determining a second distance between the point to be matched and the actual position, a third distance between the start and end reference points and the point to be matched, a fourth distance between the actual position and the predicted position, a fifth distance between the predicted position and the point to be matched, a first tangent angle difference between the start and end reference points and the point to be matched, and a second tangent angle difference between the actual position and the point to be matched, wherein the start and end reference points are intersections of the start and end lines and the reference line, and the point to be matched is a second projection position of the actual position on the track segment to be matched; determining a degree of matching between the to-be-matched track segment and the actual position according to the second distance, the third distance, the fourth distance, the fifth distance, the first tangent angle difference, and the second tangent angle difference; A target track segment matching the actual position among the track segments to be matched is determined according to the matching degrees of the track segments to be matched.

7. The method according to claim 1, characterized in that When the predicted position exceeds the start and end lines of the track, before determining the vehicle's finish line time based on the driving data, the method further includes: determining a third projection position of the predicted position on a reference line of the track; Whether the predicted position exceeds the start and end lines of the track is determined based on the third projection position and the start and end reference points, where the start and end reference points are intersections of the start and end lines and the reference line.

8. The method according to any one of claims 1 to 7, characterized in that: Determining a predicted position of the vehicle within the track at a next predicted moment based on the driving data includes: When the vehicle is in the finish line buffer area of ​​the track, the predicted position of the vehicle in the track at the next predicted moment is determined based on the driving data, wherein the distance between the first boundary of the finish line buffer area and the start and end lines is the sixth distance, and the second boundary of the finish line buffer area is the start and end lines.

9. The method according to claim 8, characterized in that Before determining the predicted position of the vehicle within the track at the next predicted moment based on the driving data when the vehicle is in the finish line buffer area of ​​the track, the method further includes: Determine whether the vehicle is in a finish line buffer area of ​​the track based on a first projection position of the actual position of the vehicle on a reference line of the track, and start and end reference points, where the start and end reference points are intersections of the start and end lines with the reference line.

10. The method according to any one of claims 1 to 7, characterized in that: The driving data includes the actual position of the vehicle on the track, and obtaining the driving data of the vehicle on the track includes: Obtaining positioning information of the vehicle; acquiring track data of a track within a set range around the vehicle according to the positioning information; The positioning information of the vehicle is associated with the track data of the track to obtain the actual position of the vehicle within the track.

11. The method according to claim 10, characterized in that Associating the positioning information of the vehicle with the track data of the track to obtain the actual position of the vehicle within the track, including: Determining grid data corresponding to the track data, the grid data including a first mapping relationship between track segments of the track and grids; performing coordinate conversion on the positioning information of the vehicle according to the grid data to obtain a second mapping relationship between the position of the vehicle and the grid; The actual position of the vehicle within the track is obtained according to the first mapping relationship and the second mapping relationship.

12. The method according to claim 10, characterized in that The acquiring, based on the positioning information, track data of a track within a set range around the vehicle includes: determining, based on the positioning information, whether the positioning position of the vehicle is within a track-associated area corresponding to the track, the track-associated area being obtained by expanding the set distance by a grid boundary of grid data corresponding to the track, the grid data including a first mapping relationship between track segments and grids of the track; When the positioning position of the vehicle is within the track associated area corresponding to the track, track data of the track is acquired.

13. A line crossing detection device, characterized in that: include: an acquisition module, configured to acquire driving data of the vehicle on the track; a position prediction module configured to determine a predicted position of the vehicle within the track at a next predicted moment based on the driving data, wherein an interval between adjacent predicted moments is less than a set time duration; The time prediction module is configured to determine the vehicle's finishing time based on the driving data when the predicted position exceeds the start and end lines of the track.

14. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the program instructions are executed by a processor, the line crossing detection method described in any one of claims 1 to 12 is implemented.

15. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the line cross detection method according to any one of claims 1 to 12 are implemented.

16. A vehicle, characterized in that: include: a storage device for storing a computer program; An execution device is used to execute the computer program to implement the line crossing detection method described in any one of claims 1-12.

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