A driving style determination method and device, electronic equipment and storage medium

Abstract

The application discloses a driving style determination method and device, electronic equipment and a storage medium. The method comprises the following steps: obtaining a plurality of driving parameters in a current statistical period; analyzing each driving parameter based on multiple dimensions to determine the driving style corresponding to each dimension for each driving parameter; and if the driving style corresponding to a specified number of dimensions for each driving parameter is an aggressive driving style, determining that the driving style in the current statistical period is an aggressive driving style. The above technical solution comprehensively and comprehensively analyzes each driving parameter based on multiple dimensions to determine the driving style of the driver according to the obtained multiple driving parameters, realizes comprehensive, comprehensive, accurate and efficient determination of the driving style of the driver, helps the vehicle to better adapt to the operation habit of the driver, more actively responds to the operation logic of the driver, and improves the safety, comfort and user experience of driving.

Description

Technical Field

[0001] The present invention relates to the field of automotive technology, and in particular to a method, apparatus, electronic device and storage medium for determining driving style. Background Technology

[0002] Due to differences in age, driving experience, and personality among drivers, even under the same traffic safety laws and regulations, there can be significant variations in driving habits and styles. Different driving styles represent different demands on vehicle power, thus raising issues of driving economy. Current methods for determining driver style primarily utilize advanced algorithms, such as neural network algorithms. However, high-level algorithms require large amounts of data input, which is difficult to meet, and due to their complexity, they are typically difficult to run in real-time on embedded systems in conventional vehicles. Summary of the Invention

[0003] This invention provides a driving style determination method, device, electronic device, and storage medium to accurately and efficiently determine the driver's driving style, which helps the vehicle better adapt to the driver's operating habits and respond more actively to the driver's operating logic.

[0004] In a first aspect, embodiments of the present invention provide a method for determining driving style, including:

[0005] The system acquires multiple driving parameters within the current statistical period, including the average following distance between the current vehicle and the vehicle in front, the average steering wheel angle, the average steering wheel angular velocity, the number of times the accelerator pedal reaches a specified opening, the average engine speed, the vehicle speed variance, and the number of gear shifts.

[0006] The driving parameters are analyzed from multiple dimensions to determine the driving style corresponding to each driving parameter for each dimension.

[0007] If all driving parameters correspond to an aggressive driving style for a specified number of dimensions, then the driving style in the current statistical period is determined to be an aggressive driving style.

[0008] In a second aspect, embodiments of the present invention provide a driving style determination device, comprising:

[0009] The acquisition module is used to acquire multiple driving parameters within the current statistical period. These driving parameters include the average following distance between the current vehicle and the vehicle in front, the average steering wheel angle, the average steering wheel angular velocity, the number of times the accelerator pedal reaches a specified opening, the average engine speed, the vehicle speed variance, and the number of gear shifts.

[0010] The analysis module is used to analyze each driving parameter based on multiple dimensions to determine the driving style corresponding to each driving parameter for each dimension.

[0011] The determination module is used to determine the driving style in the current statistical period as aggressive driving style if all driving parameters correspond to an aggressive driving style for a specified number of dimensions.

[0012] Thirdly, embodiments of the present invention provide an electronic device, including:

[0013] At least one processor; and

[0014] A memory that is communicatively connected to at least one processor; wherein,

[0015] The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to perform the driving style determination method as described in the first aspect.

[0016] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the driving style determination method as described in the first aspect.

[0017] This invention provides a method, device, electronic device, and storage medium for determining driving style. First, multiple driving parameters within the current statistical period are acquired. These parameters include the average following distance between the current vehicle and the vehicle in front, the average steering wheel angle, the average steering wheel angular velocity, the number of times the accelerator pedal reaches a specified opening, the average engine speed, the vehicle speed variance, and the number of gear shifts. Then, each driving parameter is analyzed based on multiple dimensions to determine the driving style corresponding to each dimension. Finally, if all driving parameters correspond to an aggressive driving style for a specified number of dimensions, the driving style within the current statistical period is determined to be an aggressive driving style. This technical solution, by acquiring multiple driving parameters and comprehensively analyzing them across multiple dimensions to determine the driver's driving style, achieves a comprehensive, accurate, and efficient determination of the driver's driving style. This helps the vehicle better adapt to the driver's operating habits and respond more actively to the driver's operating logic, improving driving safety, comfort, and user experience.

[0018] It should be understood that the description in this section is not intended to identify key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0019] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0020] Figure 1 This is a flowchart of a method for determining driving style provided in Embodiment 1 of the present invention;

[0021] Figure 2 This is a flowchart of a method for determining driving style provided in Embodiment 2 of the present invention;

[0022] Figure 3 This is a schematic diagram of a process for obtaining multiple driving parameters within the current statistical period, provided in Embodiment 2 of the present invention;

[0023] Figure 4 This is a flowchart illustrating a method for determining driving style according to Embodiment 2 of the present invention;

[0024] Figure 5 This is a schematic diagram of a driving style determination device provided in Embodiment 3 of the present invention;

[0025] Figure 6 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0026] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, the embodiments and features described herein can be combined with each other unless otherwise specified. It should also be noted that, for ease of description, only the parts relevant to the present invention are shown in the drawings, not the entire structure.

[0027] Before discussing the exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the steps as sequential processes, many of these steps can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the steps can be rearranged. The process can be terminated when its operation is complete, but may also have additional steps not included in the figures. The process can correspond to a method, function, procedure, subroutine, subroutine, etc.

[0028] It should be noted that the concepts of "first" and "second" mentioned in the embodiments of the present invention are only used to distinguish different devices, modules, units or other objects, and are not used to limit the order of functions performed by these devices, modules, units or other objects or their interdependencies.

[0029] Example 1

[0030] Figure 1 This is a flowchart of a driving style determination method provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where a driver's driving style needs to be determined. Specifically, the driving style determination method can be executed by a driving style determination device, which can be implemented through software and / or hardware and integrated into an electronic device. Further, the electronic device includes, but is not limited to, desktop computers, laptops, smartphones, and servers.

[0031] like Figure 1 As shown, the method specifically includes the following steps:

[0032] S110. Obtain multiple driving parameters within the current statistical period. These parameters include the average following distance between the current vehicle and the vehicle in front, the average steering wheel angle, the average steering wheel angular velocity, the number of times the accelerator pedal reaches a specified opening, the average engine speed, the vehicle speed variance, and the number of gear shifts.

[0033] In this embodiment, the current statistical period can be understood as the time range for currently statistically analyzing driving parameters, such as five minutes. The current vehicle can be understood as the vehicle whose driving parameters need to be obtained. The average following distance can be understood as the average distance between the current vehicle and the vehicle in front (which can be one or more) within the current statistical period. The average steering wheel angle can be understood as the average steering wheel rotation angle measured based on the position of the steering wheel when the vehicle is traveling straight (with the average steering angle of the left and right steering wheels at zero). The average steering wheel angular velocity can be understood as the average speed at which the driver turns the steering wheel. The vehicle speed variance can be understood as a variable characterizing vehicle speed fluctuations. Reaching a specified accelerator pedal opening can be understood as a preset accelerator pedal opening, such as reaching 100% opening, i.e., pressing the accelerator pedal all the way down. The average engine speed can be understood as the average number of revolutions per minute of the engine crankshaft.

[0034] Various driving parameters can be acquired through onboard sensors or cameras, such as speed sensors, yaw rate sensors, or dual-axis acceleration sensors, etc., and this embodiment is not limited to any particular type. Alternatively, various driving parameters can be acquired through a controller.

[0035] S120. Analyze each driving parameter based on multiple dimensions to determine the driving style corresponding to each driving parameter for each dimension.

[0036] In this embodiment, driving style can be understood as the relatively stable behavioral characteristics exhibited by a driver when manipulating a vehicle. For example, driving style can be aggressive, cautious, confident, or calm. It should be noted that driving parameters are important factors in determining driving style. For instance, drivers with an aggressive driving style tend to have a closer average following distance, a larger average steering wheel angle and speed, more frequent accelerator pedal engagements, a higher average engine speed, a larger vehicle speed variance, and more gear shifts within the statistical period. In other words, the smaller the average following distance, the larger the average steering wheel angle, the faster the average steering wheel speed, the more frequent the accelerator pedal engagements, the higher the average engine speed, the larger the vehicle speed variance, and / or the more gear shifts, the more likely the driver has an aggressive driving style.

[0037] Specifically, a comprehensive analysis of various driving parameters can be performed based on multiple dimensions to determine driving style. For example, a machine learning model can be constructed to determine the driving style corresponding to each dimension. This machine learning model can be a decision tree model, a fully connected neural network model, or a convolutional neural network model, etc., and this embodiment is not limited to any particular model. Furthermore, a pre-set scoring table for each dimension can be used to score each driving parameter, thereby evaluating whether the driving operation in each dimension is aggressive, thus comprehensively and accurately determining the driving style corresponding to each dimension. These multiple dimensions can be, for example, the specific numerical level (score) of each driving parameter, which can reflect the aggressiveness of the driver's driving operation; the frequency of change of each driving parameter; the degree of fluctuation of each driving parameter; the coordination between different driving parameters (e.g., a large accelerator pedal opening and a large steering wheel turning angle are likely to indicate an aggressive driving style); or the order in which different driving parameters reach an aggressive level, etc.

[0038] S130. If each driving parameter corresponds to an aggressive driving style for a specified number of dimensions, then the driving style in the current statistical period is determined to be an aggressive driving style.

[0039] In this embodiment, the aggressive driving style can be understood as the driver's overall driving operation being more aggressive and the vehicle driving being relatively unstable. Drivers with an aggressive driving style are prone to driving behaviors such as speeding, frequent lane changes, sudden braking, or following other vehicles closely.

[0040] For example, when three dimensions are used to analyze each driving parameter to determine the driving style, if the analysis of each driving parameter determines that each driving parameter satisfies two or more dimensions as an aggressive driving style, then the driving style in the current statistical period can be determined as an aggressive driving style.

[0041] Optionally, if the number of dimensions corresponding to the driving style of each driving parameter as aggressive driving style is less than the specified number, then the driving style in the current statistical period is determined to be non-aggressive driving style.

[0042] In this embodiment, the non-aggressive driving style can be a cautious or peaceful style, etc.

[0043] For example, when three dimensions are used to analyze each driving parameter to determine the driving style, if the analysis of each driving parameter determines that each driving parameter satisfies one or less of the dimensions and belongs to an aggressive driving style, then the driving style in the current statistical period can be determined as a non-aggressive driving style.

[0044] It should be noted that drivers with aggressive driving styles tend to frequently change lanes, accelerate and decelerate abruptly, and follow other vehicles closely, which increases the probability of accidents. Therefore, it is more important to identify aggressive driving styles to monitor and educate drivers on their driving behavior. This will facilitate timely warnings and prompts for drivers to take appropriate measures to automatically maintain vehicle stability or prevent potential hazards, thereby improving driving safety, comfort, and user experience.

[0045] This invention provides a method for determining driving style. This method acquires multiple driving parameters within the current statistical period, including the average following distance between the current vehicle and the vehicle in front, the average steering wheel angle, the average steering wheel angular velocity, the number of times the accelerator pedal reaches a specified opening, the average engine speed, the vehicle speed variance, and the number of gear shifts. Based on multiple dimensions, each driving parameter is analyzed to determine the driving style corresponding to each dimension. If each driving parameter corresponds to an aggressive driving style for a specified number of dimensions, then the driving style within the current statistical period is determined to be an aggressive driving style. This technical solution, by acquiring multiple driving parameters and comprehensively analyzing them across multiple dimensions, determines the driver's driving style. This comprehensive, accurate, and efficient method helps the vehicle better adapt to the driver's operating habits and respond more actively to the driver's operating logic, thereby improving driving safety, comfort, and user experience.

[0046] Example 2

[0047] Figure 2 This is a flowchart of a driving style determination method provided in Embodiment 2 of the present invention. This embodiment is a refinement based on the above embodiments, analyzing each driving parameter based on multiple dimensions to specifically describe the driving style corresponding to each driving parameter for each dimension. It should be noted that technical details not described in detail in this embodiment can be found in any of the above embodiments.

[0048] Specifically, such as Figure 2 As shown, the method specifically includes the following steps:

[0049] S210. Obtain multiple driving parameters within the current statistical period. These parameters include the average following distance between the current vehicle and the vehicle in front, the average steering wheel angle, the average steering wheel angular velocity, the number of times the accelerator pedal reaches a specified opening, the average engine speed, the vehicle speed variance, and the number of gear shifts.

[0050] S220. Before analyzing each driving parameter based on multiple dimensions, the obtained driving parameters are preprocessed.

[0051] Specifically, before analyzing various driving parameters based on multiple dimensions, the acquired driving parameters can be preprocessed to retain valid parameters. Preprocessing can involve removing invalid parameters corresponding to a set vehicle state to update the driving parameters, or removing the duration occupied by the set vehicle state to update the current statistical period. The set vehicle state includes at least one of the following: a vehicle speed of zero, the vehicle in park, or the vehicle in neutral.

[0052] In this embodiment, invalid parameters can be understood as the driving parameters corresponding to the current vehicle's speed being zero, in park, or in neutral. Invalid parameters corresponding to the set vehicle state are removed from the current statistical period to update the driving parameters. The duration occupied can be understood as the time spent when the current vehicle is in a zero-speed, park, or neutral state. The duration occupied by the set vehicle state is removed from the current statistical period to update the current statistical period. The removal of invalid parameters and the duration occupied can be achieved using a machine learning model. Based on this, driving parameters irrelevant or of little significance in determining driving style can be removed, retaining valid driving parameters. Driving parameter analysis is then performed on the time periods containing valid parameters to improve the accuracy of determining driving style and avoid the adverse effects of invalid driving parameters on the judgment results.

[0053] S230: Analyze each driving parameter based on multiple dimensions to determine the driving style corresponding to each driving parameter for each dimension.

[0054] Optionally, for the first dimension, the corresponding rating coefficients for each driving parameter are obtained by looking up a table based on each driving parameter; the mean of the corresponding rating coefficients for each driving parameter is calculated as the rating coefficient for the first dimension; if the rating coefficient for the first dimension is greater than a set threshold, then the driving style corresponding to the first dimension is an aggressive driving style.

[0055] In this embodiment, the corresponding rating coefficients for each driving parameter can be obtained by looking up a table. When the values ​​of each driving parameter within the current statistical period are within the corresponding threshold, the corresponding rating coefficients for each driving parameter are obtained by looking up the table. The unit of the rating coefficients can be %, and the table can be a two-dimensional coordinate system, with the horizontal axis representing the values ​​of each driving parameter and the vertical axis representing the corresponding rating coefficients for each driving parameter.

[0056] Specifically, after acquiring various driving parameters within the current statistical period through onboard sensors or cameras, preprocessing is performed to remove invalid parameters and their durations within the current statistical period. The corresponding rating coefficients for each driving parameter are obtained by looking up a table, and the average value of the corresponding rating coefficients for each driving parameter is calculated and used as the rating coefficient for the first dimension. If the rating coefficient for the first dimension is greater than a set threshold, then the driving style corresponding to the first dimension is an aggressive driving style.

[0057] For example, after acquiring various driving parameters within the current statistical period through onboard sensors or cameras, the acquired driving parameters are preprocessed to remove invalid parameters and their durations within the current statistical period. The corresponding scoring coefficient for each driving parameter is obtained by looking up a table, specifically as follows: when the average following distance threshold A1 ≤ the average following distance A within the current statistical period ≤ the average following distance threshold A2, the average following distance scoring coefficient (unit: %) W A By looking up the MAP table A (The horizontal axis represents the average following distance A, and the vertical axis represents the average following distance rating coefficient W) A The steering wheel average angle rating coefficient (unit: %) is obtained when the steering wheel average angle threshold B1 ≤ steering wheel average angle B in the current statistical period ≤ steering wheel average angle threshold B2. B By looking up the MAP table B (The horizontal axis represents the average steering wheel angle B, and the vertical axis represents the average steering wheel angle rating coefficient W.) B The steering wheel average angular velocity rating coefficient (unit: %) is obtained when the steering wheel average angular velocity threshold C1 ≤ steering wheel average angular velocity C in the current statistical period ≤ steering wheel average angular velocity threshold C2. C By looking up the MAP table C (The horizontal axis represents the average steering wheel angular velocity C, and the vertical axis represents the rating coefficient W for the average steering wheel angular velocity.) C The accelerator pedal reaching a specified opening state is obtained when the threshold value D1 is less than or equal to the threshold value D2 of the number of times the accelerator pedal reaches a specified opening state in the current statistical period, and the accelerator pedal reaching a specified opening state score coefficient (unit: %) W is calculated. D By looking up the MAP table D(The horizontal axis represents the number of times the accelerator pedal reaches the specified opening, D; the vertical axis represents the scoring coefficient W for the number of times the accelerator pedal reaches the specified opening.) D The engine average speed score coefficient (unit: %) is obtained when the engine average speed threshold E1 ≤ engine average speed E in the current statistical period ≤ engine average speed threshold E2. E By looking up the MAP table E (Horizontal axis represents the engine's average speed E, and vertical axis represents the engine's average speed rating coefficient W) E The vehicle speed variance score coefficient (unit: %) W is obtained when the vehicle speed variance threshold F1 ≤ vehicle speed variance F in the current statistical period ≤ vehicle speed variance threshold F2. F By looking up the MAP table F (The horizontal axis represents the vehicle speed variance F, and the vertical axis represents the vehicle speed variance rating coefficient W) F The gear shift frequency rating coefficient (unit: %) W is obtained when the gear shift frequency threshold G1 ≤ gear shift frequency G in the current statistical period ≤ gear shift frequency G2. G By looking up the MAP table G (The horizontal axis represents the number of gear shifts (G), and the vertical axis represents the gear shift number rating coefficient (W)) G The average following distance rating coefficient W is obtained by calculating the average rating coefficient for each driving parameter. A Steering wheel average turning angle rating coefficient W B Steering wheel average angular velocity rating coefficient W C The scoring coefficient W is the number of times the accelerator pedal reaches the specified opening degree. D Engine average speed rating coefficient W E Vehicle speed variance scoring coefficient W F , Gearbox shift frequency rating coefficient W G The average value W is used as the scoring coefficient for the first dimension. If the scoring coefficient W for the first dimension is greater than the set threshold W1, then the driving parameter corresponds to the aggressive driving style for the first dimension.

[0058] Optionally, for the second dimension, the corresponding scoring coefficients for each driving parameter can be obtained by looking up a table based on each driving parameter;

[0059] Determine whether the statistical characteristics of the corresponding driving parameter are activated based on the corresponding scoring coefficients for each driving parameter;

[0060] If the number of driving parameters activated by the statistical feature is greater than or equal to the set number, then the driving style corresponding to the second dimension is an aggressive driving style.

[0061] Specifically, after acquiring various driving parameters within the current statistical period through onboard sensors or cameras, the acquired driving parameters are preprocessed to remove invalid parameters and their durations within the current statistical period. The corresponding scoring coefficients for each driving parameter are obtained by looking up a table. Then, based on the corresponding scoring coefficients for each driving parameter, it is determined whether the statistical characteristics of the corresponding driving parameter are activated. The criterion for determining whether it is activated can be whether the corresponding scoring coefficient of each driving parameter is greater than a specified value (e.g., 50%). If the number of driving parameters with activated statistical characteristics is greater than or equal to a set number, where the set number can be 4, then the driving style corresponding to the second dimension of the driving parameter is an aggressive driving style.

[0062] For example, after acquiring various driving parameters within the current statistical period through onboard sensors or cameras, the acquired driving parameters are preprocessed to remove invalid parameters and their durations within the current statistical period. The corresponding scoring coefficient for each driving parameter is obtained by looking up a table. Then, based on the corresponding scoring coefficient, it is determined whether the statistical characteristics of the corresponding driving parameter are activated. Specifically, when the average following distance scoring coefficient W... A When it exceeds 50%, the statistical feature of average following distance is determined to be activated; when the average steering wheel angle rating coefficient W... B When it exceeds 50%, the statistical feature of the average steering wheel angle is determined to be activated; when the average steering wheel angular velocity rating coefficient W... C When it exceeds 50%, the statistical feature of the average steering wheel angular velocity is determined to be activated; the scoring coefficient W is determined by the number of times the accelerator pedal reaches a specified opening. D When the percentage is greater than 50%, the statistical feature of the number of times the accelerator pedal reaches the specified opening degree is activated; when the engine average speed rating coefficient W... E When it exceeds 50%, the statistical feature of engine average speed is determined to be activated; when the vehicle speed variance score coefficient W F When it exceeds 50%, the statistical feature of the vehicle speed variance is activated; when the gearbox shift frequency rating coefficient W... G If the number of shifts is greater than 50%, the transmission shift count statistical feature is activated. If the number of driving parameters whose statistical features are activated is greater than or equal to 4, then the driving style corresponding to the second dimension is an aggressive driving style.

[0063] Optionally, for the third dimension, the corresponding scoring coefficients for each driving parameter are obtained by looking up a table based on each driving parameter; the corresponding statistical features of each driving parameter are then determined to be activated based on their respective scoring coefficients; if the activation order of the statistical features meets the preset order, then the driving style corresponding to the third dimension is an aggressive driving style.

[0064] The preset order includes at least one of the following:

[0065] The statistical feature of average following distance is activated first, and the statistical feature of the number of times the accelerator pedal reaches a specified opening degree is activated then.

[0066] The statistical characteristics of the engine average speed are activated first, and then the statistical characteristics of the vehicle speed variance are activated.

[0067] The statistical features of the vehicle speed variance are activated first, followed by the statistical features of the number of gear shifts in the transmission.

[0068] Specifically, after acquiring various driving parameters within the current statistical period through onboard sensors or cameras, the acquired driving parameters are preprocessed to remove invalid parameters and their durations within the current statistical period. The corresponding rating coefficients for each driving parameter are obtained by looking up a table. Then, based on the corresponding rating coefficients of each driving parameter, it is determined whether the statistical features of the corresponding driving parameter are activated. The criteria for determining whether they are activated can be whether the corresponding rating coefficients of each driving parameter are greater than a specified value (e.g., 50%). If the activation order of the statistical features meets one of the preset orders, then the driving style corresponding to the third dimension of the driving parameter is an aggressive driving style.

[0069] One possible approach is to determine whether a driver's driving style is aggressive by comprehensively considering the above three dimensions. For example, if the corresponding score coefficients for each driving parameter satisfy the criteria for aggressive driving style across two or more dimensions, then the driver's driving style is determined to be aggressive within the current statistical period; otherwise, it is considered non-aggressive. The current statistical period ends, and the next statistical period begins.

[0070] It should be noted that the step of obtaining the corresponding scoring coefficients for each driving parameter by looking up the table in the first, second, and third dimensions can be completed in one go; the step of determining whether the statistical features of the corresponding driving parameters are activated based on the scoring coefficients in the second and third dimensions can be completed in one go, and the activation of the statistical features of the corresponding driving parameters can be determined sequentially in a certain order.

[0071] For example, firstly, the driving parameters within the current statistical period are acquired through onboard sensors or cameras. Then, the acquired driving parameters are preprocessed to remove invalid parameters and their durations within the current statistical period. The corresponding rating coefficients for each driving parameter are obtained by looking up a table. Then, the average value of the corresponding rating coefficients for each driving parameter is calculated. On the one hand, the driving style can be determined from the first dimension. On the other hand, the driving style can be determined from the second dimension based on the corresponding rating coefficients for each driving parameter. At the same time, the activation order of the statistical features can also be determined. Then, the driving style can be determined from the third dimension.

[0072] Optionally, the corresponding scoring coefficients for each driving parameter can be obtained by looking up a table, including:

[0073] If the average following distance is within the preset distance range, the first rating coefficient is obtained by looking up the table based on the average following distance;

[0074] If the average steering wheel angle is within the preset angle range, the second scoring coefficient is obtained by looking up the table based on the average steering wheel angle.

[0075] If the average steering wheel angular velocity is within the preset angular velocity range, the third scoring coefficient is obtained by looking up the table based on the average steering wheel angular velocity.

[0076] If the number of times the accelerator pedal reaches the specified opening is within the preset number of times, then the fourth scoring coefficient is obtained by looking up the table based on the number of times the accelerator pedal reaches the specified opening.

[0077] If the average engine speed is within the preset speed range, the fifth scoring coefficient is obtained by looking up the table based on the average engine speed.

[0078] If the vehicle speed variance value is within the preset variance value range, the sixth scoring coefficient is obtained by looking up the table based on the vehicle speed variance value;

[0079] If the number of gear shifts in the transmission is within the preset range, the seventh scoring coefficient is obtained by looking up the number of gear shifts in the table.

[0080] S240. If each driving parameter corresponds to an aggressive driving style for a specified number of dimensions, then the driving style in the current statistical period is determined to be an aggressive driving style.

[0081] For example, Figure 3 This is a flowchart illustrating a process for obtaining multiple driving parameters within the current statistical period, as provided in Embodiment 2 of the present invention. Figure 3As shown, the controller can acquire the average following distance, average steering wheel angle, average steering wheel angular velocity, number of times the accelerator pedal reaches a specified opening, average engine speed, vehicle speed variance, and transmission shift count. The order in which these driving parameters are acquired can be based on a specific time or pattern, or it can be acquired randomly; this embodiment does not impose any limitations on this.

[0082] For example, Figure 4 This is a flowchart illustrating a method for determining driving style according to Embodiment 2 of the present invention, as shown below. Figure 4 As shown, after starting, multiple driving parameters within the current statistical period are acquired. These parameters include the average following distance between the current vehicle and the vehicle in front, the average steering wheel angle, the average steering wheel angular velocity, the number of times the accelerator pedal reaches a specified opening, the average engine speed, the vehicle speed variance, and the number of gear shifts. The next step is to preprocess the acquired driving parameters, removing invalid parameters corresponding to the set vehicle state from the current statistical period and removing the time occupied by the set vehicle state from the current statistical period. The next step is to analyze each driving parameter based on multiple dimensions to determine the driving style corresponding to each dimension. After determining the driving style, the current statistical period ends, and the next statistical period begins, finally ending the process.

[0083] The second embodiment of this invention provides a method for determining driving style, which is a refinement of the above embodiments. First, multiple driving parameters within the current statistical period are acquired. These parameters include the average following distance between the current vehicle and the vehicle in front, the average steering wheel angle, the average steering wheel angular velocity, the number of times the accelerator pedal reaches a specified opening, the average engine speed, the vehicle speed variance, and the number of gear shifts. The acquired driving parameters are then preprocessed to remove invalid parameters and their durations within the current statistical period, ensuring the accuracy and reliability of the driving parameters. Next, each driving parameter is analyzed based on multiple dimensions to determine the driving style corresponding to each dimension. These multiple dimensions may include a first dimension, a second dimension, or a third dimension, ensuring the comprehensiveness and accuracy of the driving style determination. Finally, if the driving style corresponding to each driving parameter for a specified number of dimensions is an aggressive driving style, then the driving style within the current statistical period is determined to be an aggressive driving style. The above technical solution acquires multiple driving parameters and performs a comprehensive analysis of each parameter across multiple dimensions to determine the driver's driving style. This comprehensive, accurate, and efficient determination of the driver's driving style helps the vehicle better adapt to the driver's operating habits, responds more actively to the driver's operating logic, and improves driving safety, comfort, and user experience.

[0084] Example 3

[0085] Figure 5 This is a schematic diagram of a driving style determination device provided in Embodiment 3 of the present invention. This device can execute the driving style determination method provided in this embodiment of the present invention. The driving style determination device provided in this embodiment includes:

[0086] The acquisition module 310 is used to acquire multiple driving parameters within the current statistical period. These driving parameters include the average following distance between the current vehicle and the vehicle in front, the average steering wheel angle, the average steering wheel angular velocity, the number of times the accelerator pedal reaches a specified opening, the average engine speed, the vehicle speed variance, and the number of gear shifts.

[0087] Analysis module 320 is used to analyze each driving parameter based on multiple dimensions to determine the driving style corresponding to each driving parameter for each dimension;

[0088] The determination module 330 is used to determine the driving style in the current statistical period as an aggressive driving style if the driving style corresponding to each driving parameter for a specified number of dimensions is an aggressive driving style.

[0089] Embodiment 3 of this invention provides a driving style determination device. This device acquires multiple driving parameters within the current statistical period, including the average following distance between the current vehicle and the vehicle in front, the average steering wheel angle, the average steering wheel angular velocity, the number of times the accelerator pedal reaches a specified opening, the average engine speed, the vehicle speed variance, and the number of gear shifts. It analyzes each driving parameter across multiple dimensions to determine the driving style corresponding to each parameter for each dimension. If each driving parameter corresponds to an aggressive driving style for a specified number of dimensions, then the driving style within the current statistical period is determined to be an aggressive driving style. This technical solution, by acquiring multiple driving parameters and comprehensively analyzing them across multiple dimensions to determine the driver's driving style, achieves a comprehensive, accurate, and efficient determination of the driver's driving style. This helps the vehicle better adapt to the driver's operating habits, respond more actively to the driver's operating logic, and improve driving safety, comfort, and user experience.

[0090] Optionally, if the number of dimensions corresponding to the driving style of each driving parameter as aggressive driving style is less than the specified number, then the driving style in the current statistical period is determined to be non-aggressive driving style.

[0091] Optionally, before analyzing each driving parameter based on multiple dimensions, the following may also be included:

[0092] Invalid parameters corresponding to the set vehicle state are removed to update each driving parameter;

[0093] Remove the time spent on setting vehicle status to update the current statistical period;

[0094] The vehicle status settings include at least one of the following: zero speed, vehicle in parking gear, or vehicle in neutral gear.

[0095] Optionally, the analysis module 320 specifically includes:

[0096] The first dimension analysis unit is used to obtain the corresponding scoring coefficients for each driving parameter by looking up a table based on each driving parameter for the first dimension.

[0097] Calculate the mean of the rating coefficients for each driving parameter, and use it as the rating coefficient for the first dimension.

[0098] If the rating coefficient corresponding to the first dimension is greater than the set threshold, then the driving parameter corresponds to an aggressive driving style for the first dimension.

[0099] The second dimension analysis unit is used to obtain the corresponding scoring coefficients for each driving parameter by looking up a table based on each driving parameter for the second dimension.

[0100] Determine whether the statistical characteristics of the corresponding driving parameter are activated based on the corresponding scoring coefficients for each driving parameter;

[0101] If the number of driving parameters activated by the statistical feature is greater than or equal to the set number, then the driving style corresponding to the second dimension is an aggressive driving style.

[0102] The third dimension analysis unit is used to obtain the corresponding scoring coefficients for each driving parameter by looking up a table based on each driving parameter for the third dimension.

[0103] The statistical characteristics of each driving parameter are determined sequentially based on the corresponding scoring coefficients.

[0104] If the activation order of the statistical features meets the preset order, then the driving style corresponding to the third dimension of the driving parameters is an aggressive driving style.

[0105] The preset order includes at least one of the following:

[0106] The statistical feature of average following distance is activated first, and the statistical feature of the number of times the accelerator pedal reaches a specified opening degree is activated then.

[0107] The statistical characteristics of the engine average speed are activated first, and then the statistical characteristics of the vehicle speed variance are activated.

[0108] The statistical features of the vehicle speed variance are activated first, followed by the statistical features of the number of gear shifts in the transmission.

[0109] Optionally, the corresponding scoring coefficients for each driving parameter can be obtained by looking up a table, including:

[0110] If the average following distance is within the preset distance range, the first rating coefficient is obtained by looking up the table based on the average following distance;

[0111] If the average steering wheel angle is within the preset angle range, the second scoring coefficient is obtained by looking up the table based on the average steering wheel angle.

[0112] If the average steering wheel angular velocity is within the preset angular velocity range, the third scoring coefficient is obtained by looking up the table based on the average steering wheel angular velocity.

[0113] If the number of times the accelerator pedal reaches the specified opening is within the preset number of times, then the fourth scoring coefficient is obtained by looking up the table based on the number of times the accelerator pedal reaches the specified opening.

[0114] If the average engine speed is within the preset speed range, the fifth scoring coefficient is obtained by looking up the table based on the average engine speed.

[0115] If the vehicle speed variance value is within the preset variance value range, the sixth scoring coefficient is obtained by looking up the table based on the vehicle speed variance value;

[0116] If the number of gear shifts in the transmission is within the preset range, the seventh scoring coefficient is obtained by looking up the number of gear shifts in the table.

[0117] The driving style determination device provided in Embodiment 3 of the present invention can be used to execute the driving style determination method provided in any of the above embodiments, and has corresponding functions and beneficial effects.

[0118] Example 4

[0119] Figure 6 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device 10 can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, user equipment, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0120] like Figure 6As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0121] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows communication node 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks and wireless networks.

[0122] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the driving style determination method.

[0123] In some embodiments, the driving style determination method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the driving style determination method by any other suitable means (e.g., by means of firmware).

[0124] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0125] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0126] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on 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 fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0127] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device 10, which includes: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device 10. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0128] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0129] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0130] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0131] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A driving style determination method characterized by comprising: include: The system acquires multiple driving parameters within the current statistical period, including the average following distance between the current vehicle and the vehicle in front, the average steering wheel angle, the average steering wheel angular velocity, the number of times the accelerator pedal reaches a specified opening degree, the average engine speed, the vehicle speed variance, and the number of gear shifts in the transmission. The driving parameters are analyzed from multiple dimensions to determine the driving style corresponding to each driving parameter for each dimension. If all the driving parameters correspond to an aggressive driving style for a specified number of dimensions, then the driving style in the current statistical period is determined to be an aggressive driving style. The step of analyzing each driving parameter based on multiple dimensions to determine the driving style corresponding to each driving parameter for each dimension includes: For the second dimension, the corresponding scoring coefficients for each driving parameter are obtained by looking up a table based on each driving parameter. The statistical characteristics of the corresponding driving parameters are determined based on the corresponding scoring coefficients of each driving parameter. If the number of driving parameters activated by the statistical feature is greater than or equal to the set number, then the driving style corresponding to the second dimension is an aggressive driving style. This includes analyzing each driving parameter based on multiple dimensions to determine the driving style corresponding to each driving parameter for each dimension, and further includes: For the third dimension, the corresponding scoring coefficients for each driving parameter are obtained by looking up a table based on each driving parameter. The statistical characteristics of each driving parameter are determined sequentially based on the corresponding scoring coefficients. If the activation order of the statistical features meets the preset order, then the driving parameter corresponds to the aggressive driving style of the third dimension. The preset order includes at least one of the following: The statistical feature of the average following distance is activated first, and the statistical feature of the number of times the accelerator pedal reaches a specified opening degree is activated then. The statistical characteristics of the engine average speed are activated first, and then the statistical characteristics of the vehicle speed variance are activated. The statistical features of the vehicle speed variance are activated first, and then the statistical features of the number of gear shifts in the transmission are activated.

2. The method according to claim 1, characterized in that, Also includes: If the number of dimensions corresponding to the driving style of each driving parameter that is an aggressive driving style is less than the specified number, then the driving style in the current statistical period is determined to be a non-aggressive driving style.

3. The method according to claim 1, characterized in that, Before analyzing each of the driving parameters based on multiple dimensions, the method further includes: Invalid parameters corresponding to the set vehicle state are removed to update the driving parameters. The time period occupied by the set vehicle status is removed to update the current statistical period; The vehicle state setting includes at least one of the following: zero speed, vehicle in parking gear, or vehicle in neutral gear.

4. The method according to claim 1, characterized in that, The analysis of each driving parameter based on multiple dimensions to determine the driving style corresponding to each driving parameter for each dimension includes: For the first dimension, the corresponding scoring coefficients for each driving parameter are obtained by looking up a table based on each driving parameter. Calculate the mean of the rating coefficients corresponding to each of the aforementioned driving parameters, and use it as the rating coefficient corresponding to the first dimension; If the rating coefficient corresponding to the first dimension is greater than the set threshold, then the driving parameter corresponds to an aggressive driving style for the driving style of the first dimension.

5. The method according to claim 1 or 4, characterized in that, The step of obtaining the corresponding scoring coefficient for each driving parameter by looking up a table for each driving parameter includes: If the average following distance is within a preset distance range, the first scoring coefficient is obtained by looking up the table based on the average following distance; If the average steering wheel angle is within the preset angle range, then the second scoring coefficient is obtained by looking up the table based on the average steering wheel angle. If the average steering wheel angular velocity is within the preset angular velocity range, then the third scoring coefficient is obtained by looking up the table based on the average steering wheel angular velocity; If the number of times the accelerator pedal reaches the specified opening degree is within the preset number of times, then the fourth scoring coefficient is obtained by looking up the table based on the number of times the accelerator pedal reaches the specified opening degree. If the average engine speed is within the preset speed range, the fifth scoring coefficient is obtained by looking up the table based on the average engine speed. If the vehicle speed variance value is within the preset variance value range, then the sixth scoring coefficient is obtained by looking up the table based on the vehicle speed variance value; If the number of gear shifts of the transmission is within the preset number of gear shifts, then the seventh scoring coefficient is obtained by looking up the number of gear shifts in the table.

6. A driving style determining device, characterized in that, include: The acquisition module is used to acquire multiple driving parameters within the current statistical period. These multiple driving parameters include the average following distance between the current vehicle and the vehicle in front, the average steering wheel angle, the average steering wheel angular velocity, the number of times the accelerator pedal reaches a specified opening degree, the average engine speed, the vehicle speed variance, and the number of gear shifts in the transmission. The analysis module is used to analyze each of the driving parameters based on multiple dimensions to determine the driving style corresponding to each of the driving parameters for each dimension; The determination module is used to determine that the driving style in the current statistical period is an aggressive driving style if the driving style corresponding to each of the driving parameters for a specified number of dimensions is an aggressive driving style. The analysis module specifically includes: The second dimension analysis unit is used to obtain the corresponding scoring coefficients for each driving parameter by looking up a table based on each driving parameter for the second dimension. The statistical characteristics of the corresponding driving parameters are determined based on the corresponding scoring coefficients of each driving parameter. If the number of driving parameters activated by the statistical feature is greater than or equal to the set number, then the driving style corresponding to the second dimension of the driving parameter is an aggressive driving style. The third dimension analysis unit is used to obtain the corresponding scoring coefficients for each of the driving parameters by looking up a table for each of the driving parameters in the third dimension. The statistical characteristics of each driving parameter are determined sequentially based on the corresponding scoring coefficients. If the activation order of the statistical features meets the preset order, then the driving parameter corresponds to the aggressive driving style of the third dimension. The preset order includes at least one of the following: The statistical feature of the average following distance is activated first, and the statistical feature of the number of times the accelerator pedal reaches a specified opening degree is activated then. The statistical characteristics of the engine average speed are activated first, and then the statistical characteristics of the vehicle speed variance are activated. The statistical features of the vehicle speed variance are activated first, and then the statistical features of the number of gear shifts in the transmission are activated.

7. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the driving style determination method as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the driving style determination method as described in any one of claims 1-5.

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