A quantitative analysis method for vehicle field of view visualization based on virtual reality

Abstract

The application relates to the technical field of automobile visual field development, and discloses a quantitative analysis method for vehicle visual field visualization based on virtual reality, which comprises the following steps: constructing a human eye visual field simulation system in a virtual environment, each ray interacts with the ground, and the human eye visual field simulation system records the emission point of each ray and the position coordinate of the interaction point interacting with the ground; importing a vehicle model into the virtual environment and removing transparent spare parts which have no influence on the visual field; constructing a collision body of the removed vehicle model to prevent the penetration of each ray; aligning the target vehicle model with the entity vehicle corresponding to the real environment in the virtual environment, and synchronizing the human body virtual model with the user corresponding to the real environment; determining the vehicle visual field blind area according to the three-dimensional area of the vehicle visual field; and changing the coordinate position of the human eye visual field simulation system into the front and back activity range of the user visual field. The application ensures the visual field analysis efficiency when complex measurement is carried out on multiple terrains and multiple road sections.

Description

Technical Field

[0001] This invention relates to the field of automotive vision development technology, and more specifically, to a quantitative analysis method for vehicle vision visualization based on virtual reality. Background Technology

[0002] With the rapid increase in the number of vehicles, accurate analysis of vehicle blind spots is a crucial step in ensuring driving safety, directly impacting the driving experience and the prevention of potential risks. Traditionally, quantitative and qualitative analysis of vehicle blind spots relies on real-world testing with actual vehicles. This involves setting up reference points such as dummies and traffic cones around the vehicle, and defining the vehicle's field of vision through manual observation and data calculation. However, this method is lengthy and cumbersome, requiring multiple people to collaborate to ensure smooth testing. Furthermore, it demands significant manpower and resources when dealing with complex measurement needs across various terrains and road sections, thus limiting analytical efficiency.

[0003] Therefore, it is necessary to design a quantitative analysis method for vehicle vision visualization based on virtual reality to solve the problems existing in the current technology. Summary of the Invention

[0004] In view of this, the present invention proposes a quantitative analysis method for vehicle field of view visualization based on virtual reality, which aims to solve the problem that the process of defining the field of view area of ​​the vehicle model is lengthy and cumbersome, requiring the cooperation of multiple people, and requiring a large amount of manpower and material resources when facing complex measurement needs of multiple terrains and road sections, thus restricting the efficiency of analysis.

[0005] This invention proposes a quantitative analysis method for vehicle field of view visualization based on virtual reality, comprising:

[0006] A human eye field simulation system is constructed in a virtual environment. The human eye field simulation system includes a human eye field simulator and a ray matrix composed of several rays. Each ray interacts with the ground, and the human eye field simulation system records the emission point of each ray and the position coordinates of the interaction point with the ground.

[0007] Import a vehicle model into the virtual environment and remove transparent components that do not affect the field of view. Construct a collision body for the removed vehicle model to prevent the penetration of each ray. Construct mirror reflection functions for the left rearview mirror, right rearview mirror, and interior rearview mirror to achieve mirror reflection of each ray and determine the target vehicle model.

[0008] In the virtual environment, the target vehicle model is aligned with the corresponding real-world physical vehicle, and the human virtual model is synchronized with the corresponding real-world user. The human eye vision simulation system is bound together, with the ray direction of each ray being the same as the line of sight of the human virtual model, and the binding position being the position of the line connecting the two eyes of the human virtual model.

[0009] The user wears a VR headset to simulate driving in the virtual environment and determines the three-dimensional area of ​​the vehicle's field of vision. Based on the three-dimensional area of ​​the vehicle's field of vision, the blind spot of the vehicle's field of vision is determined. The coordinate position change of the human eye field of vision simulation system is the range of movement of the user's field of vision.

[0010] Furthermore, when constructing a human visual field simulation system in a virtual environment, the human visual field simulation system includes a human visual field simulator and a ray matrix composed of several rays, it includes:

[0011] The ray matrix includes the effective field of view and the maximum eye rotation area;

[0012] The human eye vision simulator, from left to right on the horizontal plane, consists of the boundary planes of both eyes, the visual interface of the right eye, the optimal eye rotation area, and the binocular visual area. Furthermore, the human eye vision simulator has rays that are evenly distributed inside the cone, starting from the cone apex.

[0013] The human eye vision simulator includes the visual field boundary ray and the maximum eye rotation limit in the vertical plane.

[0014] Furthermore, when importing a vehicle model into the virtual environment and removing transparent components that do not affect the field of view, constructing a collision body for the removed vehicle model to prevent the penetration of each ray, and constructing the specular reflection function of the left rearview mirror, right rearview mirror, and interior rearview mirror to achieve specular reflection of each ray, the process includes:

[0015] The transparent components include a windshield, a left front door glass, a right front door glass, a left rear door glass, a right rear door glass, and a rear windshield. The rays from the human eye vision simulator are reflected on the surfaces of the left rearview mirror, the right rearview mirror, and the interior rearview mirror. The human eye vision simulation system records the results of the interaction between the reflected rays and other objects.

[0016] Furthermore, when the rays from the human eye vision simulator are reflected from the surfaces of the left rearview mirror, right rearview mirror, and interior rearview mirror, the following additional steps are included:

[0017] The surfaces of the left rearview mirror, right rearview mirror, and interior rearview mirror are treated with reflective surfaces. When the rays from the human eye field simulator are reflected by the reflective surfaces, the intersection of the reflected rays and the ground is determined as the visible point. The angles of the left rearview mirror, right rearview mirror, and interior rearview mirror are adjusted to ensure that the scene behind the target vehicle model can be observed.

[0018] Furthermore, when aligning the target vehicle model with the corresponding real-world physical vehicle in the virtual environment, the process includes:

[0019] Alignment positions include the center of the steering wheel, the seat position, and the door position lights.

[0020] Furthermore, when synchronizing the virtual human body model with the corresponding real-world user environment and binding the human eye vision simulation system, the process includes:

[0021] A virtual human body model is constructed based on the VR headset, the focal position of the two eyes of the virtual human body model is determined, and the human eye vision simulator is bound to the head of the virtual human body model. The opening direction of the human eye vision simulator is the same as the orientation of the head of the virtual human body model.

[0022] The location where the user touches the physical vehicle is the same as the location where the virtual human model touches the target vehicle model.

[0023] Furthermore, when the user wears a VR headset to simulate driving in the virtual environment to determine the three-dimensional area of ​​the vehicle's field of vision, and determines the vehicle's blind spot based on the three-dimensional area of ​​the vehicle's field of vision, the process includes:

[0024] When the user sits in the driver's seat of the physical vehicle, the coordinate value recording switch is turned on, which activates the human eye field detection function and enters the virtual environment. The virtual environment ignores the collision point data between the human eye field simulator and the target vehicle model, and determines the point with the minimum value of the collision point data at each angle and the user's position. The boundary line enclosed by the point with the minimum value is the boundary line of the blind spot of the physical vehicle.

[0025] Furthermore, when the user wears a VR headset to simulate driving in the virtual environment to determine the three-dimensional area of ​​the vehicle's field of vision, and determines the vehicle's blind spot based on the three-dimensional area of ​​the vehicle's field of vision, the method further includes:

[0026] In the virtual environment, the three-dimensional area encompassed by the boundary line of the vehicle's blind spot, the vehicle body boundary line, and the ground constitutes the vehicle's blind spot.

[0027] Furthermore, the quantitative analysis method for vehicle field of view visualization based on virtual reality also includes:

[0028] The user adjusts the components of the target vehicle model in the virtual environment, and the human eye vision simulator predicts the changes in the vehicle's field of vision based on the adjustment results.

[0029] Furthermore, the quantitative analysis method for vehicle field of view visualization based on virtual reality also includes:

[0030] When the user starts the human eye vision simulator in the virtual environment, the user operates and controls the human eye vision simulator by moving it up, down, left and right in the virtual environment to simulate the human eye movements of the virtual human body model, and outputs the simulated vehicle vision structure of the physical vehicle according to the scanning angle range of the human eye vision simulator.

[0031] Compared with existing technologies, the advantages of this invention are as follows: It enables fully online operation through digital modeling, eliminating the need for physical sites and material preparation. A single user can complete simulation tests using VR equipment, reducing manpower and material consumption. Especially in multi-vehicle, multi-scenario testing, it shortens the cycle of quantitative analysis of vehicle vision visualization. Furthermore, the virtual environment accurately simulates line-of-sight propagation through ray matrices, and combined with collision objects and mirror reflections, realistically recreates the obstruction of vision by vehicle components and the reflection effects of rearview mirrors. It automatically records the coordinates of ray emission points and interaction points, thus achieving the quantification and precise definition of blind spot data. The virtual environment can flexibly construct various complex scenarios, combining a virtual human model with synchronized binding of user actions to accurately simulate changes in vision under different driving postures, covering more comprehensive driving conditions. It effectively captures blind spot details easily missed in traditional testing, providing comprehensive reference for vehicle vision optimization and ensuring efficient vision analysis during complex measurements across multiple terrains and road sections. Attached Figure Description

[0032] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0033] Figure 1 A flowchart illustrating a quantitative analysis method for vehicle field of view visualization based on virtual reality, provided as an embodiment of the present invention.

[0034] Figure 2 This is a schematic diagram of a human eye field simulator provided in an embodiment of the present invention.

[0035] Among them: 1. Field of view boundary contour; 2. Standard field of view; 3. Ray. Detailed Implementation

[0036] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0037] In some embodiments of this application, see Figure 1-2 As shown, a quantitative analysis method for vehicle field of view visualization based on virtual reality includes:

[0038] S100: Construct a human eye vision simulation system in a virtual environment. The human eye vision simulation system includes a human eye vision simulator and a ray matrix composed of several rays. Each ray interacts with the ground, and the human eye vision simulation system records the emission point of each ray and the position coordinates of the interaction point with the ground.

[0039] S200: Import the vehicle model into the virtual environment and remove transparent parts that do not affect the field of view. Construct a collision body for the removed vehicle model to prevent the penetration of each ray. Construct the mirror reflection function of the left rearview mirror, right rearview mirror and interior rearview mirror to achieve mirror reflection of each ray and determine the target vehicle model.

[0040] S300: Aligns the target vehicle model with the corresponding real-world vehicle in the virtual environment, and synchronizes the human virtual model with the corresponding real-world user. Binds the human eye vision simulation system, with the ray direction of each ray being the same as the line of sight of the human virtual model, and the binding position being the position of the line connecting the two eyes of the human virtual model.

[0041] S400: The user wears a VR headset to simulate driving in a virtual environment to determine the three-dimensional area of ​​the vehicle's field of vision. Based on the three-dimensional area of ​​the vehicle's field of vision, the blind spot of the vehicle's field of vision is determined. The coordinate position change of the human eye field of vision simulation system is the range of movement of the user's field of vision.

[0042] Specifically, a human eye vision simulation system is constructed using virtual reality technology. This system generates a ray matrix composed of several rays, simulating the trajectory of the human eye during observation. Each ray originates from a designated emission point (simulating the position of the human eye) and interacts with the virtual ground. The system accurately records the coordinates of the emission point and the interaction point, digitally marking the boundaries of the field of vision. The coverage area of ​​the rays quantifies the field of vision region. A vehicle model is imported into the virtual environment, maintaining consistency with the type and model of real-world vehicles. When processing the vehicle model, transparent components (such as glass) are removed because they do not obstruct the field of vision. Collisions are constructed for the remaining components, essentially preventing rays from penetrating like real-world obstacles, thus simulating the obstruction of the field of vision by the vehicle body, pillars, etc. Simultaneously, a mirror reflection function is added to the rearview mirror. By simulating the principle of light reflection through a physics engine, the rays change their propagation direction after being reflected by the mirror, accurately reproducing the field of vision observed through the rearview mirror during real driving. Ultimately, a target vehicle model that realistically reflects the interaction of the field of vision is formed. Aligning the target vehicle model with the physical vehicle is achieved through coordinate calibration to ensure consistency in size and position. Meanwhile, the virtual human model synchronizes with the user using motion capture technology, matching the virtual model's head rotation and body posture to the user's in real time. The human eye field simulation system is then bound to the line connecting the virtual model's eyes, with each ray's direction aligned with the virtual model's line of sight. This ensures the ray matrix's emission angle and coverage completely follow the user's line of sight, achieving "what you see is what you measure" synchronization. When the user wears the VR headset to simulate driving, head movements cause the ray matrix to adjust synchronously. The three-dimensional space covered by the rays represents the vehicle's three-dimensional field of vision; areas not covered by rays are defined as blind spots. The range of changes in the ray matrix's coordinates corresponds to the range of changes in the user's field of vision caused by head movements during driving, thus fully capturing dynamics. In traditional real-vehicle testing, visual field characteristics rely on human judgment, making it difficult to quantify the boundaries of the field of view (e.g., the size of the blind spot cannot be accurately described). Ray matrix, however, achieves digital quantification of the field of view through coordinate recording, solving the problem of easy qualitative analysis but difficult quantitative analysis. In real-vehicle testing, the obstruction of the field of view by vehicle body parts and the reflection effect of rearview mirrors are affected by ambient light and observation angle, making it difficult for humans to accurately reproduce. However, collision objects and mirror reflection functions are standardized through physics engine simulation, eliminating environmental interference and ensuring the consistency of field of view obstruction and reflection effects. Furthermore, the virtual human model is synchronously bound to the user, and motion capture technology fixes the user's perspective benchmark, avoiding errors caused by individual differences. In addition, traditional methods require repeated scheduling of real vehicles and setting up of the site when testing in multiple terrains and road sections, while the virtual environment can quickly switch terrain parameters in the virtual environment, ensuring the efficiency of visual analysis of vehicle vision.

[0043] Understandably, the coordinate recording of the ray matrix achieves millimeter-level quantization of the field of view boundary. Compared to the approximate range estimated manually, the data accuracy is improved by at least one order of magnitude, providing traceable digital evidence for blind spot analysis. In terms of reliability, the physical simulation of collision objects and mirror reflections eliminates environmental interference such as weather and lighting in real vehicle testing. The field of view data of the same vehicle model can be repeatedly verified in different virtual environments. In terms of efficiency, the virtual environment does not require the placement of physical reference objects, and the field of view analysis process for a single vehicle model shortens the redundancy of the traditional process. Moreover, it can be quickly adapted to various terrain scenarios such as mountainous areas, cities, and highways through parameter adjustment. Furthermore, the immersive experience of the VR headset allows users to intuitively perceive the location of blind spots (such as pedestrian areas obscured by the A-pillar, and side and rear areas not covered by the rearview mirror), providing accurate field of view parameter support for functions such as automatic parking and blind spot monitoring. This solves the problems of inefficiency, high consumption, and insufficient accuracy in traditional real vehicle testing, ensuring the efficiency of field of view analysis when performing complex measurements on multiple terrains and road segments.

[0044] In some embodiments of this application, when constructing a human visual field simulation system in a virtual environment, the human visual field simulation system includes a human visual field simulator and a ray matrix composed of several rays, including: the ray matrix includes an effective visual field area and a maximum eye rotation area; the human visual field simulator, in the horizontal plane, consists of, from left to right, the boundary surfaces of both eyes, the visual field interface of the right eye, the optimal eye rotation area, and the binocular visual area; and the human visual field simulator has rays evenly distributed inside the cone body starting from the cone apex; and the human visual field simulator includes visual field boundary rays and the maximum eye rotation limit in the vertical plane.

[0045] Specifically, the visual field boundary contour 1 of the human eye visual field simulator refers to the visual field boundary of both eyes, that is, the maximum visual field range that both eyes can see. The standard visual field 2 refers to the spatial range that can be clearly seen when both eyes are focused on a point. The ray 3 of the human eye visual field simulator refers to the simulated light emitted from the position perceived by the eye and illuminating the object, simulating the light received by the eye. The effective visual field area of ​​the ray matrix corresponds to the functional visual field that the human eye can clearly identify. The maximum eye rotation area simulates the limit range of eyeball rotation within the eye socket (approximately 160° horizontally and 130° vertically). In the horizontal plane partitioning, the binocular boundary plane defines the outermost boundary of the left and right eye visual fields. The right eye visual field interface separately divides the right monocular visual range. The optimal eye rotation area corresponds to the comfortable range of natural eyeball rotation (approximately 60° horizontally). The binocular visual area is the overlapping area of ​​the left and right eye visual fields (approximately 120°). The uniform coverage of the visual field density in this area is achieved by the evenly distributed rays in the cone. The visual field boundary rays in the vertical plane simulate the upper and lower visual field limits of the human eye (approximately 60° upward to approximately 70° downward). The maximum eye rotation limit corresponds to the range of visual activity when looking up or down, thus forming a complete three-dimensional visual field constraint. Traditional methods simplify the human eye's field of vision to a fixed angle (such as "horizontal 120°"), ignoring physiological details such as the difference between monocular and binocular visual fields, and the comfort and limits of eye movement, leading to distorted blind spot analysis results. In contrast, the partitioned design can accurately distinguish between the binocular overlapping stereoscopic vision zone (crucial for judging visual distance) and the monocular visual field zone. The uniform distribution of cone rays solves the problem of sampling density differences between the edge and center of the visual field. By reproducing the characteristics of eye movement, it ensures the consistency between the visual field simulation and actual vision.

[0046] In some embodiments of this application, when importing a vehicle model into a virtual environment and removing transparent components that do not affect the field of vision, constructing a collision body for the removed vehicle model to prevent the penetration of each ray, and constructing the mirror reflection function of the left rearview mirror, right rearview mirror, and interior rearview mirror to achieve mirror reflection of each ray, the following are included: the transparent components include the windshield, left front door glass, right front door glass, left rear door glass, right rear door glass, and rear windshield; the rays of the human eye vision simulator are reflected on the surfaces of the left rearview mirror, right rearview mirror, and interior rearview mirror; and the human eye vision simulation system records the results of the interaction between the reflected rays and other objects.

[0047] Specifically, in traditional real-vehicle testing, the light transmittance of transparent glass and the reflection effect of rearview mirrors are greatly affected by ambient light, making it difficult to accurately quantify their impact on vision. Transparent components such as the windshield and the left front door glass are eliminated because these components do not obstruct the line of sight (rays can pass through directly). Eliminating transparent components can avoid interference from invalid collision objects on the trajectory of rays, ensuring that rays are only blocked by real obstructing components. The remaining non-transparent components (such as the body frame and pillars) are simulated by collision objects to block the rays, which are blocked when they come into contact with the vehicle, accurately replicating the vehicle's obstruction effect on the field of vision. The mirror reflection function of the left, right, and interior rearview mirrors, along with the physical simulation of the collision object, eliminates the occlusion deviation caused by component deformation and installation errors in real vehicle testing. By simulating the law of light reflection (the angle of incidence equals the angle of reflection) through a physics engine, the ray changes its propagation direction after contacting the mirror surface, thereby simultaneously recording the path of the reflected ray and the coordinates of the interaction point with other objects. This completely restores the field of view observed through the rearview mirror, avoiding the risk of blurred blind spot boundaries in human observation, thus improving the accuracy of the simulation. The combination of the collision object and the reflection function effectively controls the field of view occlusion error, thereby efficiently restoring the field of view of the rearview mirror and ensuring the efficiency and reliability of quantitative analysis of vehicle field of view visualization.

[0048] In some embodiments of this application, when the rays from the human eye vision simulator are reflected on the surfaces of the left rearview mirror, the right rearview mirror, and the interior rearview mirror, the method further includes: processing the surfaces of the left rearview mirror, the right rearview mirror, and the interior rearview mirror as reflective surfaces, and when the rays from the human eye vision simulator are reflected by the reflective surfaces, the intersection of the reflected rays and the ground is determined as the visible point, and the angles of the left rearview mirror, the right rearview mirror, and the interior rearview mirror are adjusted to ensure that the rear scene of the target vehicle model is observed.

[0049] Specifically, the surfaces of the left rearview mirror, right rearview mirror, and interior rearview mirror are treated with reflective surfaces. Through digital modeling, uniform reflective properties are given to the mirror surfaces to ensure that rays follow a fixed reflection law when they come into contact with the mirror surfaces, thus avoiding reflection deviations caused by surface roughness or uneven optical properties. After the rays from the human eye vision simulator are reflected by the reflective surface, the intersection point with the ground is defined as the visible point. This point directly marks the ground position that can be observed through the rearview mirror, forming the boundary marker of the reflected field of view. In traditional real vehicle testing, physical wear and differences in reflective coatings on the rearview mirror surface can lead to unstable reflection effects. Manually adjusting the angle relies on experience and makes it difficult to accurately ensure complete coverage of the rear scene. When adjusting the rearview mirror angle, the mirror tilt is changed by adjusting parameters in the virtual environment, which changes the propagation direction of the reflected rays. Ultimately, the visible points covered by the reflected rays are continuously distributed, thus completely covering the scene range behind the target vehicle model. Standardized reflective surface processing makes the virtual reflection effect highly consistent with the optical characteristics of real rearview mirrors, avoiding field of view deviations caused by differences in mirror surfaces. The clear marking of the visible points allows the effective observation range of the rearview mirror to be accurately defined, making it easy to identify uncovered blind spots. By parametrically adjusting the angle of the rearview mirror, the rear scene is fully covered, ensuring the efficiency and reliability of quantitative analysis of vehicle vision visualization.

[0050] In some embodiments of this application, when aligning a target vehicle model with a corresponding real-world physical vehicle in a virtual environment, the alignment positions include the steering wheel center, seat position, and door position light.

[0051] In some embodiments of this application, when the human virtual model is synchronized with the user in the corresponding real environment and the human eye vision simulation system is bound together, the process includes: constructing a human virtual model based on the VR headset, determining the focal position of the human virtual model's eyes, binding the human eye vision simulator to the head of the human virtual model, the opening direction of the human eye vision simulator being the same as the orientation of the human virtual model's head, and the position where the user touches the physical vehicle being the same as the position where the human virtual model touches the target vehicle model.

[0052] Specifically, in traditional testing, positional discrepancies between virtual technology and the physical vehicle can lead to a loss of reference points for visual field analysis. However, when the target vehicle model is aligned with the physical vehicle, reference points such as the center of the steering wheel, seat position, and door position are used. Coordinate calibration ensures that the position and angle of the target vehicle model in three-dimensional space completely correspond to the physical vehicle, guaranteeing consistency in structural dimensions and spatial layout. This avoids a disconnect between virtual and reality. The alignment of key positions establishes a precise spatial mapping, ensuring the synchronization between virtual testing and the real-world vehicle scenario. The human virtual model is constructed based on data from a VR headset. The headset tracks and determines the focal position of the human virtual model's eyes. A human eye vision simulator is then attached to the head of the human virtual model, causing the opening direction of the simulator to change synchronously with the head's orientation. This achieves linkage between the gaze direction and head rotation. If the human virtual model is not synchronized with the user, the simulated visual field will deviate from the real driving perspective. The VR headset-based binding can capture head movements and gaze focus in real time, ensuring that the simulated visual field is consistent with the user's actual visual field. Simultaneously, the user's touch on the physical vehicle is captured by sensors and synchronously mapped to the touch position of the human virtual model on the target vehicle model, ensuring spatial consistency of the interactive behavior.

[0053] Understandably, by aligning reference points and synchronizing actions, the mapping error between the virtual environment and the real scene is gradually reduced, ensuring that the vision analysis results can directly correspond to the actual vehicle situation. Head binding and eye-tracking linkage enable the simulated vision to change naturally with the user's actions, thereby reproducing the dynamic observation state in real driving. Touch position synchronization allows users to obtain feedback consistent with the real vehicle in virtual operation, reducing operation errors caused by environmental differences, and providing a scenario basis close to actual driving for quantitative analysis of vehicle vision visualization.

[0054] In some embodiments of this application, when a user wears a VR headset to simulate driving in a virtual environment to determine the three-dimensional area of ​​the vehicle's field of vision and to determine the vehicle's blind spot based on the three-dimensional area of ​​the vehicle's field of vision, the process includes: when the user is sitting in the driver's seat of the physical vehicle, turning on the coordinate value recording switch, then activating the human eye field of vision detection function and entering the virtual environment. The virtual environment ignores the collision point data between the human eye field of vision simulator and the target vehicle model, and determines the point with the minimum value of the collision point data at each angle and the user's position. The boundary line enclosed by the point with the minimum value is the boundary line of the vehicle's blind spot.

[0055] Specifically, when the user is in the driver's seat of the physical vehicle, after turning on the coordinate value recording switch, the human eye field detection function is activated and synchronously connected to the virtual environment. At this time, the virtual environment automatically filters the collision point data between the human eye field simulator and the target vehicle model to avoid interference from the vehicle's own structure (such as the body and pillars) on the judgment of the field of vision boundary. In traditional field of vision detection, the occlusion of the vehicle's own components is easily misjudged as an external blind spot, resulting in distorted analysis results. Ignoring the collision points between the human eye field simulator and the target vehicle model can eliminate the interference of the vehicle structure on the field of vision boundary, and only retain the occlusion data formed by the external environment (such as other vehicles and obstacles). At each observation angle (up, down, left, right, front, rear, etc.), the point with the smallest distance from the user's position is selected from the remaining collision points. These points represent the occlusion boundary closest to the user within the field of vision, which can truly reflect the critical position where the line of sight is blocked by external objects, ensuring that the boundary line accurately corresponds to the actual blind spot range. The boundary line formed by connecting these minimum points is the vehicle blind spot boundary of the physical vehicle in this virtual environment.

[0056] Understandably, by removing the occlusion data of the target vehicle model, the blind spot boundary is determined only by external environmental factors, thus closely reflecting the limited field of vision in actual driving. Based on the actual position of the user's driving seat, the blind spot changes under different sitting postures and different scenarios can be dynamically captured, avoiding deviations caused by fixed parameters. The clear definition of the boundary line makes the blind spot range visible, ensuring the efficiency and reliability of quantitative analysis of vehicle vision visualization.

[0057] In some embodiments of this application, when a user wears a VR headset to simulate driving in a virtual environment to determine the three-dimensional area of ​​the vehicle's field of vision, and determines the vehicle's blind spot based on the three-dimensional area of ​​the vehicle's field of vision, the method further includes: in the virtual environment, the three-dimensional area surrounded by the ray involved in the boundary line of the vehicle's blind spot, the boundary line of the vehicle body, and the ground is the vehicle's blind spot.

[0058] Specifically, in the virtual environment, the boundary of the vehicle's blind spot is formed by the rays emitted from the human eye vision simulator, the vehicle's body boundary line, and the ground. The rays, as the digital carrier of the line of sight, have their portion obscured by vehicle body components, forming the upper boundary of the blind spot. The vehicle body boundary line (such as the outlines of the A-pillar and B-pillar) serves as the physical boundary of this obstruction, forming the lateral boundary of the blind spot. The ground, as the bottom reference point, constitutes the lower boundary of the blind spot. This enclosed three-dimensional area, formed by the overlapping of these three elements, completely covers the spatial range inaccessible by sight, which is the vehicle's blind spot. By enclosing the blind spot with rays, body boundary lines, and the ground, the abstract "obstructed area" is transformed into a concrete three-dimensional spatial form, solving the problem of vague blind spot boundaries and difficulty in precise positioning. Furthermore, the coordinated enclosure of these three elements in the virtual environment realistically reproduces the impact of different vehicle body structures (such as pillars with different angles) on the blind spot shape, avoiding interference from environmental factors in boundary judgment during real-world vehicle testing. The three-dimensional enclosed structure allows for precise description of the spatial extent of blind spots. At the same time, the presentation of the three-dimensional area enables intuitive observation of the size, shape, and location of blind spots (such as the triangular blind spot to the side and rear), avoiding the omission of vertical blind spots in traditional two-dimensional planar analysis and ensuring the efficiency and reliability of quantitative analysis of vehicle vision visualization.

[0059] In some embodiments of this application, the quantitative analysis method for vehicle field of view visualization based on virtual reality further includes: the user adjusting the components of the target vehicle model in the virtual environment, and the human eye field simulator predicting the changes in the vehicle field of view of the target vehicle model based on the adjustment results.

[0060] In some embodiments of this application, the quantitative analysis method for vehicle field of vision visualization based on virtual reality further includes: when a user starts a human eye field of vision simulator in a virtual environment, the user operates and controls the human eye field of vision simulator by using a mouse, moves the human eye field of vision simulator up, down, left and right in the virtual environment to simulate the human eye movements of a virtual human body model, and outputs a simulated structure of the vehicle field of vision of the physical vehicle based on the scanning angle range of the human eye field of vision simulator.

[0061] Specifically, in traditional methods, vehicle components are fixed and cannot verify their impact on the field of vision. However, virtual adjustment and real-time prediction can achieve dynamic analysis where modifications show immediate effects. In real-vehicle testing, subtle changes in human eye movements are difficult to capture in time. When a user adjusts the components of the target vehicle model, the virtual environment updates the virtual parameters (such as size and position) of the component in real time. The human eye field simulator automatically recalculates the ray coverage area through collision detection between rays and the modified components, thereby predicting the expansion, contraction, or morphological changes of the field of vision area. When users operate the human eye vision simulator with a mouse, the mouse's up, down, left, and right movements are converted into coordinate displacements of the simulator in virtual space. This simulates the head rotation and eye movement of the virtual human model, compensating for the subjective bias of manual observation and ensuring that the vision simulation can cover dynamic scenes. Simultaneously, the human eye vision simulator emits rays based on the scanning angle range, interacting with the target vehicle model and the ground to generate structured outputs of the vision simulation (such as boundary lines and coverage areas). The simulator can mark the tested vision areas with different colors, facilitating user analysis and verification of vision areas and blind spots. Furthermore, the simulator can simulate the vehicle's vision range in a virtual environment under different vehicle positions, different climbing angles, different road curvatures, and different road bends, using either manual or one-click automatic modes. It outputs structured vision simulation results, facilitating data comparison across different test scenarios, providing a unified analytical benchmark for vehicle vision analysis, enhancing the reusability of results, and ensuring the efficiency and reliability of quantitative analysis of vehicle vision visualization.

[0062] In summary, the beneficial effects of this invention are as follows: It enables fully online operation through digital modeling, eliminating the need for physical sites and material preparation. A single user can complete simulation tests using VR equipment, reducing manpower and material consumption. Especially in multi-vehicle, multi-scenario testing, it significantly shortens the cycle of quantitative analysis of vehicle vision visualization. Furthermore, the virtual environment accurately simulates line-of-sight propagation through ray matrices, and combined with collision objects and mirror reflections, realistically recreates the obstruction of vision by vehicle components and the reflection effects of rearview mirrors. It automatically records the coordinates of ray emission points and interaction points, thereby achieving the quantification and precise definition of blind spot data. The virtual environment can flexibly construct various complex scenarios, combining a virtual human model with synchronized binding of user actions to accurately simulate changes in vision under different driving postures, covering more comprehensive driving conditions. It effectively captures blind spot details easily missed in traditional testing, providing comprehensive reference for vehicle vision optimization and ensuring efficient vision analysis during complex measurements across multiple terrains and road sections.

[0063] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program goods. 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 goods 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.

[0064] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods 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.

[0065] 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.

[0066] 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.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A quantitative analysis method for vehicle field of view visualization based on virtual reality, characterized in that, include: A human eye field simulation system is constructed in a virtual environment. The human eye field simulation system includes a human eye field simulator and a ray matrix composed of several rays. Each ray interacts with the ground, and the human eye field simulation system records the emission point of each ray and the position coordinates of the interaction point with the ground. Import a vehicle model into the virtual environment and remove transparent components that do not affect the field of view. Construct a collision body for the removed vehicle model to prevent the penetration of each ray. Construct mirror reflection functions for the left rearview mirror, right rearview mirror, and interior rearview mirror to achieve mirror reflection of each ray and determine the target vehicle model. In the virtual environment, the target vehicle model is aligned with the corresponding real-world physical vehicle, and the human virtual model is synchronized with the corresponding real-world user. The human eye vision simulation system is bound together, with the ray direction of each ray being the same as the line of sight of the human virtual model, and the binding position being the position of the line connecting the two eyes of the human virtual model. The user wears a VR headset to simulate driving in the virtual environment and determines the three-dimensional area of ​​the vehicle's field of vision. Based on the three-dimensional area of ​​the vehicle's field of vision, the blind spot of the vehicle's field of vision is determined. The coordinate position change of the human eye field of vision simulation system is the range of movement of the user's field of vision.

2. The quantitative analysis method for vehicle field of view visualization based on virtual reality according to claim 1, characterized in that, When constructing a human visual field simulation system in a virtual environment, the human visual field simulation system includes a human visual field simulator and a ray matrix composed of several rays, the following steps are taken: The ray matrix includes the effective field of view and the maximum eye rotation area; The human eye vision simulator, from left to right on the horizontal plane, consists of the boundary planes of both eyes, the visual interface of the right eye, the optimal eye rotation area, and the binocular visual area. Furthermore, the human eye vision simulator has rays that are evenly distributed inside the cone, starting from the cone apex. The human eye vision simulator includes the visual field boundary ray and the maximum eye rotation limit in the vertical plane.

3. The quantitative analysis method for vehicle field of view visualization based on virtual reality according to claim 2, characterized in that, Importing a vehicle model into a virtual environment and removing transparent components that do not affect the field of view, constructing a collision body for the removed vehicle model to prevent the penetration of each ray, and constructing mirror reflection functions for the left rearview mirror, right rearview mirror, and interior rearview mirror to achieve mirror reflection of each ray, includes: The transparent components include a windshield, a left front door glass, a right front door glass, a left rear door glass, a right rear door glass, and a rear windshield. The rays from the human eye vision simulator are reflected on the surfaces of the left rearview mirror, the right rearview mirror, and the interior rearview mirror. The human eye vision simulation system records the results of the interaction between the reflected rays and other objects.

4. The quantitative analysis method for vehicle field of view visualization based on virtual reality according to claim 3, characterized in that, When the rays from the human eye field simulator are reflected from the surfaces of the left rearview mirror, right rearview mirror, and interior rearview mirror, the following additional features are included: The surfaces of the left rearview mirror, right rearview mirror, and interior rearview mirror are treated with reflective surfaces. When the rays from the human eye field simulator are reflected by the reflective surfaces, the intersection of the reflected rays and the ground is determined as the visible point. The angles of the left rearview mirror, right rearview mirror, and interior rearview mirror are adjusted to ensure that the scene behind the target vehicle model can be observed.

5. The quantitative analysis method for vehicle field of view visualization based on virtual reality according to claim 4, characterized in that, Aligning the target vehicle model with a corresponding real-world physical vehicle in the virtual environment includes: Alignment positions include the center of the steering wheel, the seat position, and the door position lights.

6. The quantitative analysis method for vehicle field of view visualization based on virtual reality according to claim 5, characterized in that, When synchronizing the virtual human body model with the corresponding real-world user environment and binding the human eye vision simulation system, the following is included: A virtual human body model is constructed based on the VR headset, the focal position of the two eyes of the virtual human body model is determined, and the human eye vision simulator is bound to the head of the virtual human body model. The opening direction of the human eye vision simulator is the same as the orientation of the head of the virtual human body model. The location where the user touches the physical vehicle is the same as the location where the virtual human model touches the target vehicle model.

7. The quantitative analysis method for vehicle field of view visualization based on virtual reality according to claim 6, characterized in that, When the user wears a VR headset to simulate driving in the virtual environment and determines the three-dimensional field of vision of the vehicle, and determines the blind spot of the vehicle based on the three-dimensional field of vision of the vehicle, the process includes: When the user sits in the driver's seat of the physical vehicle, the coordinate value recording switch is turned on, which activates the human eye field detection function and enters the virtual environment. The virtual environment ignores the collision point data between the human eye field simulator and the target vehicle model, and determines the point with the minimum value of the collision point data at each angle and the user's position. The boundary line enclosed by the point with the minimum value is the boundary line of the blind spot of the physical vehicle.

8. The quantitative analysis method for vehicle field of view visualization based on virtual reality according to claim 7, characterized in that, When the user wears a VR headset to simulate driving in the virtual environment and determines the three-dimensional area of ​​the vehicle's field of vision, and determines the vehicle's blind spot based on the three-dimensional area of ​​the vehicle's field of vision, the method further includes: In the virtual environment, the three-dimensional area encompassed by the boundary line of the vehicle's blind spot, the vehicle body boundary line, and the ground constitutes the vehicle's blind spot.

9. The quantitative analysis method for vehicle field of view visualization based on virtual reality according to claim 8, characterized in that, Also includes: The user adjusts the components of the target vehicle model in the virtual environment, and the human eye vision simulator predicts the changes in the vehicle's field of vision based on the adjustment results.

10. The quantitative analysis method for vehicle field of view visualization based on virtual reality according to claim 9, characterized in that, Also includes: When the user starts the human eye vision simulator in the virtual environment, the user operates and controls the human eye vision simulator by moving it up, down, left and right in the virtual environment to simulate the human eye movements of the virtual human body model, and outputs the simulated vehicle vision structure of the physical vehicle according to the scanning angle range of the human eye vision simulator.

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