Vehicle longitudinal comfort detection method and device and medium

Abstract

The invention discloses a vehicle longitudinal comfort detection method and device and a medium, and relates to the technical field of automobiles. The method comprises the steps that the longitudinal acceleration of a vehicle in a first time period is obtained, and the first longitudinal acceleration of the vehicle corresponding to the positions of the feet, the seat cushion and the backrest of a first object is determined; fourier transform is carried out on the first longitudinal acceleration of each position, and mapping processing is carried out to obtain a first weight corresponding to each position; performing weighted calculation based on the weight to obtain a second longitudinal acceleration; comfort calculation is carried out according to the second longitudinal acceleration, and a longitudinal comfort score of each position is obtained; and fusing the longitudinal comfort scores of all the positions to obtain the overall longitudinal comfort score of the vehicle, and determining the comfort level of the vehicle. Therefore, accurate and objective evaluation of the longitudinal comfort of the vehicle can be realized, so that a driver can be guided to drive the vehicle more comfortably, and a reliable basis is provided for improving the driving experience.

Description

Technical Field

[0001] This application relates to the field of automotive technology, and in particular to a method, device and medium for detecting longitudinal comfort of a vehicle. Background Technology

[0002] Automobile comfort refers to the impact of vibrations and shocks experienced during vehicle operation on the comfort of passengers. With the rapid development of electric and hybrid vehicles, the acceleration and braking capabilities of these vehicles have fundamentally changed compared to traditional gasoline-powered vehicles. Their rapid acceleration and powerful regenerative braking result in a longitudinal (i.e., front-to-back) acceleration rate of change far exceeding that of traditional gasoline-powered vehicles, making longitudinal comfort a key pain point affecting the driving experience. Automobile longitudinal comfort refers to the vehicle's ability to filter impacts and vibrations from the front and rear directions during operation.

[0003] In related technologies, the optimization of longitudinal comfort mainly relies on the fixed tuning at the vehicle's factory (such as power response curves and suspension stiffness settings). This approach is passive and static, unable to adapt to changing road conditions, driving styles, or the real-time state of passengers. Furthermore, the evaluation of comfort mainly depends on the driver's subjective feelings or the experience of test drive engineers, lacking objective and quantifiable data support, making it difficult to conduct precise and closed-loop optimization. Summary of the Invention

[0004] This application provides a method, device, and medium for detecting vehicle longitudinal comfort, in order to solve the following technical problem: how to achieve an accurate and objective assessment of vehicle longitudinal comfort.

[0005] In a first aspect, embodiments of this application provide a method for detecting longitudinal comfort of a vehicle, the method comprising: The longitudinal acceleration of the vehicle within a first time period is obtained, and based on the longitudinal acceleration of the vehicle, a first longitudinal acceleration is determined for each first position, the first position including the foot position of the first object and the seat position and backrest position of the vehicle. Perform a Fourier transform on the first longitudinal acceleration at each first position to obtain the vibration frequency at each first position, and perform a mapping process on the vibration frequency at each first position to obtain the first weight corresponding to each first position. Based on the first weight corresponding to each first position, the first longitudinal acceleration of each first position is weighted and calculated to obtain the second longitudinal acceleration of each first position; For each of the first positions, a comfort calculation is performed on the first position based on the second longitudinal acceleration of the first position to obtain a longitudinal comfort score for the first position within the first time period; The longitudinal comfort scores at each of the first positions are fused to obtain the longitudinal comfort score of the vehicle within the first time period, and the longitudinal comfort level of the vehicle within the first time period is determined based on the longitudinal comfort score of the vehicle.

[0006] Secondly, embodiments of this application also provide a vehicle longitudinal comfort detection device, the device comprising: 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, the instructions being executed by the at least one processor to enable the at least one processor to perform a vehicle longitudinal comfort detection method as described above.

[0007] Thirdly, embodiments of this application also provide a computer storage medium storing computer-executable instructions, which, when executed, implement a vehicle longitudinal comfort detection method as described above.

[0008] The vehicle longitudinal comfort detection method, equipment, and medium provided in this application have the following beneficial effects: First, by acquiring the vehicle's longitudinal acceleration and equating it to three key human body positions—the feet, seat cushion, and backrest—this overcomes the limitations of traditional single-point sensor measurements. The input data accurately reflects the actual force state of different body parts of the driver and passengers, providing a data foundation for subsequent longitudinal comfort assessments. Then, Fourier transforms and frequency mappings are performed on the data from each position, weighting them according to the human body's physiological sensitivity to vibrations of different frequencies. This closely links physical vibration with physiological perception, allowing subsequent calculations to more closely approximate subjective feelings, resulting in a more accurate second longitudinal acceleration. Finally, based on this second longitudinal acceleration... The comfort score calculation quantifies complex vibration data over a period of time into an objective score value, transforming complex signals into intuitive indicators and providing a unified scale for cross-vehicle and cross-scenario comparisons. Finally, the scores from various locations are merged to obtain the overall vehicle longitudinal comfort score and classify it into levels. By comprehensively considering the overall feelings of multiple parts of the human body, the comfort score is elevated from local to overall, ultimately outputting an intuitive and accurate comfort level. This enables a precise and objective assessment of the vehicle's longitudinal comfort, providing a clear and reliable decision-making basis for subsequent intelligent control strategies such as automatic adjustment of driving modes and driving suggestion prompts. Attached Figure Description

[0009] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1A flowchart of a vehicle longitudinal comfort detection method provided in this application embodiment; Figure 2 This is a schematic diagram of the internal structure of a vehicle longitudinal comfort detection device provided in an embodiment of this application. Detailed Implementation

[0010] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0011] It is understood that in the embodiments of this disclosure, data related to user information (such as user accounts) is involved. When the embodiments of this disclosure are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with relevant laws, regulations and standards.

[0012] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. The terminology used herein is for the purpose of describing embodiments of this disclosure only and is not intended to be limiting of this disclosure.

[0013] In the following description, the terms “first, second, ...” are used merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that “first, second, ...” may be interchanged in a specific order or sequence where permitted, so that the embodiments of this disclosure described herein can be implemented in an order other than that illustrated or described herein.

[0014] Automobile comfort refers to the impact of vibrations and shocks experienced during vehicle operation on the comfort of passengers. With the rapid development of electric and hybrid vehicles, the acceleration and braking capabilities of these vehicles have fundamentally changed compared to traditional gasoline-powered vehicles. Their rapid acceleration and powerful regenerative braking result in a longitudinal (i.e., front-to-back) acceleration rate of change far exceeding that of traditional gasoline-powered vehicles, making longitudinal comfort a key pain point affecting the driving experience. Automobile longitudinal comfort refers to the vehicle's ability to filter impacts and vibrations from the front and rear directions during operation.

[0015] In related technologies, the optimization of longitudinal comfort mainly relies on the fixed tuning at the vehicle's factory (such as power response curves and suspension stiffness settings). This approach is passive and static, unable to adapt to changing road conditions, driving styles, or the real-time state of passengers. Furthermore, the evaluation of comfort mainly depends on the driver's subjective feelings or the experience of test drive engineers, lacking objective and quantifiable data support, making it difficult to conduct precise and closed-loop optimization.

[0016] With the popularization of smart cockpits and intelligent driving, more and more high-precision sensors (such as accelerometers) are being deployed in vehicles, which can capture the vehicle's motion state in real time and accurately. In addition, smart cockpits have powerful local computing power and can process complex sensor data.

[0017] Based on this, this application provides a method for detecting vehicle longitudinal comfort. The method uses a vehicle acceleration sensor to collect longitudinal acceleration signals of the vehicle, and uses the computing power of the smart cockpit to calibrate, weight, and calculate the data to monitor the longitudinal comfort of the vehicle in real time. This enables an accurate and objective assessment of the vehicle's longitudinal comfort, guiding the driver to drive the vehicle more comfortably and providing a reliable basis for improving the driving experience.

[0018] The technical solutions proposed in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0019] Figure 1 This document presents a flowchart of a vehicle longitudinal comfort detection method as provided in an embodiment of this application. This method can be applied to various types of vehicle driving scenarios, such as urban commuting and congested road conditions, dynamic driving and aggressive handling, energy recovery intensity optimization, and start-stop control for assisted driving. Certain input parameters or intermediate results in the process can be manually adjusted to help improve accuracy.

[0020] This disclosure provides a method for detecting longitudinal comfort in vehicles. It should be noted that the execution entity in these embodiments can be a server or any terminal device with data processing capabilities. For example, the server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. The terminal device can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, in-vehicle terminal, etc., but is not limited to these.

[0021] like Figure 1As shown in the figure, the vehicle longitudinal comfort detection method provided in this application embodiment specifically includes the following steps: Step 101: Obtain the longitudinal acceleration of the vehicle during the first time period.

[0022] It should be noted that the first time period refers to a certain period of time during the vehicle's operation, such as 30 seconds of acceleration or 20 seconds of deceleration; longitudinal acceleration is a physical quantity used to describe the rate of change of the vehicle's forward or backward speed, which can be obtained through sensors built into the vehicle, such as the vehicle acceleration sensor installed on the driver's cab floor, and is not specifically limited here.

[0023] As an example, suppose we use the vehicle's acceleration sensor to collect longitudinal acceleration data of the vehicle over a period of time, such as collecting longitudinal acceleration data of the vehicle over 30 seconds.

[0024] In some embodiments, in addition to collecting longitudinal acceleration information of the vehicle, information such as the vehicle speed, engine or drive motor speed, and gearbox gear can also be collected and read to obtain more comprehensive longitudinal acceleration information of the vehicle.

[0025] In some embodiments, the data acquisition requirement may be to start data acquisition when the vehicle's acceleration or deceleration is greater than 0.1 during the vehicle's acceleration or deceleration, with an acquisition duration of 30 seconds and a sampling frequency of 200Hz.

[0026] Step 102: Based on the longitudinal acceleration of the vehicle, determine the first longitudinal acceleration at each first position.

[0027] Here, the first position includes the foot position of the first object as well as the seat position and backrest position of the vehicle.

[0028] It should be noted that the first object is an object located inside the vehicle. For example, the first object can be the driver or a passenger located in another position in the vehicle; the foot position can be the position of a key point of the first object's feet, such as the position of the center of gravity; the seat cushion position can be the position of a key point on the plane where the seat is located, such as the position at the center of the seat plane; the backrest position can be the position of a key point on the plane where the seat back is located, such as the position 29.2cm above the center of the seat plane, and no specific limitation is made here.

[0029] In some embodiments, step 102 described above can be implemented as follows: for each of the first positions, the following processing is performed: obtaining the second position of the vehicle's acceleration sensor; multiplying the height value of the first position with the longitudinal acceleration of the vehicle to obtain a multiplication result; and using the ratio of the multiplication result to the height value of the second position as the first longitudinal acceleration of the first position.

[0030] By using the relative height of the sensor to each location as a variable and constructing an equivalent algorithm using the height ratio, the vibration transmission and amplification effects at different heights can be accurately simulated. This provides a high-fidelity and ergonomic data input layer for the overall comfort assessment process, and provides a reliable data foundation for subsequent calculations and evaluations.

[0031] It should be noted that the second position can be the driver's cab floor, and the height value refers to the height between the current position and the horizontal ground. For example, the height value of the first position refers to the height between the first position and the horizontal ground.

[0032] As an example, suppose the first position is the center of gravity of the front passenger's feet. The second position of the vehicle's acceleration sensor was obtained. The longitudinal acceleration of the vehicle is The height value of the first position is The height value of the second position is Set the height value of the first position to longitudinal acceleration of the vehicle Multiplying them together will give us the result of multiplication. Then, the result of the multiplication can be obtained. Height value of the second position The ratio is That is, the first longitudinal acceleration of the foot position (first position) is Similarly, the first longitudinal acceleration of the seat cushion position and the backrest position can be obtained by referring to the above method.

[0033] Step 103: Perform a Fourier transform on the first longitudinal acceleration at each of the first positions to obtain the vibration frequency at each of the first positions.

[0034] In some embodiments, step 103 described above can be implemented in the following manner: for each of the first positions, the following processing is performed respectively: preprocessing the first longitudinal acceleration of the first position to obtain the preprocessed first longitudinal acceleration; performing a Fourier transform on the preprocessed first longitudinal acceleration to obtain the complex frequency domain data of the first position; determining the single-sided amplitude spectrum of the first position based on the complex frequency domain data of the first position; and performing frequency band energy aggregation on the single-sided amplitude spectrum of the first position based on multiple preset frequency bands to obtain the vibration frequency of the first position.

[0035] Thus, by preprocessing the raw acceleration, the DC component can be eliminated and irrelevant high-frequency noise can be filtered out to ensure the purity and accuracy of the signal in subsequent analysis. Then, the clean signal is subjected to Fourier transform to convert the time-domain vibration information, which is difficult to analyze directly, into complex frequency domain data containing rich physical meaning. After that, the complex frequency domain information is converted into an intuitive spectrum containing only amplitude and positive frequency to clearly show the vibration intensity at each frequency point. Finally, through frequency band energy aggregation, all frequency points are mapped to a limited frequency band directly related to human subjective feelings, realizing the connection between physical vibration and physiological perception, and providing more reliable data support for subsequent comfort assessment.

[0036] It should be noted that the preset frequency bands are 1 / 3 octave bands.

[0037] As an example, assuming a scenario of frequent acceleration and deceleration in congested urban traffic, the system calculates the original acceleration curve (first longitudinal acceleration at the first position) for 30 seconds at the seat position, containing 2000 data points (200Hz sampling rate). First, the system removes DC offsets that do not represent vibration (e.g., shifting the entire curve back to zero), and uses a digital filter to eliminate extremely high-frequency vibrations (e.g., minute vibrations above 80Hz) that are almost imperceptible to humans and unrelated to motion sickness, thus obtaining a pre-processed first longitudinal acceleration, retaining only the main vibration components affecting ride comfort. Subsequently, a Fourier transform is performed on the pre-processed first longitudinal acceleration to obtain complex frequency domain data corresponding to the seat position, containing the vibration intensity and phase relationship at every minute frequency point between 0.5Hz and 80Hz. Then, the system calculates based on the complex frequency domain data... The system obtains the corresponding single-sided amplitude spectrum, which shows the correlation between different frequencies and amplitudes. For example, amplitude A is at 1 Hz, amplitude B is at 5 Hz, and amplitude C is at 20 Hz. Finally, the system aggregates the single-sided amplitude spectrum according to multiple preset frequency bands (e.g., 0.8-1 Hz is one frequency band, 1-1.25 Hz is the next frequency band, etc.). For example, the system aggregates the amplitudes of all frequency points between 0.8 Hz and 1 Hz to obtain the amplitude value of the first frequency band; then it aggregates the amplitudes between 1 Hz and 1.25 Hz to obtain the amplitude value of the second frequency band. Through frequency band energy aggregation, a set of structured data is obtained, which describes the vibration intensity of the seat cushion in several key frequency bands corresponding to human perception, that is, the vibration frequency of the seat cushion position. Amplitude aggregation can be done by adding, averaging, or taking the maximum or minimum value of the amplitudes of all frequency points between frequency bands, which is not specifically limited here.

[0038] Step 104: Map the vibration frequency of each first position to obtain the first weight corresponding to each first position.

[0039] It should be noted that the mapping process here can be implemented based on a preset mapping function or a pre-defined mapping table; no specific limitation is made here.

[0040] As an example, taking a pre-calibrated mapping table as an example, refer to Table 1. The vibration frequency of each first position is mapped according to the mapping table to obtain the weight coefficient (i.e., the first weight) for each first position. For example, if the vibration frequency of the foot position hits the frequency band with a center frequency of 0.5 f / Hz, then according to Table 1, the weight coefficient (first weight) corresponding to the foot position is 0.418. Here, the center frequency is a representative frequency value of a frequency band, representing a frequency range. For example, the upper cutoff frequency corresponding to a center frequency of 0.5 f / Hz is 0.561 f / Hz, and the lower cutoff frequency is 0.445 f / Hz; the upper cutoff frequency corresponding to a center frequency of 0.63 f / Hz is 0.707 f / Hz, and the lower cutoff frequency is 0.561 f / Hz.

[0041] Table 1 Weighting coefficients for each position in the 1 / 3 octave band

[0042] Step 105: Based on the first weight corresponding to each first position, perform a weighted calculation on the first longitudinal acceleration of each first position to obtain the second longitudinal acceleration of each first position.

[0043] It should be noted that the first weight is the set of weight coefficients for the first longitudinal acceleration at each moment within the first time period.

[0044] As an example, taking the foot position as the first position, assuming that the first weight is obtained by mapping the vibration frequency corresponding to the foot position, Then the first longitudinal acceleration at the foot position By performing a weighted calculation, the second longitudinal acceleration at the corresponding foot position can be obtained. for .

[0045] Step 106: For each first position, perform comfort calculation on the first position based on the second longitudinal acceleration of the first position to obtain the longitudinal comfort score of the first position within the first time period.

[0046] In some embodiments, the comfort calculation of the first position based on the second longitudinal acceleration of the first position in step 106 above, to obtain the longitudinal comfort score of the first position within the first time period, can be achieved by: integrating the fourth power of the second longitudinal acceleration of the first position within the first time period to obtain the integral result; and using the quarter power of the integral result as the longitudinal comfort score of the first position within the first time period.

[0047] In this way, the longitudinal comfort score of each position in the first time period is calculated by the preset comfort calculation formula, so as to objectively quantify the vehicle's parameter data into an intuitive evaluation score, and provide a data basis for the accurate evaluation of the vehicle's comfort level in the future.

[0048] As an example, the longitudinal comfort score for each first position within the first time period can be calculated using formula (1). .

[0049] (1) in, The equation for the change of longitudinal acceleration during the first time interval is given in units of... T represents the duration of the first time period.

[0050] Step 107: Merge the longitudinal comfort scores of each of the first positions to obtain the longitudinal comfort score of the vehicle within the first time period.

[0051] It should be noted that fusion can be a simple summation of the longitudinal comfort scores of each first position, a weighted summation, or a calculation based on a preset function; no specific limitation is made here.

[0052] In some embodiments, step 107 described above can be implemented as follows: based on a preset second weight, the squares of the longitudinal comfort scores at each of the first positions are weighted and summed to obtain a weighted summation result; the first half of the weighted summation result is taken as the longitudinal comfort score of the vehicle in the first time period.

[0053] In this way, by integrating the longitudinal comfort scores from different locations according to preset weights, the final overall score can truly and accurately reflect the comprehensive feelings of drivers and passengers inside the vehicle, providing a reliable reference for subsequent vehicle comfort level and intelligent control decisions.

[0054] As an example, the longitudinal comfort score of the vehicle in the first time period can be calculated by referring to formula (2). .

[0055] (2) in, The longitudinal comfort level of the foot position is rated. Rate the longitudinal comfort of the seat position. Rating the longitudinal comfort of the backrest position.

[0056] Step 108: Based on the longitudinal comfort score of the vehicle, determine the longitudinal comfort level of the vehicle within the first time period.

[0057] In some embodiments, step 108 described above can be implemented as follows: in response to the longitudinal comfort score of the vehicle being less than or equal to a first threshold, the longitudinal comfort level of the vehicle during the first time period is determined to be comfortable; in response to the longitudinal comfort score of the vehicle being greater than the first threshold and less than or equal to a second threshold, the longitudinal comfort level of the vehicle during the first time period is determined to be slightly uncomfortable; in response to the longitudinal comfort score of the vehicle being greater than the second threshold and less than or equal to a third threshold, the longitudinal comfort level of the vehicle during the first time period is determined to be uncomfortable; in response to the longitudinal comfort score of the vehicle being greater than the third threshold, the longitudinal comfort level of the vehicle during the first time period is determined to be very uncomfortable.

[0058] In this way, by converting continuous and accurate quantitative scores into non-continuous qualitative levels that are easy to understand and decide, a foundation can be laid for subsequent intelligent and automated control. Furthermore, by setting multiple thresholds to establish a clear quantitative evaluation standard for the levels, the converted levels become more objective and accurate.

[0059] It should be noted that the first threshold, the second threshold, and the third threshold can be preset values ​​or dynamically set according to different vehicles and driving types; no specific limitations are made here.

[0060] As an example, suppose the first threshold is 0.5. The second threshold is 2.0. The third threshold is 4.0. When the vehicle's comfort rating ≤0.5 When the vehicle's comfort level is 0.5, it is rated as "Comfort". < ≤2.0 When the vehicle's comfort level is determined to be slightly uncomfortable, and the comfort score is 2.0... < ≤4.0 When the vehicle's comfort level is determined to be uncomfortable, the vehicle's comfort score is... >4.0 At that time, the vehicle's comfort level was determined to be very uncomfortable.

[0061] In some embodiments, the degree of discomfort can be determined by analyzing passengers' micro-expressions (such as frowning), head shaking frequency, and body posture using biosensors inside the vehicle, or by detecting changes in passengers' heart rate using seat / steering wheel sensors, or by combining environmental information of the vehicle's driving environment (such as GPS and map data, weather data, etc.) to improve the accuracy of the longitudinal comfort level determination of the vehicle.

[0062] In some embodiments, after step 108, the following processes may also be performed: In response to the vehicle's driving mode being automatic mode and the vehicle's longitudinal comfort level being uncomfortable or very uncomfortable during the first time period, the vehicle's acceleration mode and braking mode are adjusted to comfort mode; in response to the vehicle's driving mode being power mode and the vehicle's longitudinal comfort level being very uncomfortable during the first time period, the driver is prompted to adjust the vehicle's acceleration mode and braking mode; in response to the vehicle's driving mode being comfort mode and the vehicle's longitudinal comfort level being uncomfortable or very uncomfortable during the first time period, the driver is prompted to accelerate and brake the vehicle slowly; in response to the vehicle's driving mode being personalized customization mode, the vehicle's driving is adjusted based on a preset adjustment strategy and the vehicle's longitudinal comfort level.

[0063] In this way, the vehicle response is dynamically adjusted based on the user's preset driving mode and the real-time monitored comfort level. By judging the dual conditions, the driver's driving intentions in different modes are respected, and effective intervention can be taken when comfort deteriorates. This achieves an intelligent balance between driving pleasure and riding comfort. Furthermore, by constructing a hierarchical and humanized intervention system, the driving experience can be significantly improved.

[0064] As an example, User A was driving a vehicle in "automatic mode" when the car entered a stop-and-go traffic jam in the city. Due to impatience, User A's driving became somewhat abrupt, with sudden acceleration and braking. After analyzing vibration data from the past 30 seconds, the intelligent cockpit system determined that the comfort level for the wife and child in the back seat had been reduced to "uncomfortable." At this point, the system did not issue any harsh warnings or prompts, but instead silently slowed down the throttle response and adjusted the regenerative braking to be gentler. User A felt that the driving became smoother, and the family members in the car also felt a significant reduction in shaking. The entire adjustment process was automated and imperceptible, thus ensuring the experience of the passengers.

[0065] In another example, User B was driving alone on a mountain road, enjoying the driving experience. The vehicle was in "Power Mode." After exiting a corner, User B accelerated rapidly, and the vehicle instantly experienced a strong push-back feeling. The system immediately detected that the comfort level instantly reached the peak of "very uncomfortable" within those few seconds. However, the system did not intervene directly. Instead, it prompted "The current longitudinal impact is relatively large. It is recommended to accelerate gently" to suggest that the user adjust the vehicle's acceleration and braking modes appropriately to reduce driving risks.

[0066] In another example, User C was practicing on a safe road outside the driving school with the vehicle in "Comfort Mode". When User C was practicing slowing down and stopping, he did not control the pressure when pressing the brake, and the vehicle swayed significantly before stopping. The system detected the comfort level as "slightly uncomfortable" multiple times. At this time, the system prompted that "you can try pressing the brake pedal 'hard first and then lightly,' and release it gently at the last stage, which can effectively suppress nose-diving", so as to prompt the user to accelerate and brake slowly to improve the driving experience.

[0067] In another example, User D is driving a vehicle in personalized mode. While User D is experiencing their customized mode, the system is working in the background. When User D accelerates with the accelerator floored, the system detects "very uncomfortable" and adjusts according to a preset strategy (e.g., automatically reducing maximum torque output by 10% when this level is reached). As a result, the vehicle's explosive power feels slightly "reduced" during the next acceleration, but it is still stronger than in normal mode. When User D brakes, due to the good comfort score, the system does not interfere with the gentle braking setting. This significantly improves the user's driving experience.

[0068] In some embodiments, after step 108, the following processing may also be performed: displaying the vehicle's longitudinal comfort level, driving mode adjustment suggestions, and driver's driving suggestions on the vehicle's central control display.

[0069] In this way, by visualizing abstract vehicle longitudinal comfort data in intuitive comfort levels (such as comfortable and uncomfortable), the cognitive threshold for users to understand the system's operating status can be greatly reduced. Users can understand the objective state of their current riding experience in an instant and clearly, thereby enhancing the friendliness of the human-machine experience and the acceptability of the system. Furthermore, by displaying driving mode adjustment suggestions and driver driving suggestions, clear and feasible solutions can be provided to users when poor vehicle comfort is detected. By integrating and displaying comfort levels, adjustment suggestions, and driving suggestions, the driver can be informed of the current vehicle driving status in real time and adjust the vehicle's driving status according to the displayed information, which can effectively improve the user's driving experience.

[0070] As an example, after the vehicle starts, a green, smooth waveform icon is displayed on the central control screen, labeled "Comfort," indicating that the vehicle's current comfort level is "Comfort." When driving into congested traffic, frequent acceleration and deceleration occur, and the vehicle suddenly accelerates to merge into a lane. At this point, the icon on the central control screen changes from a green waveform to an orange, somewhat bumpy waveform, and the text below changes from "Comfort" to "Mild Discomfort," indicating the vehicle's current comfort level is "Mild Discomfort." The central control screen then provides driving advice: "Sudden acceleration detected; it is recommended to gently press the pedal to improve comfort." Simultaneously, a mode adjustment suggestion appears: "Current riding experience is unsatisfactory; would you like to switch to 'Comfort Mode' for a smoother driving experience?" Below the suggestion are "Yes" and "No" options for selection, allowing the driver to choose according to their needs to effectively improve the driving experience.

[0071] In some embodiments, when the system predicts or detects uncomfortable vibrations (such as passing over road seams), it can instruct the suspension to actively adjust damping or height; when it detects signs of motion sickness in passengers, it can automatically adjust the internal and external air circulation and air volume, and release specific fragrances that help refresh or relieve nausea, or automatically adjust the ambient lighting to a soothing tone and brightness, and play gentle white noise or music of a specific frequency, using multi-sensory synergy to combat motion sickness; during long-distance navigation, the system can also plan a "high-comfort route" (prioritizing straight, flat roads, even if they are slightly longer), and when it is about to reach its destination and has sufficient battery power, it can actively reduce the intensity of energy recovery to prevent passengers from feeling uncomfortable due to excessive deceleration at the end of the journey.

[0072] In some embodiments, when a decrease in comfort is detected, the system can proactively remind you with a gentle voice: "Road bumps detected, you have been switched to a more comfortable driving mode." At the same time, passengers can provide feedback via voice: "I feel a little dizzy." Upon receiving feedback, the system can immediately activate the full set of anti-motion sickness procedures.

[0073] In some embodiments, the system can identify the passenger's identity information through seat sensors or facial recognition, and match the vehicle driving mode to the passenger information. For example, different vehicle driving modes can be used for "a wife who is prone to motion sickness" or "a son who seeks excitement".

[0074] In some embodiments, user feedback data can also be uploaded to the cloud to train a more accurate and universal comfort judgment model using big data, so as to more effectively identify the longitudinal comfort of the vehicle.

[0075] The above are embodiments of the method proposed in this application. Based on the same inventive concept, embodiments of this application also provide a vehicle longitudinal comfort detection device, the structure of which is as follows: Figure 2As shown.

[0076] Figure 2 This is a schematic diagram of the internal structure of a vehicle longitudinal comfort detection device provided in an embodiment of this application. Figure 2 As shown, the device includes: At least one processor 201; And a memory 202 that is communicatively connected to at least one processor; The memory 202 stores instructions that can be executed by at least one processor. The instructions are executed by at least one processor 201 to enable at least one processor 201 to perform the steps of the method corresponding to any of the above embodiments.

[0077] Some embodiments of this application provide corresponding to Figure 1 A non-volatile computer storage medium stores computer-executable instructions configured to perform the steps of the method corresponding to any of the above embodiments.

[0078] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments for IoT devices and media are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0079] The systems, media, and methods provided in this application are one-to-one correspondences. Therefore, the systems and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and media will not be repeated here.

[0080] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0081] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0082] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0083] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0084] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0085] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0086] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0087] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0088] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for detecting longitudinal comfort of a vehicle, characterized in that, The method includes: The longitudinal acceleration of the vehicle within a first time period is obtained, and based on the longitudinal acceleration of the vehicle, a first longitudinal acceleration is determined for each first position, the first position including the foot position of the first object and the seat position and backrest position of the vehicle. Perform a Fourier transform on the first longitudinal acceleration at each first position to obtain the vibration frequency at each first position, and perform a mapping process on the vibration frequency at each first position to obtain the first weight corresponding to each first position. Based on the first weight corresponding to each first position, the first longitudinal acceleration of each first position is weighted and calculated to obtain the second longitudinal acceleration of each first position; For each of the first positions, a comfort calculation is performed on the first position based on the second longitudinal acceleration of the first position to obtain a longitudinal comfort score for the first position within the first time period; The longitudinal comfort scores at each of the first positions are fused to obtain the longitudinal comfort score of the vehicle within the first time period, and the longitudinal comfort level of the vehicle within the first time period is determined based on the longitudinal comfort score of the vehicle.

2. The method according to claim 1, characterized in that, Determining the first longitudinal acceleration at each first position based on the vehicle's longitudinal acceleration includes: For each of the first positions, perform the following processing: Obtain the second position of the vehicle's acceleration sensor; Multiply the height value at the first position by the longitudinal acceleration of the vehicle to obtain the multiplication result; The ratio of the multiplication result to the height value of the second position is used as the first longitudinal acceleration of the first position.

3. The method according to claim 1, characterized in that, The step of performing a Fourier transform on the first longitudinal acceleration at each of the first positions to obtain the vibration frequency at each of the first positions includes: For each of the first positions, perform the following processing: The first longitudinal acceleration at the first position is preprocessed to obtain the preprocessed first longitudinal acceleration. Perform a Fourier transform on the preprocessed first longitudinal acceleration to obtain complex frequency domain data of the first position; Based on the complex frequency domain data at the first position, determine the one-sided amplitude spectrum at the first position; Based on multiple preset frequency bands, the single-sided amplitude spectrum at the first position is subjected to frequency band energy aggregation to obtain the vibration frequency at the first position.

4. The method according to claim 1, characterized in that, The comfort calculation for the first position based on the second longitudinal acceleration at the first position, to obtain a longitudinal comfort score for the first position within the first time period, includes: Integrate the fourth power of the second longitudinal acceleration at the first position during the first time period to obtain the integral result; The quarter-th power of the integral result is used as the longitudinal comfort score of the first position within the first time period.

5. The method according to claim 1, characterized in that, The step of fusing the longitudinal comfort scores at each of the first positions to obtain the longitudinal comfort score of the vehicle within the first time period includes: Based on the preset second weight, the squares of the longitudinal comfort scores for each first position are summed in a weighted manner to obtain the weighted summation result; The weighted summation result is raised to the power of half as the longitudinal comfort score of the vehicle during the first time period.

6. The method according to claim 1, characterized in that, Determining the longitudinal comfort level of the vehicle within the first time period based on the vehicle's longitudinal comfort score includes: In response to the longitudinal comfort score of the vehicle being less than or equal to a first threshold, the longitudinal comfort level of the vehicle during the first time period is determined to be comfortable. In response to the vehicle's longitudinal comfort score being greater than the first threshold and less than or equal to the second threshold, the longitudinal comfort level of the vehicle during the first time period is determined to be mild discomfort. In response to the vehicle's longitudinal comfort score being greater than the second threshold and less than or equal to the third threshold, the longitudinal comfort level of the vehicle during the first time period is determined to be uncomfortable. In response to the vehicle's longitudinal comfort score being greater than the third threshold, the longitudinal comfort level of the vehicle during the first time period is determined to be very uncomfortable.

7. The method according to claim 1, characterized in that, The method further includes: In response to the vehicle's driving mode being automatic and the vehicle's longitudinal comfort level being uncomfortable or very uncomfortable during the first time period, the vehicle's acceleration mode and braking mode are adjusted to comfort mode. In response to the vehicle's driving mode being power mode and the vehicle's longitudinal comfort level being very uncomfortable during the first time period, the driver is prompted to adjust the vehicle's acceleration and braking modes. In response to the vehicle's driving mode being comfort mode, and the vehicle's longitudinal comfort level being uncomfortable or very uncomfortable during the first time period, the driver is prompted to accelerate and brake the vehicle slowly. In response to the vehicle's driving mode being a personalized customization mode, the vehicle's driving is adjusted based on a preset adjustment strategy and the vehicle's longitudinal comfort level.

8. The method according to claim 1, characterized in that, The method further includes: The vehicle's central control display shows the vehicle's longitudinal comfort level, driving mode adjustment suggestions, and driving suggestions for the driver.

9. A vehicle longitudinal comfort testing device, characterized in that, The device includes: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform a vehicle longitudinal comfort detection method as described in any one of claims 1-8.

10. A computer storage medium storing computer-executable instructions, characterized in that, When the computer-executable instructions are executed, a vehicle longitudinal comfort detection method as described in any one of claims 1-8 is implemented.

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