Method, device and medium for scoring a side slip angle term during vehicle drift

Abstract

The application discloses a kind of vehicle drift process in side slip angle item scoring method, equipment and medium, comprising: collection side slip angle data and segmentation, with commutating point as segmentation point to eliminate the influence of large angle change on scoring;Adopt double threshold window to detect extreme point and dynamic correction, avoid repeated counting;Based on side slip angle average value, trim extreme value sequence, improve analysis accuracy;Calculate adjacent extreme value angle difference and mean deviation, combined with segmented linear deduction rule, realize the quantitative scoring of stability and mean deviation.The application solves the error problem caused by commutation mutation, extreme value redundancy and static threshold in traditional drift scoring, and can be widely applied in racing training, automatic driving test and other fields.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of driving behavior evaluation, and particularly relates to a scoring method for a side slip angle term in a vehicle drifting process, a device and a medium. BACKGROUND

[0002] In the vehicle drifting process, the side slip angle is one of the important parameters for measuring the vehicle handling performance and driving skills. Drifting is an advanced driving skill, which realizes the side sliding driving of the vehicle by losing the grip of the rear wheels and keeping the front wheels turning. In this process, the side slip angle of the vehicle, i.e. the angle between the driving direction of the vehicle and the longitudinal axis of the vehicle body, directly reflects the dynamic posture and drifting state of the vehicle. Accurate evaluation of the side slip angle is of great significance to improve the stability, safety and driving experience of drifting control.

[0003] However, the existing side slip angle evaluation methods mainly rely on traditional sensor data such as gyroscopes, accelerometers, etc. These methods are easily disturbed by noise in complex drifting environment, resulting in inaccurate evaluation results. In addition, the existing methods often lack comprehensive analysis of the dynamic change process of the side slip angle, making it difficult to effectively distinguish the influence of different driving styles and skills on the side slip angle. Therefore, how to accurately score the side slip angle in the vehicle drifting process has become a technical problem to be solved. SUMMARY

[0004] One of the main purposes of the application is to provide a scoring method for a side slip angle term in a vehicle drifting process, which significantly improves the accuracy, robustness and practicality of drifting scoring by using segmented processing, dynamic extreme value correction, mean-oriented pruning and segmented deduction rules, and solves the problem of insufficient adaptability of traditional methods in dynamic environment.

[0005] The application achieves the above-mentioned purposes through the following technical solutions: a scoring method for a side slip angle term in a vehicle drifting process, comprising the following steps:

[0006] S1, collecting side slip angle data in the vehicle driving process to form a side slip angle curve with time as the horizontal coordinate and side slip angle as the vertical coordinate;

[0007] S2, according to the graphical features of the drifting map, setting a segmented point at each reversing point position of the driving path, dividing the driving path into multiple driving sections, and setting a detection device at the reversing point position to detect the specific time when the vehicle reaches or passes through the reversing point, and dividing the side slip angle curve into multiple sections according to the time when the vehicle passes through the set reversing points in sequence according to the driving path;

[0008] S3. For each segment of the sideslip angle curve, find the adjacent maxima and minima, and then calculate the angle difference d between the maxima and minima. For each pair of adjacent maxima and minima obtained, an angle difference d is obtained.

[0009] S4. For each angular difference obtained, an angular stability deduction is performed. Angle difference d and stability deduction The correspondence is as follows:

[0010] ;

[0011] in, Define the range of fluctuations as a quantification; The deduction value is calibrated for the corresponding fluctuation range;

[0012] S5. For each segment of the sideslip angle curve, calculate the ratio of the average sideslip angle of each segment to the set reference average sideslip angle. According to the ratio Deduct points from the corresponding average score. ,ratio Deduct points from average The correspondence is as follows:

[0013] ;

[0014] in, Quantitative values ​​for the average sideslip angle interval; The deduction values ​​are calibrated for the corresponding average sideslip angle interval.

[0015] Furthermore, step S3 includes:

[0016] S31. Traverse the side deflection angle data sequence through a double threshold window, and make extreme value judgments on the preset window range before and after each data point: if the current point is a local maximum or local minimum, mark it as a candidate extreme value.

[0017] S32. Determine whether the candidate extreme values ​​appear consecutively. If so, adopt a dynamic correction strategy to retain the position of the last extreme value.

[0018] S33. Calculate the average side deviation angle of each segment, and prune the extreme value sequence according to the sign characteristics of the average side deviation angle: if the average value is positive, delete the first and last minimum values ​​to ensure that the maximum values ​​are located at both ends of the sequence; if the average value is negative, delete the first and last maximum values ​​to maintain the alternating distribution of minimum values.

[0019] Furthermore, in step S32, if a maximum or minimum value appears and then remains unchanged, then only the last value that remains unchanged is taken as the maximum value; if a maximum value appears multiple times in a row, then the maximum value among the multiple occurrences of the maximum value is taken as the maximum value; if a minimum value appears multiple times in a row, then the minimum value among the multiple occurrences of the minimum value is taken as the minimum value.

[0020] Furthermore, it also includes step S6: deduction of points for the sideslip angle during vehicle drifting. for:

[0021] ;

[0022] in, This is the maximum deduction value.

[0023] Another object of the present invention is to provide an electronic device comprising a processor, a memory, and a bus; the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the memory via the bus, and the machine-readable instructions are executed by the processor to perform the steps of the scoring method for the sideslip angle during vehicle drifting as described above.

[0024] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the scoring method for the sideslip angle during vehicle drifting as described above.

[0025] Compared with existing technologies, the beneficial effects of this invention's scoring method, device, and medium for the sideslip angle during vehicle drifting are as follows: It collects and segments sideslip angle data, using the reversal point as the dividing point to eliminate the influence of large angle changes on the scoring; it employs a dual-threshold window to detect extreme points and dynamically corrects them, avoiding duplicate counting; it prunes the extreme value sequence based on the average sideslip angle, improving analysis accuracy; it calculates the difference between adjacent extreme angles and the mean deviation, and combines this with piecewise linear deduction rules to achieve quantitative scoring of stability and mean deviation. This invention solves the error problems caused by sudden reversal, extreme value redundancy, and static thresholds in traditional drift scoring, and can be widely applied in fields such as racing car training and autonomous driving testing. Specifically:

[0026] (1) Eliminate the interference of sudden changes in direction and improve the rationality of the score: By setting segment points at the turning points of the drift path (such as points A and B in the figure-eight track), the sideslip angle curve is divided into multiple segments (L1~L5), and the sudden change data of the turning interval (such as the sideslip angle θ changing from positive to negative) is eliminated; by segmented calculation, only the sideslip angle fluctuation of the stable drift interval is evaluated, avoiding the negative impact of the turning action on the overall score, which is more in line with the actual driving performance;

[0027] (2) Dynamic extreme value detection and correction to reduce redundant errors: Use a double threshold window (such as ±∆t range) to traverse the data and mark local extreme value points; if candidate extreme values ​​appear consecutively (such as multiple adjacent extreme values ​​due to sensor noise or plateau period), then dynamically correct to retain the last extreme value; through dynamic correction strategy, merge consecutive extreme values ​​to avoid repeated deduction and improve the robustness of scoring;

[0028] (3) Extreme value pruning based on mean sign to enhance analysis accuracy: The extreme value sequence is pruned according to the sign (positive / negative) of the average side slip angle of the section: if the mean is positive, the first and last minimum values ​​are deleted; if it is negative, the first and last maximum values ​​are deleted to ensure that the extreme values ​​are distributed alternately. This method removes interference data through mean-oriented pruning, making the extreme value analysis more consistent with the actual vehicle dynamics characteristics.

[0029] (4) Piecewise linear deduction rules to achieve refined scoring: Piecewise linear deduction functions (such as S) are designed for angle difference (d) and mean deviation (aa) respectively. ms S ma The scoring gradient and upper limit are dynamically adjusted by calibrated quantities (Fc_xc1~5, Fc_yc0~5, etc.); through multi-interval linear mapping, small fluctuations result in light scoring and large fluctuations result in heavy scoring, and a scoring upper limit is set to avoid scoring imbalance in extreme cases, which is more in line with the actual application needs. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of the drift path breakdown in an embodiment of the present invention;

[0031] Figure 2 This is a schematic diagram of the side slip angle curve in an embodiment of the present invention;

[0032] Figure 3 This is a schematic diagram of the peak detection of the side slip angle curve in an embodiment of the present invention;

[0033] Figure 4 This is a schematic diagram of the fluctuation of the side slip angle curve in an embodiment of the present invention. Detailed Implementation

[0034] Example 1:

[0035] This embodiment is a scoring method for the sideslip angle during vehicle drifting, which includes the following steps:

[0036] S1. Collect side slip angle data during vehicle movement. Plotting time t as the x-axis and the yaw angle as the yaw angle Using the vertical axis as the ordinate, a lateral deflection angle curve is formed;

[0037] S2. Based on the graphic characteristics of the drift map, segmentation points are set at each turning point of the driving path, dividing the driving path into multiple driving segments. A detection device is set at each turning point to detect the specific time when the vehicle arrives at or passes through the turning point. Based on the time when the vehicle passes through the set turning points in sequence according to the driving path, the sideslip angle curve is divided into multiple segments to eliminate the influence of the large angle generated by the turning point on the sideslip angle deduction calculation.

[0038] For example Figure 1 The image shows a figure-eight drift map. Point A is the first reversal point, and point B is the second reversal point. The figure-eight drift map is divided into five sections, L1 to L5. L1 is the section from outside the track to point A (shown by solid lines in the image). L2 is the section from point A to point B after a half-lap (shown by dotted lines in the image). L3 is the section from point B to point B after a full lap (shown by short dashed lines in the image). L4 is the section from point B to point A after a half-lap (shown by dashed lines in the image). L5 is the section from point A to point B outside the track (shown by long dashed lines in the image).

[0039] During a complete figure-eight drift, four vehicle direction changes occur near points A and B. During these changes, the vehicle's sideslip angle undergoes a significant shift, changing from positive to negative or vice versa. Observing the curves, a perfect, stable drift requires a sideslip angle curve that approximates a straight line. However, the angle changes caused by the four direction changes near points A and B greatly affect the stability score. Therefore, in this embodiment, when calculating sideslip angle deductions, the direction change points in the driving segment are removed, and the driving segment is divided into segments based on these points for segmented deduction calculations. This eliminates the impact of the large angles caused by direction changes on the sideslip angle deduction calculation.

[0040] The detection device collects the time when the vehicle passes the starting point as t0, the time when it passes point A for the first time as t1, the time when it passes point B for the first time as t2, the time when it passes point B for the second time as t3, the time when it passes point A for the second time as t4, and the time when it passes the ending point as t5.

[0041] On the sideslip angle curve, corresponding to the time intervals t0 to t5, the curve is divided into five segments, such as... Figure 2 As shown in the figure. Subsequent calculations then deduct points from the stability and average value of the sideslip angle for each of the five segments.

[0042] S3. Traverse the side deflection angle data sequence through a double threshold window and perform extreme value condition judgment on the preset window range before and after each data point: if the current point is a local maximum (all surrounding points are less than or equal to the current value) or a local minimum (all surrounding points are greater than or equal to the current value), then mark it as a candidate extreme value.

[0043] For example, the current point data is The range of the dual threshold window is Then the set A of all data within the detection window is:

[0044] ;

[0045] Then determine Is the current point a local maximum or local minimum in dataset A? If so, then set the current point's data as... Mark as a candidate extreme value.

[0046] S4. Determine whether the candidate extreme values ​​appear consecutively. If so, adopt a dynamic correction strategy to retain the position of the last extreme value and avoid errors caused by repeated counting.

[0047] In general, maxima and minima alternate, and this is calculated normally. In most cases, the extreme value is an instantaneous value. If the extreme value remains unchanged after a maximum or minimum value appears, only the last value that remains unchanged is taken as the extreme value. When the maximum or minimum value appears consecutively, such as two consecutive maximum values, the maximum value of the two maximum values ​​is taken as the extreme value.

[0048] S5. Calculate the average lateral slip angle for each segment, and prune the extreme value sequence based on the sign characteristic of the average lateral slip angle: if the average is positive, delete the first and last minima to ensure that the maxima are located at both ends of the sequence; if the average is negative, delete the first and last maxima to maintain an alternating distribution of minima, thereby eliminating the interference of atypical extreme values ​​on subsequent analysis. Figure 3 As shown.

[0049] S6. For each segment of the sideslip angle curve, extract the corresponding candidate extrema to obtain a candidate extremum set. Then calculate the angle difference d between adjacent maxima and minima. For each set of adjacent maxima and minima obtained, a corresponding angle difference d is obtained; for example... Figure 4 The numbers d1, d2, and d3 are shown.

[0050] S7. For each angle difference d obtained, an angle stability deduction is performed. Angle difference d and stability deduction The correspondence is as follows:

[0051] ;

[0052] in, The calibrated values ​​for the fluctuation range are used to adjust the range and size of the range. In this embodiment, they are set to 5, 9, 15, 20, and 30, respectively. In this embodiment, the deduction values ​​for the corresponding fluctuation ranges are set to 0, 1, 3, 5, 8, and 10, respectively.

[0053] For example, when the volatility is Points will be deducted at that time. When the volatility is Points will be deducted during this period. And so on. A maximum limit is set for point deductions, i.e., when the fluctuation amount... At any time, regardless of the amount, points will be deducted. .

[0054] S8. For each segment of the sideslip angle curve, calculate the ratio of the average sideslip angle of each segment to the set reference average sideslip angle. According to the ratio Deduct points from the corresponding average score. ,ratio Deduct points from average The correspondence is as follows:

[0055] ;

[0056] in, The calibrated values ​​for the average sideslip angle interval are used to adjust the range and size of the interval. In this embodiment, they are set to 0.1, 0.3, 0.7, 0.8, and 0.9, respectively. In this embodiment, the deduction values ​​for the corresponding average sideslip angle intervals are set to 10, 8, 7, 2, 1, and 0, respectively.

[0057] For example, when the average sideslip angle is Points will be deducted at that time. When the average sideslip angle is In between, deduct And so on. A maximum penalty is set, i.e., when the average sideslip angle... At any time, regardless of the amount, points will be deducted. .

[0058] S9. During vehicle drifting, the deduction of points related to the sideslip angle in the normal scoring area. Then it is:

[0059] ;

[0060] in, Deduct points for sideslip angle stability. Points are deducted based on the average side slip angle. The base score for side slip angle deduction is... Therefore, the maximum deduction value is 10 points. For example, 25, 30 points, etc.

[0061] This embodiment also provides an electronic device, which includes a processor, a memory, and a bus.

[0062] The memory stores machine-readable instructions that the processor can execute. When the electronic device is running, the processor and the memory communicate via a bus. When the machine-readable instructions are executed by the processor, the steps of the scoring method for the sideslip angle during vehicle drifting described above can be implemented.

[0063] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, can perform steps of the scoring method for the sideslip angle during vehicle drifting as described above.

[0064] For those skilled in the art, various modifications and improvements can be made without departing from the inventive concept of this invention, and these all fall within the protection scope of this invention.

Claims

1. A scoring method for the sideslip angle during vehicle drifting, characterized in that: It includes the following steps: S1. Collect the sideslip angle data during vehicle driving, and form a sideslip angle curve with time as the horizontal axis and sideslip angle as the vertical axis; S2. Based on the graphic features of the drift map, segmentation points are set at each turning point of the driving path to divide the driving path into multiple driving segments; and a detection device is set at the turning point to detect the specific time when the vehicle arrives at or passes the turning point. Based on the time when the vehicle passes through the set turning points in sequence according to the driving path, the sideslip angle curve is divided into multiple segments. S3. For each segment of the sideslip angle curve, find the adjacent maxima and minima, and then calculate the angle difference d between the maxima and minima. For each pair of adjacent maxima and minima obtained, an angle difference d is obtained. S4. For each angular difference obtained, an angular stability deduction is performed. Angle difference d and stability deduction The correspondence is as follows: ； in, Define the range of fluctuations as a quantification; The deduction value is calibrated for the corresponding fluctuation range; S5. For each segment of the sideslip angle curve, calculate the ratio of the average sideslip angle of each segment to the set reference average sideslip angle. According to the ratio Deduct points from the corresponding average score. ,ratio Deduct points from average The correspondence is as follows: ； in, Quantitative values ​​for the average sideslip angle interval; The deduction values ​​are calibrated for the corresponding average sideslip angle interval.

2. The scoring method for the sideslip angle during vehicle drifting as described in claim 1, characterized in that: Step S3 includes: S31. Traverse the side deflection angle data sequence through a double threshold window, and make extreme value judgments on the preset window range before and after each data point: if the current point is a local maximum or local minimum, mark it as a candidate extreme value. S32. Determine whether the candidate extreme values ​​appear consecutively. If so, adopt a dynamic correction strategy to retain the position of the last extreme value. S33. Calculate the average side deviation angle of each segment, and prune the extreme value sequence according to the sign characteristics of the average side deviation angle: if the average value is positive, delete the first and last minimum values ​​to ensure that the maximum values ​​are located at both ends of the sequence; if the average value is negative, delete the first and last maximum values ​​to maintain the alternating distribution of minimum values.

3. The scoring method for the sideslip angle during vehicle drifting as described in claim 2, characterized in that: In step S32, if a maximum or minimum value appears and then remains unchanged, then only the last value that remains unchanged is taken as the maximum value; if a maximum value appears multiple times in a row, then the maximum value among the multiple maximum values ​​is taken as the maximum value. If a minimum value appears multiple times consecutively, then the minimum value among the consecutive minimum values ​​is taken as the minimum value.

4. The scoring method for the sideslip angle during vehicle drifting as described in claim 1, characterized in that: It also includes step S6: deduction of points for the sideslip angle during vehicle drifting. for: ； in, This is the maximum deduction value.

5. An electronic device, characterized in that: Includes processor, memory, and bus; The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via a bus. The machine-readable instructions are executed by the processor to perform the steps of the scoring method for the sideslip angle during vehicle drifting as described in any one of claims 1 to 4.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the method for scoring the sideslip angle during vehicle drift as described in any one of claims 1 to 4.

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