Vehicle control method, device and equipment and storage medium

Abstract

The invention discloses a vehicle control method, device and equipment and a storage medium, and relates to the technical field of vehicle control, and the method comprises the steps: constructing a speed correction coefficient according to driver information, surrounding environment information and / or driver historical driving records; acquiring a coefficient weight value corresponding to each speed correction coefficient; constructing a speed correction parameter according to the coefficient weight value and the speed correction coefficient; and determining a speed upper limit value according to the speed correction parameter and the reference speed, and performing speed control on the vehicle based on the speed upper limit value. The multi-dimensional speed correction coefficient is generated by comprehensively considering the personal information of the driver, the historical driving record and the surrounding environment information of the vehicle, then the speed upper limit value is set in combination with the coefficient weight value and the reference speed, speed limiting control is conducted on the vehicle, and it is guaranteed that the driving speed of the vehicle is increased under the condition that actual driving of the driver is not affected as much as possible. And the upper limit of the vehicle speed is reasonably limited, so that the safety is improved.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to vehicle control methods, devices, equipment and storage media. Background Technology

[0002] Currently, there are no measures to actively limit the speed of cars under manual driving conditions. Moreover, with the trend of electrification, the acceleration and maximum speed of vehicles have generally increased. In cases of insufficient driving experience, misoperation, or fatigued driving, it is easy to cause excessive speed, which can lead to serious consequences in the event of a car accident. Summary of the Invention

[0003] The main objective of this application is to provide a vehicle control method, device, equipment, and storage medium, which aims to solve the technical problem that related technologies do not reasonably limit the speed of vehicles based on the actual situation of the driver.

[0004] To achieve the above objectives, this application proposes a vehicle control method, the method comprising: Speed ​​correction coefficients are constructed based on driver information, surrounding environment information, and / or the driver's historical driving records. The speed correction coefficients include at least one of vehicle familiarity coefficient, driving experience coefficient, fatigue level coefficient, vehicle surrounding environment coefficient, and driving habit coefficient. Obtain the coefficient weight values ​​corresponding to each speed correction factor; A speed correction parameter is constructed based on the coefficient weight values ​​and the speed correction coefficient. The upper speed limit is determined based on the speed correction parameter and the reference speed, and the vehicle speed is controlled based on the upper speed limit.

[0005] Optionally, before constructing the speed correction coefficient based on driver information, surrounding environment information, and / or the driver's historical driving records, the method further includes: Acquire speed sample data, which includes the maximum speed corresponding to each moment when the driver is actually driving the vehicle within a preset time period; Construct at least one weight combination based on the weight adjustment step size and weight constraint information; Based on the speed sample data, calculate the sample standard deviation corresponding to each weight combination; Select the target combination from the weighted combinations based on the sample standard deviation; If the weight adjustment step size is less than or equal to a preset step size threshold, then the target combination is marked as the optimal combination; Accordingly, obtaining the coefficient weight values ​​corresponding to each speed correction coefficient includes: Extract the coefficient weight values ​​corresponding to each speed correction coefficient from the optimal combination.

[0006] Optionally, constructing at least one weight combination based on the weight adjustment step size and weight constraint information includes: The total weight value and the weight value range corresponding to each speed correction coefficient are determined based on the weight constraint information. The selectable weight values ​​corresponding to each speed correction coefficient are determined based on the weight value range and the weight adjustment step size. The optional weight values ​​corresponding to each speed correction coefficient are arranged and combined to construct multiple coefficient combinations; Calculate the sum of the optional weight values ​​in each coefficient combination to obtain the total sum of the combination coefficients for each coefficient combination; The coefficient combination whose sum of corresponding combination coefficients equals the sum of the weight values ​​is taken as the weight combination.

[0007] Optionally, after selecting the target combination from the weighted combinations based on the sample standard deviation, the method further includes: If the weight adjustment step size is greater than the preset step size threshold, then the weight adjustment step size is reduced to obtain a new weight adjustment step size; Construct at least one new weight combination based on the target combination, the new weight adjustment step size, and the weight constraint information, and return the step of calculating the sample standard deviation corresponding to each weight combination based on the velocity sample data.

[0008] Optionally, constructing at least one new weight combination based on the target combination, the new weight adjustment step size, and weight constraint information includes: Extract the coefficient weight values ​​corresponding to each velocity correction coefficient from the target combination; The coefficient weights corresponding to each speed correction coefficient are extracted from the target combination as the value center, and the optional weight values ​​corresponding to each speed correction coefficient are calculated according to the new weights by adjusting the step size. The optional weight values ​​corresponding to each speed correction coefficient are arranged and combined to construct multiple coefficient combinations; Calculate the sum of the optional weight values ​​in each coefficient combination to obtain the total sum of the combination coefficients for each coefficient combination; The coefficient combination whose sum of corresponding combination coefficients equals the sum of weight values ​​is taken as the new weight combination.

[0009] Optionally, the step of calculating the sample standard deviation corresponding to each weight combination based on the speed sample data includes: Determine the sample baseline velocity and sample velocity correction coefficient based on the velocity sample data; The upper limit value of the speed corresponding to each weight combination is determined based on the sample speed correction coefficient, the weight combination, and the sample baseline speed. The standard deviation of each weight combination is calculated based on the upper limit of speed corresponding to each weight combination and the speed sample data.

[0010] Optionally, before determining the upper speed limit based on the speed correction parameter and the reference speed, and before performing speed control on the vehicle based on the upper speed limit, the method further includes: Calculate the upper speed limit corresponding to the historical driving record of each driver, and obtain the historical driving speed corresponding to the historical driving record of each driver; A personal correction coefficient is constructed based on the aforementioned speed limit and the historical driving speed. Accordingly, determining the upper speed limit based on the speed correction parameter and the reference speed, and controlling the vehicle speed based on the upper speed limit, includes: The upper speed limit is determined based on the personal correction coefficient, the speed correction parameter, and the reference speed, and the vehicle speed is controlled based on the upper speed limit.

[0011] Furthermore, to achieve the above objectives, this application also proposes a vehicle control device, the vehicle control device comprising: The construction module is used to construct a speed correction coefficient based on driver information, surrounding environment information and / or the driver's historical driving record. The speed correction coefficient includes at least one of vehicle familiarity coefficient, driving experience coefficient, fatigue level coefficient, vehicle surrounding environment coefficient and driving habit coefficient. The acquisition module is used to obtain the coefficient weight values ​​corresponding to each speed correction coefficient; The calculation module is used to construct the speed correction parameter based on the coefficient weight value and the speed correction coefficient; The control module is used to determine the upper speed limit based on the speed correction parameter and the reference speed, and to control the speed of the vehicle based on the upper speed limit.

[0012] In addition, to achieve the above objectives, this application also proposes a vehicle control device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the vehicle control method as described above.

[0013] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and which, when executed by a processor, implements the steps of the vehicle control method described above.

[0014] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the vehicle control method described above.

[0015] One or more technical solutions proposed in this application have at least the following technical effects: This application comprehensively considers the driver's personal information, historical driving records, and environmental information around the vehicle to generate multi-dimensional speed correction coefficients. Then, it combines the coefficient weight values ​​and the baseline speed to set the speed limit value, thereby controlling the vehicle's speed and ensuring that the vehicle's speed limit is reasonably restricted without affecting the driver's actual driving as much as possible, thereby improving safety. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating an embodiment of the vehicle control method of this application. Figure 2 This is a flowchart illustrating Embodiment 2 of the vehicle control method of this application; Figure 3 This is a flowchart illustrating Embodiment 3 of the vehicle control method of this application; Figure 4 This is a schematic diagram of the module structure of the vehicle control device according to an embodiment of this application; Figure 5 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the vehicle control method in the embodiments of this application.

[0019] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0021] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0022] Based on this, embodiments of this application provide a vehicle control method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the vehicle control method of this application.

[0023] In this embodiment, the vehicle control method includes steps S10 to S40: Step S10: Construct a speed correction coefficient based on driver information, surrounding environment information, and / or the driver's historical driving records.

[0024] It should be noted that the executing entity in this embodiment can be the vehicle itself or a vehicle control device installed in the vehicle. The vehicle control device can be a controller in the vehicle, such as an ECU controller, or other devices that can achieve the same or similar functions. This embodiment does not limit this. In this embodiment and the following embodiments, the vehicle control device is used as an example to illustrate the vehicle control method of this application.

[0025] It should be noted that driver information can be collected from vehicle drivers, including information such as vehicle usage time, driver's license duration, and current fatigue level. Driver's historical driving records can be collected from previous driving data, including information such as vehicle speed, turning angle, and turning speed. Surrounding environment information can be information about the environment around the vehicle, including road specifications, road conditions, and nearby pedestrians.

[0026] In practical use, the speed correction factor may include at least one of the following: vehicle familiarity factor, driving experience factor, fatigue factor, vehicle surrounding environment factor, and driving habit factor.

[0027] In practical implementation, the driver's familiarity with the vehicle can be defined by the cumulative driving time. The longer the time, the more familiar the driver is. Based on this, the vehicle familiarity coefficient can be calculated based on the cumulative driving time being greater than or equal to a specific duration. For example, the relationship between the cumulative driving time and the vehicle familiarity coefficient k1 is: 200:100:50:0 = 1.0:0.98:0.95:0.9.

[0028] The formula 200:100:50:0 = 1.0:0.98:0.95:0.9 represents the vehicle familiarity coefficient k1. If the cumulative driving time exceeds 200 hours, the vehicle familiarity coefficient k1 = 1, and so on. The representation methods used below are similar and will not be elaborated further. The dynamic update cycle for the k1 coefficient is calculated once every time the vehicle is started. Within a single vehicle start cycle, this coefficient remains unchanged.

[0029] To facilitate understanding, an example is given below, but this does not limit the scope of the solution: Suppose that after boarding the vehicle, facial recognition identifies that driver A is driving the vehicle, and the database is checked to confirm that A has been driving the vehicle for 150 hours. Then, according to the definition, the coefficient k1 is taken as 0.98.

[0030] In practice, driving experience can be determined based on the time the driver's license was obtained and the driver's gender. Specifically, a driving experience coefficient can be constructed based on the driver's gender and the number of years the driver has held the license. For example, the driving experience coefficient k2 = q1 * q2. q1 is defined as: male:female = 1:0.98, meaning that, based on experience, males drive faster than females. q2 is defined as the number of years the driver's license has been held. The longer the number of years, the more driving experience is assumed to be. The relationship between the number of years and the coefficient can be represented as: 3:2:1:0 = 1:0.98:0.95:0.9.

[0031] To facilitate understanding, an example is provided below, but this does not limit the scope of the solution: If facial recognition identifies the driver as male, then the coefficient q1 is 1; simultaneously, if facial recognition matches the driver's license acquisition time recorded in the driver's file and the number of years the license has been held is greater than 3, then according to the definition, the coefficient q2 is 1, and the driving experience coefficient k2 = q1 * q2 = 1.

[0032] The dynamic update cycle of the driving experience coefficient can be calculated once every time the vehicle is started. Within a single vehicle start cycle, the coefficient remains unchanged until the next vehicle restart.

[0033] In practice, the driver's fatigue level can be determined based on the driver's continuous driving time and specific facial expressions (such as the number of blinks and the duration of yawning). The more severe the fatigue, the smaller the fatigue coefficient.

[0034] Specifically, a fatigue assessment method can be established by measuring continuous driving time (T, in hours), the longest duration of a single blink within a unit of time (e.g., one hour) (S, in seconds), and the longest duration of a single yawn within a unit of time (e.g., one hour) (L, in seconds). The fatigue level score is F, ranging from 0 to 10, with higher values ​​indicating greater fatigue. The fatigue level is then determined based on the score, and a fatigue level coefficient is determined based on the fatigue level.

[0035] In practical implementation, considering the differences in fatigue levels among different performance responses, the formulas for calculating F and its various values ​​are as follows: F = β1*T + β2*S³ + β3*L²; where the sum of β1 to β3 is 1.

[0036] The appropriate β1~β3 can be calculated by surveying multiple passenger sample data and passenger self-evaluation F: The calculated values ​​for β1 to β3 are 0.25, 0.35, and 0.4, respectively.

[0037] The fatigue score is calculated as F = 0.25 * T + 0.35 * S² + 0.4 * L³. The F-values ​​correspond to the following ranges for different levels of fatigue: 0~0.5, no fatigue; 0.5~1.5, mild fatigue; 1.5~4, moderate fatigue; 4~∞, severe fatigue.

[0038] The relationship between fatigue level and fatigue level coefficient can be expressed as: no fatigue: mild fatigue: moderate fatigue: severe fatigue = 1.1:1:0.98:0.9.

[0039] For example: Suppose driver A has been driving for 2 hours. The longest blinking time within 1 hour is detected to be 1.5 seconds, and the longest yawning duration is 2 seconds. Then, F = 0.25*2 + 0.35*1.5² + 0.4*2³ = 4.49, which corresponds to a severe fatigue level. Therefore, the fatigue level coefficient k3 is 0.9.

[0040] The dynamic update cycle for the fatigue level coefficient can be set to 1.1, with the vehicle starting at the default state of no fatigue. The actual values ​​of the three dimensions above are then calculated every hour thereafter, and the real-time fatigue level coefficient k3 is matched based on the calculated F value.

[0041] In the specific implementation, the vehicle's surrounding environment coefficient k4 can be determined based on the vehicles and pedestrians in the current vehicle's surrounding environment, such as: let k4 = b1 * b2; Here, b1 corresponds to the surrounding traffic flow. This can be determined by recording the number of vehicles traveling in the direction of travel within a certain time period using vehicle cameras. It can be defined as: counting the number of vehicles within a 30m radius ahead within 1 minute (counting once every 10 seconds, for a total of 6 times). This count determines the coefficients corresponding to different congestion levels. For example, a vehicle takes 6 photos every 1 minute (each 10-second interval) to calculate the number of vehicles within a 30m radius ahead. The total number of vehicles within the 6 ranges is counted. The current road segment is defined as congested, slow-moving, normal, and smooth-moving based on the number of vehicles reaching 80, 60, 30, and 10 respectively. The coefficients for these four states are represented as: Congested: Slow-moving: Normal: Smooth-moving = 0.9:0.95:1:1.3; b2 corresponds to the surrounding pedestrian flow. The number of people on the road in the direction of the vehicle's travel can be recorded by the vehicle's camera within a certain period of time. It can be defined as: the number of people within a 30m range in front of the vehicle within 1 minute (counted once every 10 seconds, for a total of 6 times) to define the degree of congestion. The coefficients corresponding to pedestrian flow conditions are defined as follows: Vehicles take 6 photos every 1 minute (10-second intervals) and calculate the number of pedestrians within a 30-meter radius ahead. The total number of pedestrians within the 6 ranges is counted, and the current road segment is defined as congested, busy, normal, or sparse based on the number of pedestrians: ≥30, ≥15, ≥5, and ≤5. The coefficients corresponding to these four states are represented as: Congested: Busy: Normal: Sparse = 0.8:0.95:1:1.3; Under this definition, assuming that while driving a vehicle to a certain road section, the number of vehicles and people within the first 30 meters recorded by the camera is 18 people and 45 vehicles respectively, then the corresponding coefficients b1 and b2 are 0.95 and 1 respectively. Therefore, the calculated vehicle surrounding environment coefficient k4 = 0.95 * 1 = 0.95.

[0042] The dynamic update cycle for the vehicle's surrounding environment coefficient can be k4=1 by default each time the vehicle is started, and the k4 value can be dynamically updated every minute according to the set logic after the vehicle starts.

[0043] In practice, the driving habit coefficient k5 can be determined based on the driver's driving habits. Specifically, the driving level can be assessed based on the user's driving habits. The rating is calculated every certain period of time or every mile of driving, such as once every 30 minutes or every 40 kilometers. The main assessment content can include sudden braking, sudden acceleration, sharp turns, speeding, etc.

[0044] For example: Driving grades A, B, C, and D correspond to score ranges of ≥90 points, ≥80 points, ≥70 points, and ≤70 points, respectively, with a maximum score of 100 points; The formula for calculating the driving level score is S = (speed score + rapid acceleration score + sharp turn score + emergency braking score) / 4; Speed ​​rating: 100 points if the vehicle speed is between 80% and 100% of the speed limit. 1 point is deducted for each instance of exceeding the speed limit outside the designated range within a 10-hour period, with a minimum score of 0. Rapid acceleration score: ≤ 5 rapid accelerations within 10 hours: 100 points; more than 5 rapid accelerations: deduct 4 points for each additional acceleration, minimum 0 points; Sharp Turn Scoring: ≤ 3 sharp turns within 10 hours: 100 points; more than 3 sharp turns: deduct 5 points for each additional sharp turn, minimum 0 points; Emergency braking score: ≤ 5 emergency braking incidents within 10 hours: 100 points; more than 5 incidents: deduct 4 points for each additional incident, minimum 0 points; The correspondence between driving levels from highest to lowest and driving habit coefficients can be represented as: A:B:C:D=1.1:1.05:1:0.9; Based on this, assuming the driver exceeded the speed limit 20 times, accelerated sharply 8 times, made sharp turns 6 times, and braked sharply 8 times within a cumulative 10 hours, his score would be: [(100-20) + (100-12) + (100-15) + (100-12)] / 4 = 85.25. His driving rating is B, corresponding to a driving habit coefficient k5 = 1.05.

[0045] The dynamic update cycle for the driving habit coefficient can be k5=1 by default each time the vehicle is started. After the vehicle is started, the driving level score is calculated every 10 hours according to the set logic, and the k5 value is dynamically updated.

[0046] Of course, the examples above are merely illustrative. The specific scoring criteria, testing cycles, and other data can be dynamically adjusted according to actual needs, and this embodiment does not impose any restrictions on this.

[0047] Step S20: Obtain the coefficient weight value corresponding to each speed correction coefficient.

[0048] Step S30: Construct speed correction parameters based on the coefficient weight values ​​and the speed correction coefficients.

[0049] It should be noted that, considering that the impact of several coefficients on safe driving is not entirely the same, certain calculation weights are added to the corresponding coefficients. Specifically, the managers of vehicle control equipment can pre-set the corresponding coefficient weight values ​​for each speed correction coefficient.

[0050] When setting the coefficient weight values, the vehicle control equipment management personnel can set them based on practical experience, or they can be calibrated through big data calculations. This embodiment does not impose any restrictions on this.

[0051] In practical applications, the speed correction coefficients can be weighted and summed according to their respective weight values, and the summed value can be used as the speed correction parameter.

[0052] For example: Assuming the speed correction coefficients are k1-k5, and the weights of each speed correction coefficient are α1-α5, then the speed correction parameter K = α1*k1 + α2*k2 + α3*k3 + α4*k4 + α5*k5.

[0053] Step S40: Determine the upper speed limit based on the speed correction parameter and the reference speed, and control the vehicle speed based on the upper speed limit.

[0054] It should be noted that the base speed can be obtained from the cloud or determined based on the vehicle's location. For example, after the vehicle starts, the vehicle's location can be determined by the GPS positioning device on the vehicle. Then, the speed limit information V of the road at that location can be read from the cloud as the base speed. If the cloud sets the corresponding speed limit, the road segment can be located and matched based on the vehicle's location (such as by matching a road segment through navigation software), and the speed limit of that road segment can be used as the base speed.

[0055] When determining speed limits based on road segments, speed limits can be determined according to the type of road segment, such as: expressways: 100km / h; national and provincial highways: 80km / h; county roads or other roads, non-roads, etc.: 60km / h.

[0056] In actual use, some roads have special speed limits. For example, some national highways may have a speed limit of 60 km / h. When there is a difference between the speed limit determined according to the road section and the special speed limit set for the road, the special speed limit set for the road shall prevail.

[0057] This embodiment provides a vehicle control method. This embodiment comprehensively considers the driver's personal information, historical driving records, and environmental information around the vehicle to generate multi-dimensional speed correction coefficients. Then, it combines the coefficient weight values ​​and the base speed to set a speed limit value to control the vehicle's speed. This ensures that the vehicle's speed limit is reasonably restricted without affecting the driver's actual driving as much as possible, thereby improving safety.

[0058] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 Before step S10, the vehicle control method further includes steps S01 to S05: Step S01: Obtain speed sample data.

[0059] It should be noted that the speed sample data may include the maximum speed corresponding to each moment when the driver is actually driving the vehicle within a preset time period. The preset time period can be set in advance by the vehicle control equipment administrator, such as setting the preset time period to within 24 hours before the current moment.

[0060] Specifically, it may be difficult to collect a large amount of speed sample data from a single driver. In order to ensure the richness of speed sample data, driving data from other drivers with similar characteristics to the current vehicle's driver (such as similar age, gender, years of driving experience, etc.) can be collected and combined with the driving data of the current vehicle's driver to construct speed sample data.

[0061] Step S02: Construct at least one weight combination based on the weight adjustment step size and weight constraint information.

[0062] It should be noted that each weight combination can correspond to a set of coefficient weight values ​​for each speed correction coefficient. The weight adjustment step size can be the difference between the selectable weight values ​​during a single optimization. The weight constraint information can include the range of values ​​for the coefficient weight values ​​corresponding to each speed correction coefficient, as well as the sum of the coefficient weight values ​​corresponding to each speed correction coefficient. For example, if the range of values ​​for the coefficient weight values ​​corresponding to each speed correction coefficient is [0.1, 0.3], and the sum is 1.

[0063] In a specific implementation, in order to reasonably construct the weight combination, step S02 of this embodiment may include: The total weight value and the weight value range corresponding to each speed correction coefficient are determined based on the weight constraint information. The selectable weight values ​​corresponding to each speed correction coefficient are determined based on the weight value range and the weight adjustment step size. The optional weight values ​​corresponding to each speed correction coefficient are arranged and combined to construct multiple coefficient combinations; Calculate the sum of the optional weight values ​​in each coefficient combination to obtain the total sum of the combination coefficients for each coefficient combination; The coefficient combination whose sum of corresponding combination coefficients equals the sum of the weight values ​​is taken as the weight combination.

[0064] It should be noted that the weight constraint information can be parsed to read the sum of weight values ​​and the range of weight values ​​corresponding to each speed correction coefficient.

[0065] In practical applications, multiple optional weight values ​​can be divided within the weight value range using the weight adjustment step size as the value interval, thereby determining the weight value range corresponding to each speed correction coefficient. For example, assuming the weight value range of the speed correction coefficient is [0.1, 0.3] and the weight adjustment step size is 0.1, then the optional weight values ​​of the speed correction coefficient are 0.1, 0.2, and 0.3.

[0066] In practical applications, the optional weight values ​​corresponding to each speed correction coefficient can be arranged and combined to construct multiple coefficient combinations.

[0067] In practical applications, combinations constructed using permutations and combinations may have some coefficient combinations whose sum of coefficients does not equal the sum of weight values. Such coefficient combinations are unreasonable and need to be excluded. Therefore, the sum of the selectable weight values ​​in each coefficient combination can be calculated separately to obtain the sum of the combination coefficients corresponding to each coefficient combination. Then, only coefficient combinations whose sum of combination coefficients equals the sum of the weight values ​​are used as weight combinations.

[0068] For example: Suppose there are 5 speed correction coefficients, k1-k5, and the sum of the weight values ​​determined by the weight constraint information is 1. The weight value range for each speed correction coefficient is [0.1, 0.3], and the weight adjustment step size is 0.1. Then, the selectable weight values ​​for each speed correction coefficient are 0.1, 0.2, and 0.3. By arranging and combining them, we can obtain 3*3*3*3*3=243 coefficient combinations. Among them, there are only 51 coefficient combinations whose sum of combined coefficients is equal to 1. These 51 coefficient combinations can be used as weight combinations.

[0069] Step S03: Based on the speed sample data, calculate the sample standard deviation corresponding to each weight combination.

[0070] Step S04: Select the target combination from the weighted combinations based on the sample standard deviation.

[0071] It should be noted that the sample standard deviation is used to characterize the difference between the speed limit calculated based on the coefficient weight values ​​in the weight combination and the actual speed. The larger the sample standard deviation, the greater the difference.

[0072] In practical applications, the weight combination with the smallest corresponding sample standard deviation can be selected as the target combination, thereby ensuring that the weight combination that is closer to the calculated speed limit and the actual speed can be used as the target combination.

[0073] In a specific implementation, in order to reasonably calculate the sample standard deviation, step S03 of this embodiment may include: Determine the sample baseline velocity and sample velocity correction coefficient based on the velocity sample data; The upper limit value of the speed corresponding to each weight combination is determined based on the sample speed correction coefficient, the weight combination, and the sample baseline speed. The standard deviation of each weight combination is calculated based on the upper limit of speed corresponding to each weight combination and the speed sample data.

[0074] It should be noted that when collecting speed sample data, a reference speed can be determined based on the vehicle's position information at the time of collection and stored as the sample reference speed in the speed sample data. Similarly, a speed correction coefficient can be constructed based on driver information, surrounding environment information, and / or the driver's historical driving records when collecting speed sample data and stored as the sample speed correction coefficient in the speed sample data.

[0075] The sample reference velocity is obtained in the same way as the aforementioned reference velocity, and the sample velocity correction coefficient is determined in the same way as the aforementioned velocity correction coefficient, so it will not be repeated here.

[0076] In practical applications, the upper limit of speed corresponding to each weight combination can be calculated based on the sample speed correction coefficient, weight combination, and sample baseline speed. Then, the sample standard deviation corresponding to each weight combination can be calculated by combining the upper limit of speed corresponding to the weight combination with the speed sample data.

[0077] For example: Assuming the baseline velocity of the sample is V, and the sample velocity correction coefficients are k1, k2, k3, k4, and k5, and the weight values ​​of each coefficient in the weight combination are α1, α2, α3, α4, and α5 respectively, then the upper limit of the velocity corresponding to this weight combination is Vmax = V * (α1 * k1 + α2 * k2 + α3 * k3 + α4 * k4 + α5 * k5). The standard deviation s of the sample corresponding to this weight combination is calculated using the following formula:

[0078] ;

[0079] In the formula, Vmax is the upper limit of speed corresponding to the weight combination, Vtn is the maximum speed at time tn when the driver is actually driving the vehicle in the speed sample data, and n is the total number of speed sample data.

[0080] Step S05: If the weight adjustment step size is less than or equal to the preset step size threshold, then the target combination is marked as the optimal combination.

[0081] Accordingly, step S20 may include step S20': Step S20': Extract the coefficient weight values ​​corresponding to each speed correction coefficient from the optimal combination.

[0082] It should be noted that the preset step size threshold can be set in advance by the vehicle control equipment administrator.

[0083] In practical use, the weight adjustment step size is the difference between the selectable weight values, which can reflect the accuracy of the weight values ​​of each coefficient in the constructed weight combination; If the weight adjustment step size is less than or equal to the preset step size threshold, it means that the accuracy of the coefficient weight values ​​in the target combination is sufficient. Therefore, the target combination can be marked as the optimal combination. Subsequently, when actually limiting the speed, the coefficient weight values ​​corresponding to each speed correction coefficient can be extracted from the optimal combination.

[0084] In a specific implementation, in order to reasonably construct the optimal combination, after step S04 in this embodiment, the following may also be included: If the weight adjustment step size is greater than the preset step size threshold, then the weight adjustment step size is reduced to obtain a new weight adjustment step size; Construct at least one new weight combination based on the target combination, the new weight adjustment step size, and the weight constraint information, and return the step of calculating the sample standard deviation corresponding to each weight combination based on the velocity sample data.

[0085] It should be noted that if the weight adjustment step size is greater than the preset step size threshold, it means that the accuracy of the coefficient weight values ​​in the target combination is insufficient. In this case, the weight adjustment step size can be reduced to generate a new weight adjustment step size. Then, based on the target combination, the new weight adjustment step size, and the weight constraint information, at least one new weight combination is constructed. After that, the process returns to step S03 to continue the optimization.

[0086] When reducing the weight adjustment step size, it can be reduced according to a preset rule. For example, based on the increase in the number of optimization attempts, the weight adjustment step size can be reduced in the order of 0.1-0.05-0.02-0.01.

[0087] Of course, depending on actual needs, it can be reduced in other ways, such as reducing by 0.01 each time or reducing by half each time, etc. This embodiment does not limit this.

[0088] In a specific implementation, in order to reasonably construct a new weight combination, the step of constructing at least one new weight combination based on the target combination, the new weight adjustment step size, and the weight constraint information, as described in this embodiment, may include: Extract the coefficient weight values ​​corresponding to each velocity correction coefficient from the target combination; The coefficient weights corresponding to each speed correction coefficient are extracted from the target combination as the value center, and the optional weight values ​​corresponding to each speed correction coefficient are calculated according to the new weights by adjusting the step size. The optional weight values ​​corresponding to each speed correction coefficient are arranged and combined to construct multiple coefficient combinations; Calculate the sum of the optional weight values ​​in each coefficient combination to obtain the total sum of the combination coefficients for each coefficient combination; The coefficient combination whose sum of corresponding combination coefficients equals the sum of weight values ​​is taken as the new weight combination.

[0089] In practical use, the coefficient weight value corresponding to each speed correction coefficient can be taken as the center value. This value is added and subtracted from the new weight adjustment step size to generate two weight values. Then, the two weight values ​​generated are combined with the coefficient weight value corresponding to the speed correction coefficient to obtain the optional weight value corresponding to the speed correction coefficient.

[0090] For example: Suppose that the coefficient weight of the speed correction coefficient k1 in the target combination is 0.1, and the new weight adjustment step size is 0.05, then the optional weight values ​​of the speed correction coefficient k1 are 0.1-0.05, 0.1, and 0.1+0.05, that is, 0.05, 0.1, and 0.15.

[0091] In practical use, multiple coefficient combinations are constructed through permutation and combination. Then, the coefficient combination whose sum of corresponding coefficients equals the sum of weight values ​​is used as a new weight combination. The implementation method is the same as described above, and will not be repeated here.

[0092] This embodiment provides a vehicle control method. This embodiment uses a specific optimization algorithm to progressively optimize and determine the most suitable weight combination, so that the set speed limit value is more in line with the actual situation when the speed limit control is actually performed.

[0093] Based on the first embodiment of this application, in the third embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 Before step S40, the vehicle control method further includes steps S31-S32: Step S31: Calculate the speed limit value corresponding to the historical driving record of each driver, and obtain the historical driving speed corresponding to the historical driving record of each driver.

[0094] In practical use, a speed correction coefficient can be constructed based on the driver information, surrounding environment information and / or the driver's historical driving record recorded in the historical driving record. The base speed is determined based on the location information recorded in the historical driving record. Then, the upper limit of speed that should be used when collecting historical driving records is calculated by combining the speed correction coefficient, the coefficient weight value and the base speed.

[0095] At the same time, the actual driving speed of the driver when actually driving the vehicle can be extracted from the driver's historical driving record and used as the historical driving speed corresponding to the driver's historical driving record.

[0096] Step S32: Construct a personal correction coefficient based on the speed limit and the historical driving speed.

[0097] In practical use, the personal correction factor kp can be calculated by substituting the speed limit and historical driving speed into the following formula:

[0098] In the formula, kp is the personal correction coefficient, Umn is the speed limit corresponding to the nth driver's historical driving record, Utn is the historical driving speed corresponding to the nth driver's historical driving record, and n is the total number of the driver's historical driving records.

[0099] Accordingly, step S40 includes step S40': The upper speed limit is determined based on the personal correction coefficient, the speed correction parameter, and the reference speed, and the vehicle speed is controlled based on the upper speed limit.

[0100] In practical use, the upper limit of speed can be determined based on the individual correction factor, speed correction parameter, and reference speed.

[0101] For example: Assuming the personal correction factor is kp, the speed correction parameter is K, and the base speed is V, then the upper limit of speed Vmax = V*(K+kp).

[0102] To avoid the personal correction factor being too large and affecting the final speed limit effect, a reasonable range of values ​​can be set for the personal correction factor kp, for example: defining -0.1≤Kp≤0.1.

[0103] Understandably, this allows for the introduction of individual adjustment parameters, which takes into account the differences among drivers when setting speed limits, ensuring that the actual speed limit is more in line with reality.

[0104] Specifically, when setting speed limits, additional speed limits required for overtaking and other needs can be taken into account. Based on this, an overtaking correction coefficient is introduced to comprehensively construct the upper speed limit.

[0105] For example: Assuming the personal correction factor is kp, the speed correction parameter is K, the base speed is V, and the overtaking correction factor is a, then the speed limit Vmax = V*a*(K+kp).

[0106] The overtaking correction factor can be determined by the vehicle control equipment manager based on the actual permissible speeding range. For example, if the permissible speeding range is 10%, then the overtaking correction factor a = 1.1.

[0107] This embodiment provides a vehicle control method. This embodiment also takes into account the individual driving style of the driver and sets an additional personal correction coefficient based on the individual driving style to ensure that the differences between drivers can be taken into account when controlling the speed limit, so that the actual speed limit is more in line with reality.

[0108] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the vehicle control method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0109] This application also provides a vehicle control device, please refer to... Figure 4 The vehicle control device includes: The construction module 10 is used to construct a speed correction coefficient based on driver information, surrounding environment information and / or driver's historical driving records. The speed correction coefficient includes at least one of vehicle familiarity coefficient, driving experience coefficient, fatigue coefficient, vehicle surrounding environment coefficient and driving habit coefficient. Module 20 is used to obtain the coefficient weight values ​​corresponding to each speed correction coefficient; Calculation module 30 is used to construct speed correction parameters based on the coefficient weight values ​​and the speed correction coefficients; The control module 40 is used to determine the upper speed limit value based on the speed correction parameter and the reference speed, and to control the speed of the vehicle based on the upper speed limit value.

[0110] The vehicle control device provided in this application, employing the vehicle control method described in the above embodiments, can solve the technical problem that related technologies do not reasonably limit the vehicle speed based on the driver's actual situation. Compared with the prior art, the beneficial effects of the vehicle control device provided in this application are the same as those of the vehicle control method provided in the above embodiments, and other technical features in the vehicle control device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0111] This application provides a vehicle control device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the vehicle control method in Embodiment 1 above.

[0112] The following is for reference. Figure 5 The diagram illustrates a structural schematic suitable for implementing vehicle control devices according to embodiments of this application. Vehicle control devices in embodiments of this application may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The vehicle control device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of this application.

[0113] like Figure 5 As shown, the vehicle control device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the vehicle control device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the vehicle control equipment to communicate wirelessly or wiredly with other devices to exchange data. Although the figures show vehicle control equipment with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0114] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0115] The vehicle control device provided in this application, employing the vehicle control method described in the above embodiments, can solve the technical problem that related technologies do not reasonably limit the vehicle speed based on the driver's actual situation. Compared with the prior art, the beneficial effects of the vehicle control device provided in this application are the same as those of the vehicle control method provided in the above embodiments, and other technical features of this vehicle control device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0116] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0117] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0118] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the vehicle control method in the above embodiments.

[0119] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0120] The aforementioned computer-readable storage medium may be included in the vehicle control equipment; or it may exist independently and not be installed in the vehicle control equipment.

[0121] The aforementioned computer-readable storage medium carries one or more programs that, when executed by a vehicle control device, cause the vehicle control device to: construct speed correction coefficients based on driver information, surrounding environment information, and / or the driver's historical driving records, wherein the speed correction coefficients include at least one of vehicle familiarity coefficients, driving experience coefficients, fatigue coefficients, vehicle surrounding environment coefficients, and driving habit coefficients; obtain coefficient weight values ​​corresponding to each speed correction coefficient; construct speed correction parameters based on the coefficient weight values ​​and the speed correction coefficients; determine a speed upper limit value based on the speed correction parameters and a reference speed; and perform speed control on the vehicle based on the speed upper limit value.

[0122] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Python, Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0123] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0124] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0125] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described vehicle control method. This addresses the technical problem that related technologies do not reasonably limit vehicle speed based on the driver's actual situation. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the vehicle control method provided in the above embodiments, and will not be elaborated upon here.

[0126] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the vehicle control method described above.

[0127] The computer program product provided in this application can solve the technical problem that related technologies do not reasonably limit the speed of the vehicle based on the actual situation of the driver. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the vehicle control method provided in the above embodiments, and will not be repeated here.

[0128] All user-related data involved in this application (such as user privacy data, user behavior data, etc.) were obtained with the user's permission or consent; that is to say, when this application is used in a specific product or technology, user permission is required to obtain and process the relevant data, and the processing of the relevant data must comply with the relevant laws, regulations and regulatory standards of the relevant countries and regions.

[0129] The above description is only a part of the embodiments of this application and does not limit the scope of protection of this application. All equivalent structural transformations made under the technical concept of this application and using the content of this application specification and drawings, or direct / indirect applications in other related technical fields, are included in the scope of protection of this application.

Claims

1. A vehicle control method characterized by, The vehicle control method comprises: constructing a speed correction coefficient according to driver information, surrounding environment information and / or driver historical driving records, the speed correction coefficient comprising at least one of a vehicle familiarity coefficient, a driving experience coefficient, a fatigue degree coefficient, a vehicle surrounding environment coefficient and a driving habit coefficient; obtaining a coefficient weight value corresponding to each speed correction coefficient; constructing a speed correction parameter according to the coefficient weight value and the speed correction coefficient; determining a speed upper limit value according to the speed correction parameter and a reference speed, and performing speed control on the vehicle based on the speed upper limit value.

2. The vehicle control method according to claim 1, characterized by, Before the step of constructing the speed correction coefficient according to the driver information, the surrounding environment information and / or the driver historical driving records, the method further comprises: obtaining speed sample data, the speed sample data comprising a maximum speed corresponding to each time when the driver actually drives the vehicle within a preset time period; constructing at least one weight combination according to a weight adjustment step and weight constraint information; calculating a sample standard deviation corresponding to each weight combination based on the speed sample data; selecting a target combination from the weight combinations according to the sample standard deviation; if the weight adjustment step is less than or equal to a preset step threshold, marking the target combination as an optimal combination; correspondingly, the step of obtaining the coefficient weight value corresponding to each speed correction coefficient comprises: extracting the coefficient weight value corresponding to each speed correction coefficient from the optimal combination.

3. The vehicle control method according to claim 2, characterized by, The step of constructing at least one weight combination according to the weight adjustment step and the weight constraint information comprises: determining a weight value sum and a weight value range corresponding to each speed correction coefficient according to the weight constraint information; determining a selectable weight value corresponding to each speed correction coefficient according to the weight value range and the weight adjustment step; arranging and combining the selectable weight values corresponding to each speed correction coefficient to construct a plurality of weight combinations; calculating a combination weight sum corresponding to each weight combination by calculating a sum of the selectable weight values in each weight combination; taking the weight combination corresponding to the combination weight sum equal to the weight value sum as the weight combination.

4. The vehicle control method according to claim 2, characterized by, After the step of selecting the target combination from the weight combinations according to the sample standard deviation, the method further comprises: if the weight adjustment step is greater than a preset step threshold, reducing the weight adjustment step to obtain a new weight adjustment step; constructing at least one new weight combination according to the target combination, the new weight adjustment step and the weight constraint information, and returning to the step of calculating a sample standard deviation corresponding to each weight combination based on the speed sample data.

5. The vehicle control method according to claim 4, characterized by, The step of constructing at least one new weight combination according to the target combination, the new weight adjustment step and the weight constraint information comprises: extracting the coefficient weight value corresponding to each speed correction coefficient from the target combination; taking the coefficient weight value corresponding to each speed correction coefficient extracted from the target combination as a value center, and calculating a selectable weight value corresponding to each speed correction coefficient according to the new weight adjustment step; arranging and combining the selectable weight values corresponding to each speed correction coefficient to construct a plurality of weight combinations; Calculate the sum of the selectable weight values in each weight combination respectively to obtain the total combination coefficient corresponding to each weight combination; Take the weight combination corresponding to the total combination coefficient equal to the total weight value as the new weight combination.

6. The vehicle control method according to claim 2, characterized by Respectively calculate the sample standard deviation corresponding to each weight combination based on the speed sample data, including: Determine the sample reference speed and the sample speed correction coefficient according to the speed sample data; Determine the speed upper limit value corresponding to each weight combination according to the sample speed correction coefficient, the weight combination, and the sample reference speed; Calculate the sample standard deviation corresponding to each weight combination according to the speed upper limit value corresponding to each weight combination and the speed sample data.

7. The vehicle control method according to any one of claims 1 to 6, characterized by, Before determining the speed upper limit value according to the speed correction parameter and the reference speed, and controlling the speed of the vehicle based on the speed upper limit value, it further includes: Respectively calculate the speed upper limit value corresponding to each driver historical driving record, and obtain the historical driving speed corresponding to each driver historical driving record; Construct the personal correction coefficient according to the speed upper limit value and the historical driving speed; Correspondingly, the step of determining the speed upper limit value according to the speed correction parameter and the reference speed, and controlling the speed of the vehicle based on the speed upper limit value, includes: Determine the speed upper limit value according to the personal correction coefficient, the speed correction parameter and the reference speed, and control the speed of the vehicle based on the speed upper limit value.

8. A vehicle control device characterized by comprising: The vehicle control device includes: A construction module for constructing a speed correction coefficient according to driver information, surrounding environment information, and / or driver historical driving records, the speed correction coefficient including at least one of a vehicle familiarity coefficient, a driving experience coefficient, a fatigue degree coefficient, a vehicle surrounding environment coefficient, and a driving habit coefficient; An acquisition module for acquiring a coefficient weight value corresponding to each speed correction coefficient; A calculation module for constructing a speed correction parameter according to the coefficient weight value and the speed correction coefficient; A control module for determining a speed upper limit value according to the speed correction parameter and a reference speed, and controlling the speed of the vehicle based on the speed upper limit value.

9. A vehicle control apparatus characterized by comprising: The device includes a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the vehicle control method according to any one of claims 1 to 7.

10. A storage medium, characterized by The storage medium is a computer readable storage medium, and the storage medium stores a computer program, which is executed by a processor to implement the steps of the vehicle control method according to any one of claims 1 to 7.

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