Passenger comfort determination method and device, storage medium and program product

Abstract

The invention discloses a passenger comfort determination method and device, a storage medium and a program product, and relates to the technical field of vehicles, and the method comprises the steps: obtaining vehicle driving data, and determining the impact score of the change of a vehicle driving state on the passenger comfort according to the vehicle driving data; the vehicle driving data can objectively reflect the change of the driving state of the vehicle in the driving process, and the change of the vehicle driving state can generate different influences on the comfort of the passengers, so that the influence score of the change of the vehicle driving state on the comfort of the passengers can be objectively determined through the vehicle driving data; the influence score reflects the objective influence of the running state of the vehicle on the comfort of the passengers; and determining a passenger comfort score according to the influence score, thereby eliminating the subjective feeling difference of individuals, and calculating to obtain a comfort score capable of objectively quantifying the real comfort of the passenger. Therefore, the technical problem that the real comfort degree of the passenger in the riding process cannot be objectively reflected can be solved.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a method, device, storage medium, and program product for determining passenger comfort. Background Technology

[0002] With the development of intelligent transportation systems and autonomous driving technology, more and more attention is being paid to the passenger experience.

[0003] However, traditional passenger comfort evaluations usually rely on questionnaires or subjective scoring, which cannot objectively reflect the true comfort level of passengers during their journey due to the large differences in individual subjective feelings. Summary of the Invention

[0004] The main objective of this application is to provide a method, device, storage medium, and program product for determining passenger comfort, aiming to solve the technical problem of being unable to objectively reflect the true comfort level of passengers during their journey.

[0005] To achieve the above objectives, this application proposes a method for determining passenger comfort, the method comprising:

[0006] Obtain vehicle driving data;

[0007] Based on the vehicle driving data, a score is determined on the impact of changes in vehicle driving status on passenger comfort.

[0008] Based on the impact score, a passenger comfort score is determined.

[0009] In one embodiment, the vehicle driving data includes acceleration in different directions, and the changes in the vehicle driving state include the stability of the vehicle in different directions and the frequency of instantaneous disturbances generated by the vehicle in different directions.

[0010] The step of determining the impact score of changes in vehicle driving status on passenger comfort based on the vehicle driving data includes:

[0011] Based on the acceleration in different directions, calculate the vehicle's stability in different directions and the frequency of instantaneous disturbances generated by the vehicle in different directions;

[0012] The impact score is determined based on the stability and the frequency.

[0013] In one embodiment, the step of calculating the vehicle's stability in different directions and the frequency of instantaneous disturbances generated by the vehicle in different directions based on the accelerations in those directions includes:

[0014] The acceleration in different directions within a preset first time period is integrated over time to obtain the integral values ​​corresponding to different directions. The integral values ​​corresponding to different directions reflect the stability of the vehicle in different directions.

[0015] Calculate the corresponding acceleration in different directions based on the acceleration in different directions within the preset first time period;

[0016] Based on the jerk corresponding to different directions and a preset first threshold, the frequency of instantaneous disturbances generated by the vehicle in different directions is calculated.

[0017] In one embodiment, the step of determining the passenger comfort score based on the influence score includes:

[0018] Obtain the driver's historical driving behavior and determine the type of the historical driving behavior;

[0019] Based on the mapping relationship between different types of driving behavior and correction coefficients, the target correction coefficient corresponding to the historical driving behavior is determined;

[0020] The impact score is corrected based on the target correction coefficient to obtain the passenger comfort score.

[0021] In one embodiment, the step of obtaining the driver's historical driving behavior includes:

[0022] Obtain the vehicle's identifier;

[0023] Based on the vehicle's identifier, query the profile data of the driver corresponding to the vehicle;

[0024] Based on the profile data, the historical driving behavior of the driver corresponding to the vehicle is determined by a driving behavior recognition model, wherein the driving behavior recognition model is used to characterize the correlation between the driver's profile data and driving behavior.

[0025] In one embodiment, the profile data includes the total number of times the driving status changed drastically within a preset second time period, corresponding to a unit mileage; before the step of querying the profile data of the driver corresponding to the vehicle based on the vehicle's identifier, the method further includes:

[0026] Obtain the historical driving data of the driver corresponding to the vehicle;

[0027] Based on the historical driving data, determine the number of times the driving status changes drastically within a preset second time period, corresponding to a unit mileage traveled.

[0028] The historical driving data includes vehicle speed, acceleration, steering wheel angle and total mileage within a preset second time period. Dramatically changing driving states include rapid acceleration, rapid deceleration, sharp turns and speeding.

[0029] The step of determining the number of times the driving state changes drastically per unit mileage within a preset second time period based on the historical driving data includes at least one of the following:

[0030] The average acceleration is calculated based on the vehicle speed at preset intervals within the preset second time period. Whenever the average acceleration is greater than the preset second threshold, the number of rapid accelerations is accumulated. The number of rapid accelerations per unit mileage is calculated based on the total mileage traveled by the vehicle within the preset second time period. The preset second threshold is a positive number.

[0031] Whenever the average acceleration is less than a preset third threshold, the number of sudden decelerations is accumulated, and the number of sudden decelerations per unit mileage is calculated based on the total mileage traveled by the vehicle within a preset second time period, wherein the preset third threshold is a negative number;

[0032] The average angular velocity is calculated based on the steering wheel angle at each preset time interval. Whenever the average angular velocity and vehicle speed are within the preset speed range, the number of sharp turns is accumulated, and the number of sharp turns per unit mileage is calculated based on the total mileage traveled by the vehicle within the preset second time period.

[0033] The average vehicle speed is calculated based on the vehicle speed at each preset time interval. Whenever the average vehicle speed is greater than a preset fourth threshold, the number of speeding violations is accumulated. The number of speeding violations per unit mileage is calculated based on the total mileage traveled by the vehicle within the preset second time period.

[0034] Specifically, when the driving state changes drastically multiple times in a row, and the time interval between adjacent changes is less than a preset threshold, the cumulative number of such changes will be changed to one.

[0035] In one embodiment, the step of determining the passenger comfort score based on the influence score includes:

[0036] Within a preset scoring period, a passenger comfort score is determined based on the impact score every preset first time period.

[0037] After determining the passenger comfort score based on the impact score, the following steps are included:

[0038] The total passenger comfort score within the preset scoring period is calculated based on multiple passenger comfort scores determined at each preset first time period.

[0039] The total passenger comfort score is displayed on the in-vehicle screen.

[0040] In addition, to achieve the above objectives, this application also proposes a passenger comfort determination device, which includes: 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 passenger comfort determination method as described above.

[0041] 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 passenger comfort determination method described above.

[0042] 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 passenger comfort determination method described above.

[0043] One or more technical solutions proposed in this application have at least the following technical effects:

[0044] This application obtains vehicle driving data and, based on this data, determines a score on the impact of changes in vehicle driving status on passenger comfort. Since vehicle driving data objectively reflects changes in the vehicle's driving status during operation, and these changes have varying impacts on passenger comfort, the impact score can be objectively determined using this data. This impact score reflects the objective influence of the vehicle's operating status on passenger comfort. By determining the passenger comfort score based on this impact score, individual subjective differences can be eliminated, thus calculating a comfort score that objectively quantifies the true comfort level of passengers. Attached Figure Description

[0045] 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.

[0046] 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.

[0047] Figure 1 A flowchart illustrating the method for determining passenger comfort in this application (Example 1);

[0048] Figure 2A flowchart illustrating Embodiment 2 of the passenger comfort determination method of this application;

[0049] Figure 3 A flowchart illustrating Embodiment 3 of the passenger comfort determination method of this application;

[0050] Figure 4 This is a schematic diagram of a scenario provided for Embodiment 3 of the passenger comfort determination method of this application;

[0051] Figure 5 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the passenger comfort determination method in this application embodiment.

[0052] 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

[0053] 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.

[0054] 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.

[0055] Based on this, embodiments of this application provide a method for determining passenger comfort, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the passenger comfort determination method of this application.

[0056] In this embodiment, the passenger comfort determination method includes steps S10 to S30:

[0057] Step S10: Obtain vehicle driving data;

[0058] Traditional passenger comfort evaluation methods typically rely on questionnaires or subjective scoring. However, due to significant differences in individual subjective feelings, these methods cannot objectively reflect the true comfort level of passengers during their journey. To address this technical problem, this embodiment aims to objectively score passenger comfort based on vehicle driving data.

[0059] It should be noted that the passenger comfort determination method can be applied to vehicle-mounted driver assistance systems, intelligent cockpit systems, or driver feedback systems, etc. The following will use driver assistance systems as an example.

[0060] Specifically, vehicle driving data can be collected through vehicle devices such as the Electronic Control Unit (ECU) or the Inertial Measurement Unit (IMU).

[0061] Among them, vehicle driving data includes braking frequency and intensity, road conditions, etc. Vehicle driving data can objectively reflect the state changes that the vehicle experiences during driving. For example, when the vehicle speed changes rapidly or when it frequently turns left and right, it will directly affect the passenger's riding comfort.

[0062] Step S20: Based on the vehicle driving data, determine the impact score of the change in vehicle driving status on passenger comfort.

[0063] Since vehicle driving data can objectively reflect changes in the vehicle's driving status during driving, these changes can have different impacts on passenger comfort. Specifically, changes in vehicle driving status include the vehicle accelerating from low speed to high speed, or frequent and drastic changes in steering wheel angle. These changes in vehicle driving status may directly cause discomfort to passengers in the vehicle due to inertia and other reasons.

[0064] Therefore, by using vehicle driving data, the impact score of changes in vehicle driving status on passenger comfort can be objectively determined. It can be understood that this impact score reflects the objective impact of vehicle operating status on passenger comfort.

[0065] Specifically, the vehicle driving data can be transformed into a score that affects passenger comfort using machine learning or rule-based models. For example, comfort-related features (such as acceleration change rate and braking frequency) can be extracted using machine learning or rule-based models, and the impact score on passenger comfort can be determined by model inference based on the extracted comfort-related features.

[0066] The impact score on passenger comfort can be a specific numerical range (such as 0 to 10 or 0 to 100). The higher the impact score, the greater the impact on passenger comfort, and the lower the impact score, the smaller the impact on passenger comfort.

[0067] In one feasible implementation, the scoring model (e.g., a machine learning model or a rule model) can be deployed in the driving assistance system. When updates are needed, the new model can be sent to the driving assistance system via the cloud, or the model parameters can be hot-updated. That is, this application adopts an edge-cloud collaborative framework to improve data processing efficiency and improve the computational efficiency that affects the scoring.

[0068] The weights of each parameter in the scoring model can be determined based on historical vehicle driving data and the distribution of expected influence on the score. Compared with the method of calibrating rules and parameters based on expert experience in related technologies, the scoring model in this embodiment can better adapt to complex and ever-changing driving scenarios.

[0069] Step S30: Determine the passenger comfort score based on the impact score.

[0070] Based on the influence score, a passenger comfort score is determined, which eliminates individual subjective differences and thus calculates a comfort score that can objectively quantify the true comfort level of passengers. Specifically, the implementation method for determining the passenger comfort score based on the influence score can be: directly using the value of the influence score as the passenger comfort score; or the influence score can be converted into linguistic variables (such as "high", "medium", "low").

[0071] Specifically, a higher impact score corresponds to a lower passenger comfort score, and vice versa. A higher comfort score indicates a better passenger comfort experience, while a lower comfort score indicates a worse passenger comfort experience.

[0072] In one feasible implementation, a passenger comfort score can also be determined based on passenger profile data and the aforementioned impact score. The passenger profile data includes age, health status, and travel purpose. The travel purpose (e.g., commuting, resting, working, etc.) can be determined based on the travel address or user behavior. The passenger profile data can be obtained through user registration information on the driving platform, onboard sensors, etc. This approach allows for a more personalized and objective determination of the passenger comfort score.

[0073] In one feasible implementation, determining the passenger comfort score based on the influence score can also be:

[0074] Within a preset scoring period, a passenger comfort score is determined based on the impact score every preset first time period.

[0075] In order to make the comfort score more data-driven and realize real-time dynamic monitoring of passenger comfort, the passenger comfort score can be determined according to the influence score at each preset first time period within the preset scoring cycle, thereby capturing the instantaneous changes in the vehicle driving status.

[0076] The preset scoring period can be a complete trip (from passenger boarding to alighting) or a fixed duration, such as 1 minute or 2 minutes. The preset first time period has a fixed duration; for example, within a complete trip, a passenger comfort score is determined every 5 seconds, or every 10 seconds. This means that multiple passenger comfort scores can be obtained at the end of the trip.

[0077] In addition, after the step of determining the passenger comfort score based on the influence score, the following can also be performed: calculate the total passenger comfort score within the preset scoring period based on multiple passenger comfort scores determined at each preset first time period; and display the total passenger comfort score on the in-vehicle display screen.

[0078] In order to remind drivers to pay attention to their driving behavior, or to help passengers intuitively understand the quality of their ride and thus assist in service evaluation, the total passenger comfort score within the preset scoring period can be calculated by using an average or weighted average method based on multiple passenger comfort scores determined at each preset first time period; and then the total passenger comfort score can be displayed through numerical scores, star ratings, text feedback, etc.

[0079] In this embodiment, by acquiring vehicle driving data, an impact score on passenger comfort is determined based on the vehicle driving data. Since vehicle driving data can objectively reflect changes in the vehicle's driving state during operation, and these changes have different impacts on passenger comfort, the impact score on passenger comfort can be objectively determined using vehicle driving data. This impact score reflects the objective influence of the vehicle's operating state on passenger comfort. By determining the passenger comfort score based on this impact score, individual subjective differences can be eliminated, thereby calculating a comfort score that can objectively quantify the true comfort level of passengers.

[0080] 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 Step S20 includes steps S01 to S02:

[0081] Step S01: Based on the acceleration in different directions, calculate the vehicle's stability in different directions and the frequency of instantaneous disturbances generated by the vehicle in different directions;

[0082] It should be noted that the vehicle driving data includes acceleration in different directions, and the changes in the vehicle's driving state include the vehicle's stability in different directions and the frequency of instantaneous disturbances generated by the vehicle in different directions; the acceleration in different directions includes longitudinal acceleration a. Igt lateral acceleration a lat and vertical acceleration a vert Longitudinal acceleration reflects the front and rear impacts caused by vehicle acceleration and braking; lateral acceleration reflects the lateral force when turning or changing lanes; and vertical acceleration reflects the rate of change of vehicle speed in the vertical direction (same / opposite to the direction of gravity).

[0083] Since acceleration in different directions can capture the transient response of a vehicle in different directions during operation, and acceleration is a quantifiable physical quantity, this embodiment calculates the vehicle's stability in different directions and the frequency of instantaneous disturbances generated by the vehicle in different directions based on the acceleration in different directions. Based on the stability and the frequency, the impact score is determined, thereby enabling the evaluation of the impact of different driving styles or road conditions on passenger comfort based on the same set of standards, and obtaining an objective impact score through quantification.

[0084] Specifically, the implementation method for calculating the vehicle's stability in different directions based on the acceleration in those different directions can be:

[0085] When the absolute value of longitudinal acceleration exceeds a certain value more than a certain number of times, the vehicle is considered to have a low degree of longitudinal stability. When the absolute value of longitudinal acceleration exceeds a certain value less than a certain number of times, the vehicle is considered to have a high degree of longitudinal stability. The standard deviation of lateral acceleration is calculated; the smaller the standard deviation, the higher the degree of lateral stability of the vehicle. The root mean square value of vertical acceleration is calculated, and the intensity of continuous vibration is assessed based on the root mean square value. The larger the root mean square value, the lower the degree of vertical stability of the vehicle.

[0086] The implementation method for calculating the frequency of instantaneous disturbances generated by the vehicle in different directions based on the acceleration in different directions can be as follows: select an appropriate wavelet basis function, perform continuous wavelet transform on the acceleration data in each direction. The continuous wavelet transform can provide the representation of the signal at different scales, convert the representation at different scales into frequencies, and thus calculate the frequency of instantaneous disturbances generated by the vehicle in different directions.

[0087] In one feasible implementation, the method of calculating the vehicle's stability in different directions and the frequency of instantaneous disturbances generated by the vehicle in different directions based on the acceleration in those different directions can be:

[0088] The acceleration in different directions within a preset first time period is integrated over time to obtain the integral values ​​corresponding to different directions. The integral values ​​corresponding to different directions reflect the stability of the vehicle in different directions. Based on the acceleration in different directions within the preset first time period, the corresponding jerk is calculated. Based on the corresponding jerk in different directions and a preset first threshold, the frequency of instantaneous disturbances generated by the vehicle in different directions is calculated.

[0089] Specifically, the acceleration in different directions can be integrated over time to obtain the integral value for each direction. If the acceleration in a certain direction changes frequently or drastically, its integral value will be larger, indicating that the vehicle is less stable in that direction; while the smaller the integral value for a certain direction, the more stable the vehicle's motion in that direction.

[0090] Therefore, this embodiment uses integral values ​​to quantify the stability of the vehicle in different directions; specifically, in order to eliminate the influence of the positive or negative value of acceleration in different directions on the integration result, this embodiment can perform time integration on the square of acceleration in different directions.

[0091] One further method for integrating the squares of accelerations in different directions over time is:

[0092]

[0093] Furthermore, based on the acceleration in different directions within the preset first time period, the corresponding jerk in different directions is calculated. Jerk is the first derivative of acceleration with respect to time, representing the rate of change of acceleration. Specifically, the calculation method for calculating the corresponding jerk in different directions based on the acceleration in different directions within the preset first time period can be as follows:

[0094] j(t2)=(a(t2)-a(t1)) / (t2-t1);

[0095] Where j is the jerk, t2 is the end time of the preset first time period, and t1 is the start time of the preset first time period.

[0096] Since a greater jerk indicates a more drastic change in acceleration, the number of instantaneous disturbances generated by the vehicle in different directions can be calculated based on the jerk corresponding to different directions and a preset first threshold. This can effectively identify how many instantaneous disturbances occurred within a preset first time period, thereby calculating the frequency of instantaneous disturbances generated by the vehicle in different directions. It can be understood that if the frequency of instantaneous disturbances generated by the vehicle in different directions is higher, the impact on passenger comfort score will be higher.

[0097] Specifically, based on the jerk corresponding to different directions and a preset first threshold, the frequency of instantaneous disturbances generated by the vehicle in different directions is calculated.

[0098] Step S02: Determine the impact score based on the stability and the frequency.

[0099] Based on the stability and frequency, the implementation method for determining the impact score can be: summing the integral values ​​corresponding to different directions to obtain a partial impact score (score1) on passenger comfort from the vehicle's stability in different directions.

[0100]

[0101] Where t is the preset first time period, and α, β, and γ are the influence coefficients of the integral values ​​of longitudinal acceleration, lateral acceleration, and vertical acceleration, respectively. These influence coefficients can be determined based on historical vehicle driving data and the distribution of the expected influence scores.

[0102] The summation of the frequencies of instantaneous disturbances generated in different directions yields a partial impact score on passenger comfort, based on the frequency of instantaneous disturbances generated by the vehicle in different directions.

[0103]

[0104] Wherein, X, Y, and Z are the preset first thresholds corresponding to the jerk in different directions. The preset first thresholds corresponding to the jerk in different directions can be determined based on historical vehicle driving data and the distribution of the expected impact score. The preset first thresholds corresponding to the jerk in different directions can be different.

[0105] The total impact score is obtained by summing the partial impact scores of vehicle stability in different directions on passenger comfort (score1) and the partial impact scores of the frequency of instantaneous disturbances generated by the vehicle in different directions on passenger comfort (score2).

[0106] In one feasible implementation, the factors influencing the score can always be:

[0107]

[0108] In this embodiment, the vehicle's stability and the number of instantaneous disturbances in different directions are comprehensively evaluated based on the vehicle's acceleration in different directions, thereby comprehensively and accurately reflecting the impact of the vehicle's driving state on passenger comfort. This not only quantifies the comfort impact on passengers in different directions during vehicle operation but also effectively captures the impact of sudden disturbances on the passenger experience, improving the objectivity and sensitivity of comfort evaluation.

[0109] Based on the first and second embodiments of this application, in the third 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 3 Step S30 includes steps A1 to A3:

[0110] Step A1: Obtain the driver's historical driving behavior and determine the type of the historical driving behavior;

[0111] It should be noted that, in addition to the direct impact of vehicle operating status on passenger comfort, different drivers' driving habits also have an indirect impact on passenger comfort perception. In order to improve the accuracy of passenger comfort evaluation, this embodiment also corrects the above-mentioned impact score based on the driver's historical driving behavior, which can reflect the driver's driving habits.

[0112] Specifically, it is necessary to obtain the driver's historical driving behavior, which may include behaviors such as forcibly overtaking, cutting in line, not maintaining a safe distance, using a mobile phone and multitasking, running red lights and failing to yield to pedestrians, driving slowly in the wrong lane and straddling lanes. These historical driving behaviors may all have a negative impact on passenger comfort and increase the probability of traffic accidents.

[0113] In one feasible implementation, refer to Figure 4 The method for obtaining the driver's historical driving behavior can be:

[0114] Obtain the vehicle's identifier; query the driver's profile data corresponding to the vehicle based on the vehicle's identifier; determine the driver's historical driving behavior based on the profile data using a driving behavior recognition model, wherein the driving behavior recognition model is used to characterize the correlation between the driver's profile data and driving behavior.

[0115] Specifically, the vehicle identifier can be a VIN (Vehicle Identification Number) code or device number, etc.; the vehicle identifier is bound to the driver, and the corresponding driver profile data is pre-stored. Therefore, the driver profile data can be queried based on the vehicle identifier; the driver profile data can be age, historical driving records, etc.

[0116] Since the driving behavior recognition model is used to characterize the relationship between the driver's profile data and driving behavior, the historical driving behavior of the driver corresponding to the vehicle can be determined by the driving behavior recognition model based on the profile data: extracting driving features from the historical driving records and inferring the historical driving behavior of the driver corresponding to the vehicle based on the driving features.

[0117] Furthermore, determining the type of the historical driving behavior can be achieved by: extracting driving features through the feature extraction layer of the driving behavior recognition model, and then inferring and analyzing the historical driving behavior of the driver corresponding to the vehicle based on the driving features, and finally classifying the historical driving behavior through the classification network of the driving behavior recognition model to obtain the type of historical driving behavior.

[0118] Among them, the types of historical driving behavior include conservative, average, and aggressive.

[0119] It should be noted that the profile data includes the total number of times the driving status changed drastically within a unit of mileage in a preset second time period; that is, the driver's behavioral characteristics are described by recording and statistically analyzing the number of times the driving status changed drastically within a unit of mileage in a preset second time period; the preset second time period can be within 30 days or 15 days, etc.

[0120] Specifically, before the step of querying the profile data of the driver corresponding to the vehicle based on the vehicle's identifier, the following steps may be taken: obtaining the historical driving data of the driver corresponding to the vehicle; and determining, based on the historical driving data, the number of times the driving status changes drastically per unit mileage within a preset second time period.

[0121] Historical driving data can include data such as vehicle speed, acceleration, steering wheel angle, mileage, timestamp, and geographical location. Historical driving data can be determined by signals transmitted through an inertial measurement unit (signal names are referenced in Table 1).

[0122] The implementation method for determining the number of times the driving state changes drastically per unit mileage within a preset second time period based on the historical driving data can be as follows: a machine learning model is trained based on training data (driving data of different drivers), and the historical driving data of the driver corresponding to the vehicle is automatically analyzed based on the trained model to identify the number of times the driving state changes drastically per unit mileage within a preset second time period.

[0123] Table 1 Driving Data List

[0124]

[0125] It should be noted that the historical driving data includes vehicle speed, acceleration, steering wheel angle and total mileage within the preset second time period. Referring to Table 1, rapidly changing driving states include rapid acceleration, rapid deceleration, sharp turns and speeding.

[0126] Specifically, the implementation method for determining the number of times the driving state changes drastically per unit mileage within a preset second time period based on the historical driving data can also be at least one of the following:

[0127] First, the average acceleration is calculated based on the vehicle speed at preset intervals within the preset second time period. Whenever the average acceleration exceeds a preset second threshold, the number of rapid accelerations is accumulated. The number of rapid accelerations per unit distance traveled is calculated based on the total distance traveled within the preset second time period. The preset second threshold is a positive number.

[0128] The preset time interval can be every 1 second or 2 seconds, for example, the average acceleration is calculated based on the vehicle speed every 1 second in the historical driving data obtained within 30 days. That is, the average acceleration is calculated based on the speed difference and time interval. Whenever the average acceleration is greater than the preset second threshold, the number of rapid accelerations is incremented by one. In order to facilitate quantitative evaluation, the number of rapid accelerations per unit mileage is calculated based on the total mileage of the vehicle within the preset second time period.

[0129] It should be noted that the preset second threshold is a positive number, for example, +2.78 m / s 2 +2.5m / s 2 +3m / s 2 When the average acceleration exceeds the preset second threshold, it is considered a rapid acceleration, and the number of rapid accelerations is accumulated.

[0130] Second, whenever the average acceleration is less than a preset third threshold, the number of rapid decelerations is accumulated, and the number of rapid decelerations per unit mileage is calculated based on the total mileage traveled by the vehicle within a preset second time period, wherein the preset third threshold is a negative number.

[0131] Similarly, whenever the average acceleration is less than a preset third threshold, the cumulative number of sudden decelerations is calculated. To facilitate quantitative evaluation, the number of sudden decelerations per unit distance traveled is calculated based on the total distance traveled by the vehicle within a preset second time period. The preset third threshold is a negative number, and can be -2.78 m / s². 2 -3m / s 2 -2.5m / s 2 wait.

[0132] Third, calculate the average angular velocity based on the steering wheel angle at each preset time interval. Whenever the average angular velocity and vehicle speed are within the preset speed range, accumulate the number of sharp turns, and calculate the number of sharp turns per unit distance traveled based on the total distance traveled by the vehicle within the preset second time period.

[0133] Specifically, the average angular velocity and vehicle speed correspond to different preset speed ranges. The average angular velocity is calculated based on the steering wheel angle at each preset time interval. Whenever the average angular velocity and vehicle speed are within the preset speed range, it is considered that the vehicle has made a sharp turn, and the number of sharp turns is incremented by one. In order to facilitate quantitative evaluation, the number of sharp turns per unit distance traveled is calculated based on the total distance traveled by the vehicle in the preset second time period.

[0134] One method for determining whether a turn is a sharp turn, based on the different speed ranges corresponding to average angular velocity and vehicle speed, is as follows: when the average angular velocity is within the range [0.1, 1] and the vehicle speed is within the range [17, 46], a sharp turn is considered to have occurred. That is, even if the angular velocity is low, a sharp turn will still occur if the vehicle speed is high, and the cumulative count is incremented by 1. For example:

[0135] when steerwheel_ag_r=0.1and speed_ms>=46then 1;

[0136] when steerwheel_ag_r=0.2and speed_ms>=40then 1;

[0137] when steerwheel_ag_r=0.3and speed_ms>=33then 1;

[0138] when steerwheel_ag_r=0.4and speed_ms>=27then 1;

[0139] when steerwheel_ag_r=0.5and speed_ms>=26then 1;

[0140] when steerwheel_ag_r=0.6and speed_ms>=23then 1;

[0141] when steerwheel_ag_r=0.7and speed_ms>=22then 1;

[0142] when steerwheel_ag_r=0.8and speed_ms>=21then 1;

[0143] when steerwheel_ag_r=0.9and speed_ms>=20then 1;

[0144] when steerwheel_ag_r=1.0and speed_ms>=18then 1 etc.

[0145] A sharp turn is considered to have occurred when the average angular velocity is within the range [1.1, 7.3] and the vehicle speed is within the range [7, 17]. That is, even if the vehicle speed is moderate, a sharp turn will occur if the angular velocity is high, and the cumulative count is incremented by 1. For example:

[0146] when steerwheel_ag_r >= 1.1 and steerwheel_ag_r <= 1.4 and speed_ms >= 17 then 1;

[0147] when steerwheel_ag_r >= 1.5 and steerwheel_ag_r <= 2.0 and speed_ms >= 15 then 1;

[0148] when steerwheel_ag_r >= 2.1 and steerwheel_ag_r <= 2.4 and speed_ms >= 14 then 1;

[0149] when steerwheel_ag_r >= 2.5 and steerwheel_ag_r <= 2.8 and speed_ms >= 13 then 1;

[0150] when steerwheel_ag_r >= 2.9 and steerwheel_ag_r <= 3.2 and speed_ms >= 12 then 1;

[0151] when steerwheel_ag_r >= 3.3 and steerwheel_ag_r <= 3.7 and speed_ms >= 11 then 1;

[0152] when steerwheel_ag_r >= 3.8 and steerwheel_ag_r <= 4.2 and speed_ms >= 10 then 1;

[0153] when steerwheel_ag_r >= 4.3 and steerwheel_ag_r <= 4.8 and speed_ms >= 9 then 1;

[0154] when steerwheel_ag_r >= 4.9 and steerwheel_ag_r <= 7.2 and speed_ms >= 8 then 1;

[0155] when steerwheel_ag_r >= 7.3 and steerwheel_ag_r <= 7.8 and speed_ms >= 7 then 1 etc.

[0156] A sharp turn is considered to have occurred when the average angular velocity is greater than 7.9 and the vehicle speed is greater than 3. That is, even if the vehicle speed is low, a sharp turn will still occur if the angular velocity is high. The cumulative count is incremented by 1. For example:

[0157] when steerwheel_ag_r>=7.9and speed_ms>=3 then 1.

[0158] Fourth, calculate the average vehicle speed based on the vehicle speed at each preset time interval. Whenever the average vehicle speed exceeds a preset fourth threshold, accumulate the number of speeding violations. Calculate the number of speeding violations per unit of mileage based on the total mileage traveled within the preset second time period.

[0159] Specifically, the preset fourth threshold can be 120km / h or 110km / h, etc. The average vehicle speed is calculated based on the vehicle speed at each preset time interval. Whenever the average vehicle speed is greater than the preset fourth threshold, the vehicle is considered to be speeding, and the number of speeding is incremented by one. In order to facilitate quantitative evaluation, the number of speeding times per unit of mileage is calculated based on the total mileage traveled by the vehicle within the preset second time period.

[0160] It should be noted that, since the acquisition period of the angular velocity signal is relatively long, it is easy for there to be no calculation within a long interval. In order to avoid inconsistent quantification standards, in the above calculation process, when the driving state changes drastically multiple times in a row and the time interval between adjacent changes is less than a preset threshold, the number of times the corresponding multiple changes are changed to one. The preset threshold can be 20 seconds or 30 seconds, for example, when the driving state changes drastically multiple times in a row within 30 seconds, it is only counted as one time.

[0161] Step A2: Determine the target correction coefficient corresponding to the historical driving behavior based on the mapping relationship between different types of driving behavior and correction coefficients;

[0162] In this embodiment, a mapping table between different types of driving behavior and correction coefficients can be pre-established. The mapping table stores the mapping relationship between different types of driving behavior and correction coefficients; for example, the correction coefficient corresponding to the conservative type is 1.0, the correction coefficient corresponding to the normal type is 0.9, and the correction coefficient corresponding to the aggressive type is 0.7, etc.

[0163] Different driving styles have different impacts on passenger comfort and require differentiated treatment. For example, even if the vehicle itself is stable, the score should be appropriately reduced for aggressive drivers. Therefore, this embodiment determines the target correction coefficient corresponding to the historical driving behavior based on the mapping relationship between different types of driving behavior and correction coefficients.

[0164] Step A3: Correct the impact score according to the target correction coefficient to obtain the passenger comfort score.

[0165] To improve the accuracy of passenger comfort scores, this embodiment comprehensively considers changes in vehicle condition and driver habits, and corrects the impact score according to the target correction coefficient to obtain a passenger comfort score that better reflects the passenger's actual riding experience.

[0166] In this embodiment, by comprehensively considering the objective impact of vehicle operating status on passenger comfort and the subjective impact of driver driving behavior on passenger comfort, the passenger comfort score is made more consistent with the passenger's actual riding experience, thus improving the accuracy of the passenger comfort score.

[0167] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the passenger comfort determination method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0168] This application provides a passenger comfort determination 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 passenger comfort determination method in the first embodiment described above.

[0169] The following is for reference. Figure 5 The diagram illustrates a structural schematic suitable for implementing a passenger comfort determination device according to embodiments of this application. The passenger comfort determination device in embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, tablets, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital televisions and desktop computers. Figure 5 The passenger comfort determination 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.

[0170] like Figure 5 As shown, the passenger comfort determination device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.) that can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004.

[0171] RAM 1004 also stores various programs and data required for the operation of the passenger comfort determination device. Processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, etc.

[0172] The system includes an input device 1007 such as a gyroscope; an output device 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 allows the passenger comfort determination device to communicate wirelessly or wiredly with other devices to exchange data. Although passenger comfort determination devices with various systems are shown in the figures, it should be understood that it is not required to implement or possess all of the systems shown. More or fewer systems may be implemented alternatively.

[0173] 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 ROM 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.

[0174] The passenger comfort determination device provided in this application, employing the passenger comfort determination method in the above embodiments, can solve the technical problem of failing to objectively reflect the true comfort level of passengers during their journey. Compared with the prior art, the beneficial effects of the passenger comfort determination device provided in this application are the same as those of the passenger comfort determination method provided in the above embodiments, and other technical features of this passenger comfort determination device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0175] 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.

[0176] 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.

[0177] 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 perform the passenger comfort determination method in the above embodiments.

[0178] 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, 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, 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.

[0179] The aforementioned computer-readable storage medium may be included in the passenger comfort determination device; or it may exist independently and not assembled into the passenger comfort determination device.

[0180] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the passenger comfort determination device, cause the passenger comfort determination device to perform the aforementioned passenger comfort determination method.

[0181] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as 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).

[0182] 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.

[0183] 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.

[0184] 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 passenger comfort determination method, thereby solving the technical problem of failing to objectively reflect the true comfort level of passengers during their journey. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the passenger comfort determination method provided in the above embodiments, and will not be repeated here.

[0185] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the passenger comfort determination method described above.

[0186] The computer program product provided in this application can solve the technical problem of failing to objectively reflect the true comfort level of passengers during their journey. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the passenger comfort determination method provided in the above embodiments, and will not be repeated here.

[0187] The above descriptions are merely some embodiments of this application and do not limit the scope of protection of this application. Any equivalent structural transformations made based on the technical concept of this application and the content of this specification and drawings, or direct / indirect applications in other related technical fields, are included within the scope of protection of this application. All actions involving the acquisition of signals, information, or data in this application are performed in accordance with the relevant data protection laws and policies of the country where the application is located and with authorization from the owner of the corresponding device.

Claims

1. A method for determining passenger comfort, characterized in that, The method for determining passenger comfort includes: Obtain vehicle driving data; Based on the vehicle driving data, a score is determined on the impact of changes in vehicle driving status on passenger comfort. Based on the impact score, a passenger comfort score is determined.

2. The passenger comfort determination method as described in claim 1, characterized in that, The vehicle driving data includes acceleration in different directions, and the changes in the vehicle driving state include the stability of the vehicle in different directions and the frequency of instantaneous disturbances generated by the vehicle in different directions. The step of determining the impact score of changes in vehicle driving status on passenger comfort based on the vehicle driving data includes: Based on the acceleration in different directions, calculate the vehicle's stability in different directions and the frequency of instantaneous disturbances generated by the vehicle in different directions; The impact score is determined based on the stability and the frequency.

3. The passenger comfort determination method as described in claim 2, characterized in that, The step of calculating the vehicle's stability in different directions and the frequency of instantaneous disturbances generated by the vehicle in different directions based on the acceleration in different directions includes: The acceleration in different directions within a preset first time period is integrated over time to obtain the integral values ​​corresponding to different directions. The integral values ​​corresponding to different directions reflect the stability of the vehicle in different directions. Calculate the corresponding acceleration in different directions based on the acceleration in different directions within the preset first time period; Based on the jerk corresponding to different directions and a preset first threshold, the frequency of instantaneous disturbances generated by the vehicle in different directions is calculated.

4. The passenger comfort determination method as described in claim 1, characterized in that, The step of determining the passenger comfort score based on the impact score includes: Obtain the driver's historical driving behavior and determine the type of the historical driving behavior; Based on the mapping relationship between different types of driving behavior and correction coefficients, the target correction coefficient corresponding to the historical driving behavior is determined; The impact score is corrected based on the target correction coefficient to obtain the passenger comfort score.

5. The passenger comfort determination method as described in claim 4, characterized in that, The step of obtaining the driver's historical driving behavior includes: Obtain the vehicle's identifier; Based on the vehicle's identifier, query the profile data of the driver corresponding to the vehicle; Based on the profile data, the historical driving behavior of the driver corresponding to the vehicle is determined by a driving behavior recognition model, wherein the driving behavior recognition model is used to characterize the correlation between the driver's profile data and driving behavior.

6. The passenger comfort determination method as described in claim 5, characterized in that, The profile data includes the total number of times the driving status changed drastically within a preset second time period, corresponding to a unit of mileage traveled. Before the step of querying the profile data of the driver corresponding to the vehicle based on the vehicle's identifier, the method further includes: Obtain the historical driving data of the driver corresponding to the vehicle; Based on the historical driving data, determine the number of times the driving status changes drastically within a preset second time period, corresponding to a unit mileage traveled. The historical driving data includes vehicle speed, acceleration, steering wheel angle and total mileage within a preset second time period. Dramatically changing driving states include rapid acceleration, rapid deceleration, sharp turns and speeding. The step of determining the number of times the driving state changes drastically per unit mileage within a preset second time period based on the historical driving data includes at least one of the following: The average acceleration is calculated based on the vehicle speed at preset intervals within the preset second time period. Whenever the average acceleration is greater than the preset second threshold, the number of rapid accelerations is accumulated. The number of rapid accelerations per unit mileage is calculated based on the total mileage traveled by the vehicle within the preset second time period. The preset second threshold is a positive number. Whenever the average acceleration is less than a preset third threshold, the number of sudden decelerations is accumulated, and the number of sudden decelerations per unit mileage is calculated based on the total mileage traveled by the vehicle within a preset second time period, wherein the preset third threshold is a negative number; The average angular velocity is calculated based on the steering wheel angle at each preset time interval. Whenever the average angular velocity and vehicle speed are within the preset speed range, the number of sharp turns is accumulated, and the number of sharp turns per unit mileage is calculated based on the total mileage traveled by the vehicle within the preset second time period. The average vehicle speed is calculated based on the vehicle speed at each preset time interval. Whenever the average vehicle speed is greater than a preset fourth threshold, the number of speeding violations is accumulated. The number of speeding violations per unit mileage is calculated based on the total mileage traveled by the vehicle within the preset second time period. Specifically, when the driving state changes drastically multiple times in a row, and the time interval between adjacent changes is less than a preset threshold, the cumulative number of such changes will be changed to one.

7. The passenger comfort determination method as described in claim 1, characterized in that, The step of determining the passenger comfort score based on the impact score includes: Within a preset scoring period, a passenger comfort score is determined based on the impact score every preset first time period. After determining the passenger comfort score based on the impact score, the following steps are included: The total passenger comfort score within the preset scoring period is calculated based on multiple passenger comfort scores determined at each preset first time period. The total passenger comfort score is displayed on the in-vehicle screen.

8. A passenger comfort determination device, characterized in that, The passenger comfort determination device includes: 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 passenger comfort determination method as described in any one of claims 1 to 7.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the passenger comfort determination method as described in any one of claims 1 to 7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the passenger comfort determination method as described in any one of claims 1 to 7.

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