A driving and riding body sensation quantitative evaluation method based on multi-dimensional perception and cognition integration

Abstract

The application discloses a kind of based on multi-dimensional perception and cognitive integration's driving and riding sense quantitative evaluation method, comprising the following steps: step one, acquisition vehicle dynamic physical signal;Step two, the original vehicle dynamic physical signal collected is filtered, and filter output value is obtained;Step three, based on Stevens power law, filter output value is converted into psychological perception intensity;Step four, psychological perception intensity is integrated by weighting, and overall body sense score is obtained.The application has the characteristics of providing more objective evaluation of driving and riding sense and improving individualized adaptability.

Description

Technical Field

[0001] This invention relates to the field of vehicle engineering technology, and more specifically, to a quantitative evaluation method for driving and riding experience based on multi-dimensional perception and cognitive integration. Background Technology

[0002] Driving experience, or ride comfort, is the sense of a car's sporty quality that users can directly perceive through test drives. It's a core element of a brand's "golden three minutes" effect and a key competitive advantage for automotive products. In today's highly homogenized automotive market, driving experience has evolved from a technical performance indicator to a core strategic asset determining product success and brand value. Developing a driving experience is crucial for automotive product performance and quality. However, since driving experience is largely determined by subjective feelings, traditional evaluation methods rely heavily on subjective ratings from professional reviewers. These subjective evaluations are highly susceptible to individual differences and environmental factors, resulting in poor repeatability, difficulty in quantifying the results, and an inability to directly apply them to digital development processes. Furthermore, they fail to systematically explain the non-linear relationship between physical performance and subjective feelings. Therefore, researching a quantifiable method for evaluating driving experience is essential.

[0003] Regarding the quantification of driving and riding comfort, Korean scholars Kim, MinSeok, et al., in their paper "Correlation between Subjective and Objective Assessments of Ride Comfort," studied the correlation between heart rate variability and subjective "discomfort" scores under vertical vibration at a specific frequency, providing a basis for establishing a quantitative relationship between vibration, physiology, and subjective perception. Meanwhile, Wassim El Falou et al., in their paper "Evaluation of driver discomfort during long-duration car..." The patent "driving" utilizes electroencephalography (EEG) to analyze changes in brainwave segments (such as theta waves and alpha waves) related to fatigue and discomfort during long-term driving, quantifying the decline in driving comfort. Patent CN201910514910.4 proposes an objective quantitative method for subjective evaluation of idling vibration in air-brake light-duty trucks, including the acquisition of vibration signals from a prototype vehicle, processing methods for subjective and objective data, and establishing a correlation model using the Spearman correlation coefficient through correlation analysis of subjective evaluation results and objective data, resulting in an objective quantitative mathematical model for subjective evaluation of idling vibration in air-brake light-duty trucks. Patent CN201810137120.4 proposes an objective quantitative method for subjective evaluation of steady-state noise in electric vehicles, using psychoacoustic parameters as objective evaluation parameters for idling noise in electric vehicles, solving the problem that sound pressure level cannot fundamentally characterize noise signals. It is evident that while some existing studies have attempted to establish a mapping relationship between objective physical quantities and subjective feelings, most of them focus only on a single sensory channel or do not fully consider the advanced integrative role of cognitive psychology in the formation of feelings. Furthermore, existing studies often consider subjective feelings and cannot ensure objective driving and riding evaluations. Summary of the Invention

[0004] The purpose of this invention is to design and develop a quantitative evaluation method for driving and riding experience based on multi-dimensional perception and cognitive integration. By converting vehicle dynamic physical signals into psychological perception, the method comprehensively considers driving and riding experience, forms an objective evaluation, and improves personalized adaptability.

[0005] The technical solution provided by this invention is as follows: A quantitative evaluation method for driving and riding experience based on multi-dimensional perception and cognitive integration includes the following steps: Step 1: Collect vehicle dynamic physical signals; Step 2: Filter the collected raw vehicle dynamic physical signals to obtain the filtered output value; Step 3: Convert the filtered output value into psychological perception intensity based on Stevens' power law: ; In the formula, For the first The psychological perception intensity of vehicle dynamic physical signals. For the first The constant coefficients of the vehicle dynamic physical signals. For the first The power exponent of the vehicle dynamic physical signal. The filtered first Vehicle dynamic physical signals, and or These correspond to the vertical acceleration, pitch angle, roll angle, longitudinal acceleration, lateral acceleration, and steering wheel torque at the vehicle's center of gravity, respectively. Step 4: Weight and integrate the intensity of psychological perception to obtain the overall tactile score: ; In the formula, A collection of vehicle dynamic physical signals. For the first Weighting coefficients for vehicle dynamic physical signals.

[0006] Preferably, the vehicle dynamic physical signals include: Vertical acceleration, pitch angle, roll angle, longitudinal acceleration, lateral acceleration, and steering wheel torque at the vehicle's center of gravity.

[0007] Preferably, the sampling frequency of the vehicle dynamic physical signal is greater than or equal to 100Hz.

[0008] Preferably, the filtering in step two is one or more of the following: low-pass filter, moving average filter, Kalman filter, and wavelet denoising.

[0009] Preferably, the weighting coefficients of the vehicle dynamic physical signals satisfy: .

[0010] Preferably, the weighting coefficients of the vehicle dynamic physical signals are adjusted according to the driving style.

[0011] Preferably, the driving styles include sport, comfort, and balanced.

[0012] Preferably, when the driving style is sporty, the weighting coefficient for vertical perception is 0.25, the weighting coefficient for pitch perception is 0.05, the weighting coefficient for roll perception is 0.1, the weighting coefficient for longitudinal perception is 0.15, the weighting coefficient for lateral perception is 0.25, and the weighting coefficient for steering perception is 0.2.

[0013] Preferably, when the driving style is comfort, the weighting coefficient for vertical perception is 0.35, the weighting coefficient for pitch perception is 0.05, the weighting coefficient for roll perception is 0.1, the weighting coefficient for longitudinal perception is 0.11, the weighting coefficient for lateral perception is 0.15, and the weighting coefficient for steering perception is 0.25.

[0014] Preferably, when the driving style is balanced, the weighting coefficient for vertical perception is 0.3, the weighting coefficient for pitch perception is 0.05, the weighting coefficient for roll perception is 0.1, the weighting coefficient for longitudinal perception is 0.15, the weighting coefficient for lateral perception is 0.2, and the weighting coefficient for steering perception is 0.2.

[0015] The beneficial effects of this invention are as follows: (1) The present invention designs and develops a quantitative evaluation method for driving and riding sensation based on multidimensional perception and cognitive integration, which comprehensively considers the vertical balance, dynamics and control effort of driving and riding. It transforms objective physical stimuli such as vertical acceleration, pitch angle, roll angle, longitudinal acceleration, lateral acceleration and steering wheel force into psychological perception by introducing Stevens' power law, and forms an objective evaluation of driving and riding sensation through weighted synthesis. This objective evaluation of driving and riding sensation is realized, and the driving and riding sensation is directly optimized in the design optimization of automobile products, thereby improving product competitiveness.

[0016] (2) The driving experience quantitative evaluation method based on multidimensional perception and cognitive integration designed and developed in this invention is the first to systematically apply Stevens' power law to multi-channel driving experience modeling, filling the gap in the lack of psychophysical parameters in the quantitative evaluation of vehicle dynamics experience; it proposes a weighted linear integration model based on driving situation and user expectations, and by constraining the adjustability and normalization conditions of the weight vector, the model has both personalized adaptability and maintains mathematical analyzability and optimizability.

[0017] (3) The present invention designs and develops a quantitative evaluation method for driving and riding experience based on multi-dimensional perception and cognitive integration, which provides a psychological evaluation basis for experience-driven design of vehicle dynamics performance and can be seamlessly integrated with vehicle multi-body dynamics simulation and active suspension control strategy optimization. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the quantitative evaluation method for driving and riding experience based on multi-dimensional perception and cognitive integration as described in this invention. Figure 2 This is a schematic diagram illustrating the specific process of the quantitative evaluation method for driving and riding experience based on multi-dimensional perception and cognitive integration as described in this invention. Figure 3This is a schematic diagram of the waveform of the vertical acceleration signal after Kalman filtering under sporty driving style as described in this invention; Figure 4 This is a schematic diagram of the calculation results of the perception intensity of each perception channel after conversion by Stevens' power law under the sporty driving style described in this invention. Figure 5 This is a schematic diagram of the overall driving and riding experience score obtained by weighted summation of the perception intensity of each channel under a sporty driving style, as described in this invention. Figure 6 This is a bar chart illustrating the weighted coefficient values ​​for each perception channel under a sporty driving style as described in this invention. Figure 7 This is a schematic diagram showing the effect curves of the six channels of signals after filtering under a sporty driving style as described in this invention; Figure 8 This is a schematic diagram summarizing the statistical indicators of the evaluation results under the sporty driving style described in this invention. Detailed Implementation

[0019] The present invention will now be described in further detail with reference to the accompanying drawings, so that those skilled in the art can implement it based on the description.

[0020] like Figure 1 , Figure 2 As shown, the present invention provides a quantitative evaluation method for driving and riding experience based on multi-dimensional perception and cognitive integration, comprising the following steps: Step 1: Collect vehicle dynamic physical signals to obtain signal sets. ; in, Let be the vertical acceleration at the vehicle's center of mass. For pitch angle, The tilt angle, For longitudinal acceleration, For lateral acceleration, Steering wheel torque; All of the above signals were collected by sensors. , , Constitutes vertical dynamic signals, For longitudinal dynamic signals, This is a transverse dynamic signal. Human-computer interaction signals; In this embodiment, the sampling frequency is This ensures complete capture of transient human sensory responses.

[0021] Step 2: Filter the collected raw vehicle dynamic physical signals to remove sensor noise, environmental interference and non-human-sensory frequency band components, and extract the real physical quantities related to the driving and riding experience.

[0022] The filtering process can employ a combination of any one or more of the following methods: Low-pass filters (such as Butterworth filters, with a cutoff frequency of 20~30 Hz), moving average filters, Kalman filters, and wavelet denoising.

[0023] In this embodiment, the Kalman filter method is used for filtering. It is a recursive optimal state estimation algorithm consisting of a prediction step and an update step. For a general linear discrete-time system: Equations of state:

[0024] Observation equation:

[0025] In the formula, for The state vector at time t, Here is the state transition matrix. To control the input matrix, To control the input, The noise is a process noise and follows a normal distribution. , for The observation vector at time t, For the observation matrix, The observed noise follows a normal distribution. .

[0026] The Kalman filter recursive process is as follows: ① Initialization: Set initial state estimation and the initial error covariance matrix ; ②Prediction steps (time update): ; ; in, Based on Always Prior state estimation at time 10:00 Let be the prior error covariance matrix. for The state at any given moment, Let be the process noise covariance.

[0027] ③ Update steps (measurement update): Calculate the Kalman gain: ; Updated state estimate: ; Update error covariance: ; in, Here is the Kalman gain matrix. For new information (measurement residuals). For posterior state estimation, Let be the posterior error covariance matrix. It is the identity matrix. To predict the error covariance, To observe the noise covariance.

[0028] ④ Output filtered physical quantities: From posterior state estimation Extract the corresponding component and use it as the filtered output value of the physical quantity. ; Therefore, Kalman filtering of the vehicle's dynamic physical signals is as follows: State vector: ; Equations of state: ; Observation equation: ; predict: ; renew: ; in, For the first Vehicle dynamic physical signals, For the first The observation noise covariance of vehicle dynamic physical signals, For the first The process noise covariance of the vehicle dynamic physical signals, and or ; Step 3: Use Stevens' power law to convert the filtered output value into psychological perceived intensity: ; In the formula, For the first The psychological perception intensity of vehicle dynamic physical signals. For the first The constant coefficients of the vehicle dynamic physical signals. For the first The power exponent of the vehicle dynamic physical signal. The filtered first Vehicle dynamic physical signals; Specifically: ① Vertical stability: Vertical acceleration, pitch angle, and roll angle together determine the "vertical stability," which can be modeled as follows: ; ; ; in, For vertical sensing intensity, For vertical sensing constant coefficients. For vertically perceptual power exponent. This is the filtered vertical acceleration; To sense the intensity of pitch, For pitch perception constant coefficients, To perceive the power exponent by tilting the head. The pitch angle after filtering; For tilt sensing intensity, The roll sensing constant coefficient, The power exponent for tilt perception. This is the filtered roll angle; ②Sense of dynamism: Longitudinal acceleration and lateral acceleration correspond to "motion dynamics": ; ; in, For longitudinal sensing intensity, For longitudinal perception constant coefficients, For vertical perception of the power exponent. This is the filtered longitudinal acceleration; For lateral sensing intensity, For lateral sensing constant coefficients, To perceive the power exponent laterally, This is the filtered lateral acceleration.

[0029] ③ The feeling of control and effort: Steering wheel torque reflects the effort required by the driver: ; Parameter settings: To enhance the intensity of the steering perception, For steering perception constant coefficient, To shift towards perceptual power exponents. This is the filtered steering wheel torque.

[0030] Step 4: Based on the driving context and user expectations, weighted and integrated the intensity of psychological perception to obtain an overall experiential score. Defined as the weighted sum of the intensities of each psychological perception: ; In the formula, The set of vehicle dynamic physical signals, i.e. , For the first The weighting coefficients for the vehicle dynamic physical signals satisfy: ; Right now: ; in, These are the weighting coefficients for vertical perception. The weighting coefficient for pitch perception. The weighting coefficient for roll perception. These are the weighting coefficients for longitudinal perception. These are the weighting coefficients for lateral perception. The weighting coefficients for steering perception.

[0031] In this embodiment, , , , , , , , , , , , .

[0032] In this embodiment, the constant coefficients and power exponents are shown in Table 1: Table 1. Values ​​of constant coefficients and power exponents

[0033] In this embodiment, the weighting coefficients for the three typical driving styles are configured as shown in Table 2: Table 2 shows the weighting coefficients for three typical driving styles.

[0034] That is, the sporty type emphasizes lateral movement. ) and steering ( Comfort models emphasize vertical stability. ) and steering compliance ( ).

[0035] To verify the quantitative evaluation method for driving and riding experience provided by this invention, data under a sporty driving style was processed, such as... Figure 3 The waveform of the vertical acceleration signal after Kalman filtering clearly shows that the filtering effectively eliminated high-frequency noise components in the original signal; for example... Figure 4 The calculation results of the sensing intensity of each sensing channel (including vertical acceleration, pitch angle, roll angle, longitudinal acceleration, lateral acceleration, and steering wheel torque) after conversion using Stevens' power law are given; such as Figure 5 The overall driving experience score is shown after weighted summation of the perceived intensity from each channel. It can be seen that within 10 seconds of the time signal, the overall driving experience score ranges from 0.5 to -0.5, reaching a maximum of 0.42 at 0.3 seconds and a minimum of -0.46 at 9.5 seconds. The score varies at different time points depending on the input signal, allowing for a detailed score throughout the entire driving experience. Figure 6 The weighting coefficient values ​​for each sensing channel are listed, such as... Figure 7 The effects of filtering on all six signal channels are presented, facilitating a direct evaluation of the filtering effectiveness. The filtered signal curves for each channel are noticeably smoother, and high-frequency noise components are effectively suppressed. After filtering, the maximum amplitude of vertical acceleration is approximately 1.5 m / s², the maximum amplitude of pitch angle is approximately 1.8°, the maximum amplitude of roll angle is approximately 2.1°, the maximum amplitude of longitudinal acceleration is approximately 1.2 m / s², the maximum amplitude of lateral acceleration is approximately 2.8 m / s², and the maximum amplitude of steering wheel torque is approximately 3.5 N·m. This verifies the effectiveness and adaptability of the Kalman filter algorithm in this application scenario. Figure 8 The statistical indicators for the evaluation results are summarized, including the mean, maximum, minimum and standard deviation. The mean of this set of data is 0.0025, the maximum is 0.42, the minimum is -0.46 and the standard deviation is 0.17, which are used to measure the central tendency and dispersion of the evaluation results.

[0036] This invention presents a quantitative evaluation method for driving and riding experience based on multi-dimensional perception and cognitive integration. It employs a three-layer architecture: a physical stimulus layer collects vehicle dynamic signals; a psychological perception layer utilizes Stevens' power law to convert physical quantities into psychological perception intensity, reflecting differences in human sensitivity to different stimuli; and a cognitive evaluation layer uses mental models and the concept of expectation conformity, combined with cognitive attention allocation in driving situations, to perform weighted integration and output an overall objective score for the driving and riding experience. This invention solves the problems of traditional driving and riding experience evaluation relying on subjective experience and lacking a unified quantitative model. It provides a quantifiable design tool for optimizing vehicle dynamic performance, offering high predictive accuracy and applicability to vehicle development, tuning, and quality evaluation.

[0037] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and embodiments shown and described herein.

Claims

1. A quantitative evaluation method for driving and riding experience based on multi-dimensional perception and cognitive integration, characterized in that, Includes the following steps: Step 1: Collect vehicle dynamic physical signals; Step 2: Filter the collected raw vehicle dynamic physical signals to obtain the filtered output value; Step 3: Convert the filtered output value into psychological perceived intensity based on Stevens' power law: ； In the formula, For the first The psychological perception intensity of vehicle dynamic physical signals. For the first The constant coefficients of the vehicle dynamic physical signals. For the first The power exponent of the vehicle dynamic physical signal. The filtered first Vehicle dynamic physical signals, and or These correspond to the vertical acceleration, pitch angle, roll angle, longitudinal acceleration, lateral acceleration, and steering wheel torque at the vehicle's center of gravity, respectively. Step 4: Weight and integrate the intensity of psychological perception to obtain the overall tactile score: ； In the formula, A collection of vehicle dynamic physical signals. For the first Weighting coefficients for vehicle dynamic physical signals.

2. The method for quantitative evaluation of driving and riding experience based on multi-dimensional perception and cognitive integration as described in claim 1, characterized in that, The vehicle dynamic physical signals include: Vertical acceleration, pitch angle, roll angle, longitudinal acceleration, lateral acceleration, and steering wheel torque at the vehicle's center of gravity.

3. The quantitative evaluation method for driving and riding experience based on multi-dimensional perception and cognitive integration as described in claim 2, characterized in that, The sampling period and sampling frequency of the vehicle dynamic physical signal are greater than or equal to 100Hz.

4. The quantitative evaluation method for driving and riding experience based on multi-dimensional perception and cognitive integration as described in claim 3, characterized in that, The filtering in step two is one or more of the following: low-pass filter, moving average filter, Kalman filter, and wavelet denoising.

5. The quantitative evaluation method for driving and riding experience based on multi-dimensional perception and cognitive integration as described in claim 4, characterized in that, The weighting coefficients of the vehicle dynamic physical signals satisfy: 。 6. The quantitative evaluation method for driving and riding experience based on multi-dimensional perception and cognitive integration as described in claim 5, characterized in that, The weighting coefficients of the vehicle dynamic physical signals are adjusted according to driving style.

7. The quantitative evaluation method for driving and riding experience based on multi-dimensional perception and cognitive integration as described in claim 6, characterized in that, The driving styles include sporty, comfortable, and balanced.

8. The quantitative evaluation method for driving and riding experience based on multi-dimensional perception and cognitive integration according to claim 7, characterized in that, When the driving style is Sport, the weighting coefficient for vertical perception is 0.25, the weighting coefficient for pitch perception is 0.05, the weighting coefficient for roll perception is 0.1, the weighting coefficient for longitudinal perception is 0.15, the weighting coefficient for lateral perception is 0.25, and the weighting coefficient for steering perception is 0.

2.

9. The quantitative evaluation method for driving and riding experience based on multi-dimensional perception and cognitive integration as described in claim 8, characterized in that, When the driving style is Comfort, the weighting coefficient for vertical perception is 0.35, the weighting coefficient for pitch perception is 0.05, the weighting coefficient for roll perception is 0.1, the weighting coefficient for longitudinal perception is 0.11, the weighting coefficient for lateral perception is 0.15, and the weighting coefficient for steering perception is 0.

25.

10. The quantitative evaluation method for driving and riding experience based on multi-dimensional perception and cognitive integration according to claim 9, characterized in that, When the driving style is balanced, the weighting coefficient for vertical perception is 0.3, the weighting coefficient for pitch perception is 0.05, the weighting coefficient for roll perception is 0.1, the weighting coefficient for longitudinal perception is 0.15, the weighting coefficient for lateral perception is 0.2, and the weighting coefficient for steering perception is 0.2.

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