Vehicle design method and device, electronic equipment and computer readable storage medium

Abstract

This application provides a vehicle design method, apparatus, electronic device, and computer-readable storage medium. The method includes: acquiring driver posture change data under multiple visual field scenarios; determining upper and lower visual field thresholds that trigger posture compensation based on the visual field angles and posture change data corresponding to the multiple visual field scenarios; and determining the vehicle's geometric design constraints based on the upper and lower visual field thresholds for vehicle design. Therefore, this method introduces the driver's posture compensation behavior as an objective and quantitative criterion to improve the flexibility and practicality of upper and lower visual field design. Simultaneously, based on the aforementioned criterion, it constructs geometric design constraints for the vehicle, thereby identifying and avoiding upper and lower visual field design schemes that induce unnatural driver posture adjustments in the early stages of vehicle design, reducing rectification costs and design cycle.

Description

Technical Field

[0001] This application relates to the field of product manufacturing technology, and in particular to a vehicle design method, apparatus, electronic device, and computer-readable storage medium. Background Technology

[0002] In related technologies, during the vehicle design process, fixed field of view requirements are first set according to regulations or company standards, and static field of view calculations are performed based on a single or limited human body model. Subsequently, in the prototype stage, verification is conducted through subjective evaluation, user testing, and competitor benchmarking to determine the final vertical field of view design scheme for subsequent vehicle production. However, this method designs the vertical field of view based on whether the field of view meets geometric visibility requirements, resulting in poor flexibility and high rectification costs and long cycles. Summary of the Invention

[0003] This application provides a method, apparatus, electronic device, and computer-readable storage medium for designing the vertical field of view of a vehicle, aiming to improve the problems of poor design flexibility, high rectification cost, and long cycle of vertical field of view in related technologies.

[0004] The first aspect of this application provides a vehicle design method, including: acquiring driver posture change data under multiple field-of-view scenarios; determining upper and lower field-of-view thresholds for triggering posture compensation based on the field-of-view angles and posture change data corresponding to the multiple field-of-view scenarios; and determining geometric design constraints of the vehicle based on the upper and lower field-of-view thresholds to design the vehicle.

[0005] According to the vehicle design method of this application embodiment, driver posture change data under multiple visual scenarios is acquired. Based on the visual angles and posture change data corresponding to the multiple visual scenarios, upper and lower visual field thresholds that trigger posture compensation are determined. Then, the geometric design constraints of the vehicle are determined based on the upper and lower visual field thresholds for vehicle design. Thus, this method introduces the driver's posture compensation behavior as an objective and quantifiable criterion to improve the flexibility and practicality of upper and lower visual field design. Simultaneously, based on the aforementioned criterion, geometric design constraints of the vehicle are constructed, thereby identifying and avoiding upper and lower visual field design schemes that induce unnatural driver posture adjustments in the early stages of vehicle design, reducing rectification costs and design cycle.

[0006] In some embodiments of this application, obtaining driver posture change data under multiple visual field scenarios includes: obtaining the driver's initial posture; determining the driver's vertical visual field reference angle based on the vehicle's preset geometric design and the initial posture; performing angle convergence on the vertical visual field reference angle according to multiple visual field scenarios, and obtaining the driver's actual posture under the corresponding visual field scenario; and determining posture change data under the corresponding visual scene based on the initial posture and the actual posture.

[0007] Based on the above technical solution, the reference angles of the upper and lower fields of view can be converged according to various field of view scenarios to obtain the driver's actual sitting posture under different field of view scenarios, identify the corresponding sitting posture change data, improve the recognition accuracy of sitting posture compensation actions, and provide data guarantee for the design accuracy of upper and lower fields of view.

[0008] In some embodiments of this application, obtaining the driver's actual sitting posture in the corresponding visual field scene includes: determining at least one target visual task in the visual field scene and triggering a task guidance instruction to guide the driver to perform at least one target visual task; collecting sampled values ​​of the driver's sitting posture parameters when performing the target visual task; and determining the driver's actual sitting posture in the visual field scene based on the sampled values ​​of the sitting posture parameters.

[0009] Based on the above technical solution, it is possible to guide the driver to perform natural driving or observation tasks under different vertical field of vision conditions, collect the driver's actual sitting posture when performing the corresponding task, identify the amount of change in the driver's sitting posture, characterize the driver's posture compensation behavior to improve the field of vision, and improve the scene test effect.

[0010] In some embodiments of this application, the parameter types of the initial sitting posture and the actual sitting posture include eye point position, torso posture parameters, and seat adjustment parameters. Determining the sitting posture change data in the corresponding visual scene based on the initial sitting posture and the actual sitting posture includes: determining the amount of eye point position change, torso posture change, and seat adjustment change corresponding to the visual scene based on the initial sitting posture and the actual sitting posture in the visual scene; and determining the sitting posture change data in the visual scene based on the amount of eye point position change, torso posture change, and seat adjustment change.

[0011] Based on the above technical solution, it is possible to recognize sitting posture changes based on eye position, torso posture parameters and seat adjustment parameters, thereby improving the recognition accuracy of sitting posture changes.

[0012] In some embodiments of this application, determining the upper and lower field of view thresholds that trigger posture compensation based on field of view angles and posture change data corresponding to multiple field of view scenarios includes: determining a target mapping relationship based on field of view angles and posture change data corresponding to multiple field of view scenarios, wherein the target mapping relationship characterizes the relationship between upper and lower field of view angles and posture change data; determining the upper and lower field of view critical intervals that trigger posture compensation based on the target mapping relationship; and determining the upper and lower field of view thresholds based on the upper and lower field of view critical intervals.

[0013] Based on the above technical solution, the mapping relationship between visual field angle and sitting posture change data can be constructed through data fitting, and discrete samples can be converted into sitting posture fluctuation models under various visual field conditions, thereby improving the recognition accuracy of the upper and lower visual field critical intervals and upper and lower visual field thresholds that trigger sitting posture compensation.

[0014] In some embodiments of this application, there are multiple drivers, and the field of vision angles corresponding to the field of vision scenarios include upper field of vision angles and lower field of vision angles. Determining the target mapping relationship based on the field of vision angles and posture change data corresponding to multiple field of vision scenarios includes: performing data fitting on the upper field of vision angles corresponding to multiple field of vision scenarios and the posture change data of multiple drivers in the corresponding field of vision scenarios to obtain a first fitting curve between the upper field of vision angles and posture change data; performing data fitting on the lower field of vision angles corresponding to multiple field of vision scenarios and the posture change data of multiple drivers in the corresponding field of vision scenarios to obtain a second fitting curve between the lower field of vision angles and posture change data; and determining the target mapping relationship based on the first fitting curve and the second fitting curve.

[0015] Based on the above technical solution, the mapping relationship between the upper field of view angle and the sitting posture compensation amount and the lower field of view angle and the sitting posture compensation amount can be obtained by curve fitting based on the sampling data of different drivers and different field of view scenarios, so as to improve the accuracy of the mapping relationship and provide a data foundation for vehicle design.

[0016] In some embodiments of this application, the critical intervals of the upper and lower visual fields include the upper visual field critical interval and the lower visual field critical interval. Determining the upper and lower visual field critical intervals that trigger posture compensation based on the target mapping relationship includes: identifying the inflection point of the first curve based on the first fitting curve, and determining the upper visual field critical interval based on the inflection point of the first curve; identifying the inflection point of the second curve based on the second fitting curve, and determining the lower visual field critical interval based on the inflection point of the second curve.

[0017] Based on the above technical solution, by identifying the inflection points of the first and second fitted curves, the location of the sitting posture compensation action is determined, which is then used as the corresponding visual field critical interval. This allows for the determination of the design boundary by utilizing the structural characteristics of the data itself, thereby improving decision-making efficiency and rationality.

[0018] A second aspect of this application provides a vehicle design apparatus, comprising: an acquisition module for acquiring driver posture change data under multiple field-of-view scenarios; a determination module for determining upper and lower field-of-view thresholds for triggering posture compensation based on the field-of-view angles and posture change data corresponding to the multiple field-of-view scenarios; and a design module for determining geometric design constraints of the vehicle based on the upper and lower field-of-view thresholds, so as to design the vehicle.

[0019] A third aspect of this application provides an electronic device, including a processor and a memory, wherein the memory is used to store a computer program; and the processor is used to execute the program stored in the memory to implement the vehicle design method provided in the first aspect embodiment.

[0020] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the vehicle design method provided in the first aspect of this application. Attached Figure Description

[0021] Figure 1 This is a flowchart of a vehicle design method provided in one embodiment of this application; Figure 2 This is a flowchart of a vehicle design method provided in a specific embodiment of this application; Figure 3 This is a structural diagram of the vehicle design device provided in the embodiments of this application; Figure 4 This is a structural diagram of the electronic device provided in the embodiments of this application. Detailed Implementation

[0022] To make the technical problems, technical solutions, and beneficial effects solved by this application clearer, the following detailed description is provided in conjunction with embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0023] The design methods, apparatus, electronic devices, and computer-readable storage media of the vehicle proposed in this application are described below with reference to the accompanying drawings.

[0024] Figure 1 This is a flowchart of a vehicle design method provided in one embodiment of this application.

[0025] Combination Figure 1 As shown, the vehicle design method of this application embodiment includes the following steps: S1 acquires data on the driver's posture changes in multiple field-of-view scenarios.

[0026] Specifically, the field of vision scenario refers to the driver's field of vision conditions with different upper and lower field of vision angles.

[0027] Posture change data can be determined based on the data difference between the driver's actual posture and initial posture under different visual scenarios, and is used to characterize the driver's posture compensation actions under different visual scenarios. Among them, the initial posture represents the driver's standard driving posture in the driver's seat, and the optimal posture represents the driver's preferred posture under the influence of no external factors.

[0028] Taking the driver's sitting posture as an example, the process involves several steps. First, a depth camera captures real-time images of the driver, generating corresponding RGB (Red, Green, Blue) and depth images. The driver's joints (such as head, shoulders, elbows, and hips) are identified and located from the RGB images. The 2D (Two-Dimensional) joint coordinates are combined with the depth information to calculate the actual spatial position (X, Y, Z coordinates) of each joint. This position is then used as the driver's torso posture parameters to characterize their sitting posture. Second, in acquiring posture change data, initial values ​​of the driver's torso posture parameters are first collected under a standard driving posture to serve as the initial sitting posture. Then, by simulating different visual scenarios, the actual values ​​of the driver's torso posture parameters are collected in real-time to represent the actual sitting posture. The difference between the actual and initial values ​​of the torso posture parameters is used as the posture change data.

[0029] Understandably, when the data on changes in sitting posture in the field of vision is 0, it is assumed that the driver's posture has not changed in the current field of vision, and the vertical field of vision angle corresponding to the current field of vision can meet the driver's driving visual needs; when the data on changes in sitting posture in the field of vision is not 0, it is assumed that the driver's posture has changed in the current field of vision, and the vertical field of vision angle corresponding to the current field of vision requires the driver to adjust their posture in order to meet the driver's driving visual needs.

[0030] S2 determines the upper and lower field of view thresholds that trigger posture compensation based on the field of view angles and posture change data corresponding to multiple field of view scenarios.

[0031] Specifically, the upper and lower field-of-view thresholds include the upper field-of-view threshold and the lower field-of-view threshold. The upper field of view refers to the angle formed by the driver's line of sight through the upper edge of the windshield and the horizontal line. The lower field of view refers to the angle formed by the driver's line of sight through the hood / dashboard and the horizontal line. Considering driving safety and perceived comfort, the upper field of view usually needs to meet the observation requirements of traffic lights, and the lower field of view needs to meet the observation requirements of the road surface or obstacles at a certain height in front of the vehicle (such as 3m to 6m in front of the tire contact point).

[0032] Because each person has different needs for perceiving vertical and horizontal vision, the system monitors the driver's posture changes with the field of vision angle based on data from multiple vision scenarios and corresponding changes in posture. This allows the system to identify the driver's posture compensation actions during changes in field of vision angle. Specifically, if a driver cannot meet their driving needs in a normal posture (e.g., accurately identifying traffic lights (upper field of vision) or the near-end of the road ahead (lower field of vision)), they need to adjust their posture to meet these needs. The upper and lower field of vision angles that trigger this adjustment are used as thresholds for vertical and horizontal vision. The field of vision scenarios can be divided based on different vertical and horizontal field of vision angles, or further combined with different working conditions; there are no specific restrictions.

[0033] S3 determines the geometric design constraints of the vehicle based on the upper and lower field of view thresholds in order to design the vehicle.

[0034] Specifically, considering that the limited upper field of vision mainly originates from the front crossbeam of the roof, the sun visor, the base of the interior rearview mirror, and the black border area of ​​the windshield, the geometric design constraints of these components can be based on the upper field of vision threshold that triggers driver posture compensation. Similarly, considering that the limited lower field of vision mainly originates from the hood and dashboard, the geometric design constraints of these components can be based on the lower field of vision threshold that triggers driver posture compensation, thereby executing the vehicle's design actions.

[0035] This embodiment acquires data on driver posture changes under different vertical field of vision conditions, identifies the critical field of vision threshold that triggers significant driver posture compensation behavior, and maps this threshold back to design constraints for vehicle geometry parameters. This allows for early-stage vehicle design assessment of whether a design scheme will induce unnatural driver posture adjustments. This method can quantitatively determine the acceptability of a vehicle's vertical field of vision without relying on prototypes or later subjective evaluations. It is applicable to the conceptual design and overall layout stages of passenger vehicles and smart cockpits, thus solving the technical problems of poor flexibility, high rectification costs, and long cycles in related technologies for vertical field of vision design.

[0036] In some embodiments of this application, obtaining driver posture change data under multiple visual field scenarios includes: obtaining the driver's initial posture; determining the driver's vertical visual field reference angle based on the vehicle's preset geometric design and the initial posture; performing angle convergence on the vertical visual field reference angle according to multiple visual field scenarios, and obtaining the driver's actual posture under the corresponding visual field scenario; and determining posture change data under the corresponding visual field scenario based on the initial posture and the actual posture.

[0037] Specifically, the driver's initial seating posture is the driver's natural seating posture under conditions of no external influencing factors. The vehicle's preset geometry design can be set using a model design that optimizes the field of vision, and is used to calculate the upper and lower field of vision reference angles in conjunction with the driver's initial seating posture, serving as the reference range threshold for adjusting the field of vision. For example, the driver's eye position is identified based on the driver's initial seating posture, and the upper edge of the field of vision (such as the upper edge of the windshield) is obtained based on the vehicle's preset geometry design. The angle between the line connecting the driver's eye position and the upper edge of the field of vision and the horizontal line is used as the upper field of vision reference angle. Simultaneously, the lower edge of the field of vision (such as the upper edge of the hood / dashboard) is obtained based on the vehicle's preset geometry design, and the angle between the line connecting the driver's eye position and the lower edge of the field of vision and the horizontal line is used as the lower field of vision reference angle.

[0038] The field of view scenario can be determined based on the vertical field of view conditions and the driving conditions. The field of view scenario includes the upper field of view scenario and the lower field of view scenario. The upper field of view scenario represents the driving conditions that require a high degree of vertical visibility, in order to identify the upper field of view threshold that triggers posture compensation, such as waiting at a red light at an urban intersection, driving on a highway, and driving off-road / uphill. The lower field of view scenario represents the driving conditions that require a high degree of vertical visibility, in order to identify the lower field of view threshold that triggers posture compensation, such as narrow road / parallel parking, following other vehicles in urban congestion, and highway cruising.

[0039] VR (Virtual Reality) devices are used to simulate the field of view and control angle convergence. For example, the upper field of view reference angle is kept smaller in the upper field of view scenario, and the lower field of view reference angle is kept smaller in the lower field of view scenario. During the angle convergence simulation, the driver's actual sitting posture in the corresponding field of view scenario is acquired. Based on the numerical differences between the initial sitting posture and the actual sitting posture, such as the driver's target joint coordinate position and the numerical differences between seat parameters, the sitting posture change data in the corresponding field of view scenario is calculated.

[0040] This embodiment converges the reference angles of the upper and lower fields of view based on multiple field of view scenarios to obtain the driver's actual sitting posture under different field of view scenarios, identify the corresponding sitting posture change data, improve the recognition accuracy of sitting posture compensation actions, and provide data guarantee for the design accuracy of the upper and lower fields of view.

[0041] In some embodiments of this application, obtaining the driver's actual sitting posture in the corresponding visual scene includes: determining at least one target visual task in the visual scene and triggering a task guidance instruction to guide the driver to perform at least one target visual task; collecting sampled values ​​of the driver's sitting posture parameters when performing the target visual task; and determining the driver's actual sitting posture in the visual scene based on the sampled values ​​of the sitting posture parameters.

[0042] Specifically, the target visual task can be set according to different visual scenarios. For example, in the upper field of view scenario, such as the red light waiting scenario at an urban intersection, the target visual task is to check the red light; in the highway driving scenario, the target visual task is to check the road sign; and in the lower field of view scenario, such as the urban traffic jam following scenario, the target visual task is to observe the lower edge of the rear bumper of the vehicle in front.

[0043] In different visual scenarios, based on the defined target visual task, the system guides the driver to perform natural driving or observation tasks. It collects sampled values ​​of the driver's posture parameters while performing the target visual task. For example, in a red light waiting scenario at an urban intersection, the upper field of view reference angle is reduced according to a preset angle convergence threshold. After the angle reduction, the target visual task is performed once, and the driver's posture parameter sampled values ​​are collected, representing the actual posture. Then, the angle reduction is performed again, and so on. The corresponding posture parameters are sampled for each angle convergence until the upper field of view reference angle converges to a preset minimum angle, at which point the test for the urban intersection red light waiting scenario is considered complete. The posture parameter sampled values ​​in this visual scenario are taken as the actual posture.

[0044] This embodiment guides the driver to perform natural driving or observation tasks under different vertical field of view conditions, collects the driver's actual sitting posture when performing the corresponding task, identifies the amount of change in the driver's sitting posture, characterizes the driver's posture compensation behavior to improve the field of view, and improves the scene testing effect.

[0045] In some embodiments of this application, the parameter types of the initial sitting posture and the actual sitting posture include eye point position, torso posture parameters, and seat adjustment parameters. Determining the sitting posture change data under the corresponding visual field scenario based on the initial sitting posture and the actual sitting posture includes: determining the amount of eye point position change, torso posture change, and seat adjustment change corresponding to the visual field scenario based on the initial sitting posture and the actual sitting posture under the visual field scenario; and determining the sitting posture change data under the visual field scenario based on the amount of eye point position change, torso posture change, and seat adjustment change.

[0046] Specifically, the eye point position is determined by capturing the corners of the driver's eyes and the center of the iris using multiple cameras, and then calculating the actual spatial position of the eyeball using an algorithm. For example, when the current field of vision meets the driver's driving vision requirements, the actual eye point position in the sitting posture is the same as or only slightly different from the initial eye point position in the sitting posture. When the current field of vision does not meet the driver's driving vision requirements, such as when the upper field of vision is too small, the actual eye point position in the sitting posture will be relatively lowered to compensate for the insufficient upward angle range; conversely, when the lower field of vision is too small, the actual eye point position in the sitting posture will be relatively higher to compensate for the insufficient downward angle range.

[0047] Trunk posture parameters can be obtained using optical motion capture systems or depth cameras. Reflective markers can be placed on key joints of the driver's body (such as shoulders, hips, knees, and ankles) or AI (Artificial Intelligence) skeletal recognition algorithms can be used to acquire the coordinates of these points in three-dimensional space in real time. These coordinates can be directly used as trunk posture parameters. Further calculations using vector dot product formulas can be performed on the acquired coordinates to determine core posture parameters such as trunk angles, thigh angles, and knee angles. For example, the trunk angle is calculated as the angle between the vector connecting the midpoint of the shoulder and the midpoint of the hip and the vertical upward vector. For instance, when the current field of vision meets the driver's driving vision requirements, the actual seated trunk posture parameters are the same as or have only a small deviation from the initial seated trunk posture parameters. When the current field of vision cannot meet the driver's driving vision requirements, such as when the upper field of vision is too small, the shoulder coordinate in the actual seated trunk posture parameters will be relatively lower to compensate for the insufficient upward angle range; conversely, when the lower field of vision is too small, the shoulder coordinate in the actual seated trunk posture parameters will be relatively higher to compensate for the insufficient downward angle range.

[0048] Seat adjustment parameters can be obtained by acquiring real-time values ​​from the seat's fore-aft position, height adjustment, seat cushion tilt, and backrest angle. For example, when the current field of vision meets the driver's driving visibility requirements, the actual seat adjustment parameters are the same as those for the initial driving visibility. When the current field of vision does not meet the driver's driving visibility requirements, such as when the upper field of vision is too small, the height adjustment parameters will be relatively lowered to compensate for the insufficient upward angle range. Similarly, when the lower field of vision is too small, the height adjustment parameters will be relatively higher to compensate for the insufficient downward angle range.

[0049] Based on the actual eye-point position, actual torso posture parameters, and actual seat adjustment parameters in the initial sitting posture and the actual sitting posture within the field of vision scenario, the changes in eye-point position, torso posture, and seat adjustment are calculated. These changes can be calculated using sitting posture parameter data sampled at adjacent time points, or by comparing the sitting posture parameter data obtained at each sampling time point with the initial sitting posture parameter data; there are no specific restrictions. The sitting posture change data within the field of vision scenario is determined based on the changes in eye-point position, torso posture, and seat adjustment. For example, weighted fusion of the changes in the three parameter types can be performed to obtain the corresponding sitting posture change data.

[0050] This embodiment recognizes sitting posture changes based on eye position, torso posture parameters, and seat adjustment parameters, thereby improving the accuracy of sitting posture change recognition.

[0051] In some embodiments of this application, determining the upper and lower field of view thresholds that trigger posture compensation based on field of view angles and posture change data corresponding to multiple field of view scenarios includes: determining a target mapping relationship based on field of view angles and posture change data corresponding to multiple field of view scenarios, wherein the target mapping relationship characterizes the relationship between upper and lower field of view angles and posture change data; determining the upper and lower field of view critical intervals that trigger posture compensation based on the target mapping relationship; and determining the upper and lower field of view thresholds based on the upper and lower field of view critical intervals.

[0052] Specifically, statistical analysis is performed on the vertical field of view angles and posture change data corresponding to the field of view scenario to establish a mapping relationship between the vertical field of view angles and the driver's posture compensation amount, i.e., posture change data. For example, a target mapping relationship can be constructed through curve fitting. Then, based on the target mapping relationship, the critical interval of the vertical field of view where the posture compensation amount increases significantly is identified, and this interval is determined as the driver's posture compensation trigger threshold. Alternatively, the angle value in this interval can be selected as the vertical field of view threshold based on different vehicle designs. Then, the vertical field of view threshold that triggers posture compensation is mapped inversely to the design constraints of vehicle geometric parameters to determine whether the current vehicle design scheme is within an acceptable field of view range.

[0053] This embodiment constructs a mapping relationship between visual field angle and sitting posture change data through data fitting, transforming discrete samples into sitting posture fluctuation models under various visual field conditions, thereby improving the recognition accuracy of the upper and lower visual field critical intervals and upper and lower visual field thresholds that trigger sitting posture compensation.

[0054] In some embodiments of this application, there are multiple drivers, and the field of vision angles corresponding to the field of vision scenarios include upper field of vision angles and lower field of vision angles. Determining the target mapping relationship based on the field of vision angles and posture change data corresponding to multiple field of vision scenarios includes: performing data fitting on the upper field of vision angles corresponding to multiple field of vision scenarios and the posture change data of multiple drivers in the corresponding field of vision scenarios to obtain a first fitting curve between the upper field of vision angles and posture change data; performing data fitting on the lower field of vision angles corresponding to multiple field of vision scenarios and the posture change data of multiple drivers in the corresponding field of vision scenarios to obtain a second fitting curve between the lower field of vision angles and posture change data; and determining the target mapping relationship based on the first fitting curve and the second fitting curve.

[0055] In other words, data fitting is performed on the upper and lower field-of-view angles corresponding to multiple field-of-view scenarios, along with data on the seating posture changes of multiple drivers in those scenarios. This yields a first fitting curve between the upper field-of-view angle and the seating posture change data, and a second fitting curve between the lower field-of-view angle and the seating posture change data. For example, the horizontal axis of the first and second fitting curves represents the field-of-view angle, and the vertical axis represents the seating posture change data. The first and second fitting curves serve as the target mapping relationship.

[0056] This embodiment obtains the mapping relationship between the upper field of view angle and the sitting posture compensation amount and the lower field of view angle and the sitting posture compensation amount respectively through curve fitting based on sampling data of different drivers and different field of view scenarios, so as to improve the accuracy of the mapping relationship and provide a data foundation for vehicle design.

[0057] Furthermore, multiple drivers can be selected based on the vehicle's target audience, making the set vertical field of view thresholds more suitable for the vehicle's design objectives.

[0058] In some embodiments of this application, the critical intervals of the upper and lower visual fields include the upper visual field critical interval and the lower visual field critical interval. Determining the upper and lower visual field critical intervals that trigger posture compensation based on the target mapping relationship includes: identifying the inflection point of the first curve based on the first fitting curve, and determining the upper visual field critical interval based on the inflection point of the first curve; identifying the inflection point of the second curve based on the second fitting curve, and determining the lower visual field critical interval based on the inflection point of the second curve.

[0059] Specifically, when the driver's sitting posture remains unchanged, the change in posture is close to zero; when the driver performs a posture compensation movement, the change in posture changes significantly, which is reflected as a clear inflection point in the fitted curve. Therefore, this embodiment determines the location of the posture compensation movement by identifying the inflection points of the first and second fitted curves, and uses this as the corresponding visual field critical interval. By utilizing the structural characteristics of the data itself to determine the design boundary, the efficiency and rationality of decision-making are improved.

[0060] As a specific embodiment of this application, such as Figure 2 As shown, the design method for this vehicle includes the following steps: S201, Obtain the driver's initial seating position.

[0061] In other words, the initial seating parameters of multiple drivers in a standard driving posture are obtained. The seating parameters include at least the driver's eye position, torso posture parameters, and seat adjustment parameters.

[0062] S202 calculates the driver's vertical field of vision reference angles based on the vehicle's preset geometry and initial seating posture.

[0063] S203 guides the driver to perform natural driving or observation tasks under different vertical field of vision conditions, collects the driver's actual sitting posture, and calculates sitting posture change data.

[0064] Among them, the data on changes in sitting posture characterizes the posture compensation behavior of drivers in order to improve their field of vision.

[0065] S204. Perform statistical analysis on the data of vertical field of vision angle and sitting posture change, and establish a mapping relationship between the vertical field of vision angle and sitting posture change data.

[0066] S205, based on the target mapping relationship, identify the critical interval of the upper and lower field of vision where the sitting posture compensation amount increases significantly, and determine this interval as the upper and lower field of vision threshold that triggers the driver's sitting posture compensation.

[0067] S206, the upper and lower field of view thresholds are mapped inversely to the design constraints of vehicle geometric parameters for vehicle design.

[0068] Therefore, this embodiment introduces driver posture compensation behavior into the design of the vehicle's vertical field of vision. It uses changes in the driver's active behavior as the evaluation basis. By identifying the critical point at which the driver's posture compensation behavior occurs when the field of vision is insufficient, it transforms the subjective discomfort that is originally difficult to quantify into an objective threshold that can be used for engineering design. At the same time, it proposes a field of vision acceptability judgment mechanism based on the compensation threshold, which improves the rationality of vehicle design and supports the early identification of field of vision design risks in the vehicle concept design stage, reducing rectification costs and design cycle.

[0069] This application also provides a vehicle design device 30, please refer to... Figure 3 The system includes: an acquisition module 310 for acquiring driver posture change data in multiple field-of-view scenarios; a determination module 320 for determining the upper and lower field-of-view thresholds for triggering posture compensation based on the field-of-view angles and posture change data corresponding to multiple field-of-view scenarios; and a design module 330 for determining the vehicle's geometric design constraints based on the upper and lower field-of-view thresholds in order to design the vehicle.

[0070] In some embodiments of this application, the acquisition module 310 acquires driver posture change data under multiple visual field scenarios, specifically for: acquiring the driver's initial posture; determining the driver's vertical visual field reference angle based on the vehicle's preset geometric design and the initial posture; performing angle convergence on the vertical visual field reference angle according to multiple visual field scenarios, and acquiring the driver's actual posture under the corresponding visual field scenario; and determining posture change data under the corresponding visual field scenario based on the initial posture and the actual posture.

[0071] In some embodiments of this application, the acquisition module 310 acquires the driver's actual sitting posture in the corresponding visual scene, specifically for: determining at least one target visual task in the visual scene and triggering a task guidance instruction to guide the driver to perform at least one target visual task; collecting the driver's sitting posture parameter sampling values ​​when performing the target visual task; and determining the driver's actual sitting posture in the visual scene based on the sitting posture parameter sampling values.

[0072] In some embodiments of this application, the parameter types of the initial sitting posture and the actual sitting posture include eye point position, torso posture parameters, and seat adjustment parameters. The acquisition module 310 determines the sitting posture change data in the corresponding visual field scene based on the initial sitting posture and the actual sitting posture. Specifically, it is used to: determine the amount of eye point position change, torso posture change, and seat adjustment change corresponding to the visual field scene based on the initial sitting posture and the actual sitting posture in the visual field scene; and determine the sitting posture change data in the visual field scene based on the amount of eye point position change, torso posture change, and seat adjustment change.

[0073] In some embodiments of this application, the determining module 320 determines the upper and lower field of view thresholds that trigger posture compensation based on the field of view angles and posture change data corresponding to multiple field of view scenarios. Specifically, it is used to: determine the target mapping relationship based on the field of view angles and posture change data corresponding to multiple field of view scenarios, wherein the target mapping relationship represents the relationship between the upper and lower field of view angles and posture change data; determine the upper and lower field of view critical intervals that trigger posture compensation based on the target mapping relationship; and determine the upper and lower field of view thresholds based on the upper and lower field of view critical intervals.

[0074] In some embodiments of this application, there are multiple drivers, and the field of vision angles corresponding to the field of vision scenarios include upper field of vision angles and lower field of vision angles. The determining module 320 determines the target mapping relationship based on the field of vision angles and posture change data corresponding to multiple field of vision scenarios. Specifically, it is used to: perform data fitting on the upper field of vision angles corresponding to multiple field of vision scenarios and the posture change data of multiple drivers in the corresponding field of vision scenarios to obtain a first fitting curve between the upper field of vision angles and posture change data; perform data fitting on the lower field of vision angles corresponding to multiple field of vision scenarios and the posture change data of multiple drivers in the corresponding field of vision scenarios to obtain a second fitting curve between the lower field of vision angles and posture change data; and determine the target mapping relationship based on the first fitting curve and the second fitting curve.

[0075] In some embodiments of this application, the critical intervals of the upper and lower visual fields include the critical intervals of the upper visual field and the critical intervals of the lower visual field. The determining module 320 determines the critical intervals of the upper and lower visual fields that trigger posture compensation based on the target mapping relationship. Specifically, it is used to: identify the inflection point of the first curve based on the first fitting curve, and determine the critical interval of the upper visual field based on the inflection point of the first curve; identify the inflection point of the second curve based on the second fitting curve, and determine the critical interval of the lower visual field based on the inflection point of the second curve.

[0076] This application also provides an electronic device 40, please refer to... Figure 4 It includes a memory 410 and a processor 420, wherein the memory 410 is used to store computer programs; and the processor 420 is used to execute the programs stored in the memory 410 to implement the vehicle design method described in any embodiment of this application.

[0077] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the vehicle design method described in any embodiment of this application.

[0078] In this application, "multiple" refers to two or more.

[0079] In this application, unless otherwise expressly defined, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0080] The terms “first,” “second,” “third,” “fourth,” etc., in this application (if present) are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0081] In this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, in this application, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0082] Unless otherwise specified, all steps in this application may be performed sequentially or randomly. For example, if the method includes steps A and B, it means that the method may include steps A and B performed sequentially, or it may include steps B and A performed sequentially. For example, if the method may also include step C, it means that step C may be added to the method in any order. For example, the method may include steps A, B, and C, or it may include steps A, C, and B, or it may include steps C, A, and B, etc.

[0083] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A vehicle design method, characterized in that, include: Acquire data on changes in driver's sitting posture across multiple visual scenarios; The upper and lower field of view thresholds that trigger posture compensation are determined based on the field of view angles corresponding to multiple field of view scenarios and the posture change data. The geometric design constraints of the vehicle are determined based on the upper and lower field of view thresholds in order to design the vehicle.

2. The method according to claim 1, characterized in that, Acquire driver posture change data in multiple field-of-view scenarios, including: Obtain the driver's initial seating position; The driver's vertical field of vision reference angle is determined based on the vehicle's preset geometry and the initial seating posture. The upper and lower field of view reference angles are converged based on multiple field of view scenarios, and the driver's actual sitting posture in the corresponding field of view scenario is obtained. Based on the initial sitting posture and the actual sitting posture, determine the sitting posture change data in the corresponding field of view scene.

3. The method according to claim 2, characterized in that, Obtaining the driver's actual sitting posture in the corresponding field of view includes: Identify at least one target visual task in the visual scene and trigger a task guidance instruction to guide the driver to perform at least one of the target visual tasks; Collect sampled values ​​of the driver's posture parameters when performing the target visual task; The driver's actual sitting posture in the field of vision is determined based on the sampled values ​​of the sitting posture parameters.

4. The method according to claim 2, characterized in that, The parameter types of the initial sitting posture and the actual sitting posture include eye point position, torso posture parameters, and seat adjustment parameters. Based on the initial sitting posture and the actual sitting posture, the corresponding sitting posture change data under the visual field scene is determined, including: Based on the initial sitting posture and the actual sitting posture in the field of vision scenario, determine the changes in eye point position, torso posture, and seat adjustment corresponding to the field of vision scenario. The sitting posture change data under the field of vision scenario is determined based on the change in eye position, the change in torso posture, and the change in seat adjustment.

5. The method according to claim 1, characterized in that, Based on the field of view angles corresponding to multiple field of view scenarios and the posture change data, the upper and lower field of view thresholds for triggering posture compensation are determined, including: A target mapping relationship is determined based on the field of view angles corresponding to multiple field of view scenarios and the sitting posture change data. The target mapping relationship represents the relationship between the vertical field of view angles and the sitting posture change data. The critical range of the upper and lower field of vision that triggers posture compensation is determined based on the target mapping relationship. The upper and lower field of vision thresholds are determined based on the aforementioned critical intervals of upper and lower field of vision.

6. The method according to claim 5, characterized in that, The number of drivers is multiple, and the field of view angles corresponding to the field of view scenes include upper field of view angles and lower field of view angles. A target mapping relationship is determined based on the field of view angles corresponding to the multiple field of view scenes and the posture change data, including: Data fitting is performed on the upper field of view angles corresponding to multiple field of view scenarios and the sitting posture change data of multiple drivers in the corresponding field of view scenarios to obtain a first fitting curve between the upper field of view angles and the sitting posture change data. Data fitting is performed on the lower field of view angles corresponding to multiple field of view scenarios and the sitting posture change data of multiple drivers in the corresponding field of view scenarios to obtain a second fitting curve between the lower field of view angles and the sitting posture change data. The target mapping relationship is determined based on the first fitted curve and the second fitted curve.

7. The method according to claim 6, characterized in that, The critical intervals for the upper and lower visual fields of view include an upper visual field critical interval and a lower visual field critical interval. The upper and lower visual field critical intervals that trigger posture compensation are determined based on the target mapping relationship, including: Identify the inflection point of the first curve based on the first fitted curve, and determine the critical range of the upper field of view based on the inflection point of the first curve; Identify the inflection point of the second curve based on the second fitted curve, and determine the critical range of the lower field of view based on the inflection point of the second curve.

8. A design device for a vehicle, characterized in that, include: The acquisition module is used to acquire data on the driver's posture changes in multiple visual scenarios. The determination module is used to determine the upper and lower field of view thresholds that trigger posture compensation based on the field of view angles corresponding to multiple field of view scenarios and the posture change data. The design module is used to determine the geometric design constraints of the vehicle based on the upper and lower field of view thresholds, so as to design the vehicle.

9. An electronic device, characterized in that, Including processor and memory, among which Memory, used to store computer programs; A processor for executing a program stored in memory to implement the method described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method described in any one of claims 1-7.

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