Automobile exhibition interactive experience method based on virtual reality

Abstract

The invention belongs to application of a virtual reality technology in the field of automobile exhibition, and particularly relates to an automobile exhibition interactive experience method based on virtual reality. Firstly, a virtual exhibition hall and a vehicle type three-dimensional model are constructed and optimized, so that a user checks the appearance and the internal structure of a vehicle in an immersive manner; recording browsing behaviors, and analyzing preferences through a recommendation algorithm to generate personalized vehicle model recommendation; after a user selects a vehicle, configuration selection and real-time rendering display can be carried out, test driving can be selected, and the system loads different driving scenes to simulate control experience; and finally, capturing a user attention area based on a line-of-sight tracking technology of a clustering algorithm to generate a visual heat map so as to identify preference key points, and providing data support for marketing and exhibition planning. According to the method, the reality sense of virtual display is improved through multiple technologies, interactivity is enhanced, test driving experience is enriched, user preferences are accurately captured, and the defects of an existing automobile exhibition display mode are effectively overcome.

Description

Technical Field

[0001] The invention belongs to the application of virtual reality technology in the field of automobile exhibitions, and specifically relates to an automobile exhibition interactive experience method based on virtual reality. Background Art

[0002] With the continuous development of virtual reality technology, auto shows are gradually introducing immersive interactive experiences. However, there are many problems with the current existing auto show display methods, which are difficult to meet the users' growing demand for in-depth experience. There is a lack of immersion, which makes it impossible for users to experience the charm of cars in person; the personalized recommendation function is missing, and it is impossible to accurately provide car model recommendations based on user preferences; the interactivity is limited, and the interaction between users and the display content is not rich and diverse enough. These problems limit the ability of auto shows to attract users, improve user experience, and provide effective data support for subsequent marketing and exhibition planning. The present invention aims to solve these problems by combining technologies such as three-dimensional modeling, personalized recommendation, and virtual test drive simulation to provide a more immersive car model display and configuration experience, while using eye tracking technology to realize user preference analysis, providing new ideas and methods for the development of auto shows. Summary of the invention

[0003] In view of the existing technical problems, the present invention proposes a method for automobile exhibition interactive experience based on virtual reality.

[0004] In order to achieve the above object, the technical solution adopted by the present invention comprises the following steps:

[0005] S1. First, use 3D modeling technology to build a virtual exhibition hall and a 3D model of the model on display in the exhibition hall, and optimize the model to allow users to immersively view the appearance and internal structure of the displayed model;

[0006] S2. Secondly, record the user's browsing behavior, analyze preferences by improving the recommendation algorithm, generate personalized car model recommendations in real time, and display them in the user's field of view for further review;

[0007] S3. After the user selects a car model, the system provides color, wheel, and seat configuration options and renders them in real time. The personalized configuration can be saved and used for sales communication;

[0008] S4. The user chooses to test drive. The system loads different driving scenarios, simulates the control experience, and provides feedback on acceleration and steering effects, allowing the user to experience the vehicle performance.

[0009] S5. Finally, we use the eye tracking technology based on clustering algorithm to capture the specific areas of users’ attention and generate visual heat maps to identify users’ preferences and provide data support for subsequent marketing and exhibition planning.

[0010] The specific implementation of using the 3D modeling technology to construct the 3D model of the virtual exhibition hall and the vehicle model displayed in the exhibition hall and optimizing the model in step S1 is as follows:

[0011] S11. First, the exhibition hall environment and the vehicle are 3D modeled using refined modeling technology. The model is divided into an appearance geometry model and an internal structure model. The overall model M is expressed as: M = M ext +M int , where M ext represents the vehicle appearance model, M int represents the vehicle interior model;

[0012] S12, then applying detail enhancement technology to the vehicle model M, increasing polygon details through a surface subdivision method, so that the surface smoothness is higher;

[0013] S13, then use the high-resolution map to texture map the surface of the model, the map is represented by T ext and T int , corresponding to the appearance and internal structure respectively, the texture of the model M after mapping is: M = M ext ·T ext +M int ·T int And use a physically based lighting model to enhance realism. The lighting equation is: L(o) = k d ·(N·L i )·I i +k s ·(R·V) n I i , where L(o) is the output brightness, k d , k s are diffuse reflection and specular reflection coefficients respectively, N is the normal vector, L i is the incident light, R is the reflection vector, V is the sight vector, I i is the incident light intensity, n is the specular coefficient;

[0014] S14. In addition, real-time rendering is achieved based on deferred shading technology. The geometric information, normal vectors and texture data are first rendered to the buffer, and then the lighting calculation is performed to optimize the rendering efficiency. The real-time rendering frame rate target is F ≥ 90FPS to meet the smoothness of the immersive experience;

[0015] S15. Finally, use spatial positioning technology to track the user's head position and viewing angle, and record the initial position as (x 0 ,y 0 ,z 0 ), the real-time position is (x, y, z), then the view position update formula is: Δx = xx 0 ,Δy=yy 0 ,Δz=zz0 The model presentation perspective is adjusted through view matrix transformation to achieve an immersive viewing effect for users in the virtual space.

[0016] Preferably, the specific implementation of analyzing preferences by improving the recommendation algorithm in step S2 to generate personalized vehicle model recommendations in real time is as follows:

[0017] S21. First, convert user preferences into feature vectors: V user =(S 1 ,S 2 ,...S m ), where S i Represents the user's preference score for the i-th type of vehicle. Vectorize the description of each vehicle model in the vehicle model data set to obtain: V model =(M 1 ,M 2 ,...M m ), where M i represents the characteristic description of the i-th model;

[0018] S22. Next, weighted cosine similarity calculation is introduced. The weighted cosine similarity formula is: where ω i The system assigns a weight to each feature, sorts the features based on similarity, recommends the top-scoring models to users, and dynamically adjusts the recommendations based on the user's real-time interactive behavior.

[0019] S23. Finally, record the user's feedback on the recommended content and fine-tune the preference feature vector so that the improved recommendation algorithm can quickly adapt to the user's latest interests. The preference update formula is: V user =αV user-old +(1-α)V feedback , where α is the adjustment coefficient, V user-old For the original preference setting, V feedback Feedback vector for users.

[0020] Preferably, in step S4, a highly immersive virtual reality experience system is constructed to simulate the control experience for the user's test drive, and the specific implementation steps are:

[0021] S41. First, an intuitive interface is provided, where users can select different driving scenarios. According to the scenario selected by the user, the system loads the corresponding 3D environment model in real time, uses scene rendering technology to make the environment have a realistic visual effect, and sets specific environmental characteristics for each scene, including weather, time, and road conditions, to enhance the sense of immersion. The characteristics are expressed as: E si ={w i ,t i ,r i}, where w is the weather, t is the time, and r is the road condition;

[0022] S42, secondly, supporting the user to use a steering wheel, pedals or handle device for control, and collecting the user's control input in real time, the input data is expressed as: I = {a, s, t}, where a is acceleration, s is the steering angle, and t is braking;

[0023] S43. Finally, while the user is controlling the vehicle, the acceleration and steering effects are fed back in real time, including steering wheel vibration, seat movement and visual effects, to enhance the sense of immersion.

[0024] Preferably, the eye tracking technology based on the clustering algorithm in step S5 captures the specific area of ​​user attention and generates a visual heat map to identify the user's preferred focus. The specific operation is:

[0025] S51, firstly, obtain the user's sight trajectory data in the virtual exhibition hall in real time through the VR device, including the viewing angle position, the duration of stay and the sight path. The sight data point set is G = {g 1 ,g 2 ,...g n}, where g i Contains the coordinates of the data point (x i ,y i ) and timestamp t i ;

[0026] S52, then by calculating the data point density at each position, obtain the high-frequency area where the user's sight stays, and the stay point is calculated as: Among them, δ(g i ,g j ) represents the sight point g i and g j The larger the value, the longer the stay time and the higher the attention;

[0027] S53, then use the density-based clustering algorithm to cluster the sight data points in real time, divide the area where the user's sight focuses into several high-density hot spots, and use the clustering results to identify the hot spots in the virtual exhibition hall. The c of each cluster center is i Attention intensity H i It includes the number of sight points and assigns a weight to each hotspot area, with the attention duration and sight point density of the hotspot area as the core indicators: where ω j Indicates the time weight of the sight point. The larger the regional intensity, the higher the user's attention.

[0028] S54. Finally, based on the clustering results of the sightline hotspot area, a visual heat map is generated through an interpolation algorithm. The intensity of the hotspot is represented by the color depth, and the user's preferred hotspot in the virtual exhibition hall is updated in real time. The sightline heat map formula is: Where (x i ,y i ) represents the location of the cluster center, and σ controls the range of diffusion.

[0029] Compared with the prior art, the advantages and positive effects of the present invention are that the realism of virtual display is improved through high-precision 3D modeling and physical lighting rendering technology; the intelligent recommendation algorithm analyzes user preferences in real time, provides personalized vehicle model recommendations, and enhances interactivity; it supports immersive test drive simulation, cooperates with diversified driving scenarios and real-time feedback, and makes the user experience richer; the eye tracking technology generates visual heat maps, accurately captures the user's attention areas, and provides effective data support for subsequent marketing and exhibition planning. DETAILED DESCRIPTION

[0030] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described below in conjunction with embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0031] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways than those described herein. Therefore, the present invention is not limited to the specific embodiments of the following disclosure.

[0032] Embodiment: With the development of virtual reality technology, auto shows are gradually introducing immersive interactive experiences. However, existing display methods have problems such as insufficient immersion, lack of personalized recommendations, and limited interactivity, which make it difficult to meet users' growing demand for in-depth experience. This patent proposes a method for interactive experience of auto shows based on virtual reality, combining technologies such as three-dimensional modeling, personalized recommendations, and virtual test drive simulation to provide an immersive vehicle model display and configuration experience, while realizing user preference analysis through line of sight tracking to provide data support for subsequent marketing.

[0033] In order to enhance the user's immersive experience, we first use 3D modeling technology to build a model of the virtual showroom and the displayed models. First, we use refined modeling technology to perform 3D modeling of the showroom environment and the vehicle. The model is divided into an appearance geometry model and an internal structure model. The overall model M is expressed as: M = M ext +M int , where M ext represents the vehicle appearance model, M intrepresents the internal model of the vehicle; then the detail enhancement technology is applied to the vehicle model M, and the polygon details are increased by the surface subdivision method to make the surface smoother; then the model surface is texture mapped using a high-resolution map, and the map is represented by T ext and T int , corresponding to the appearance and internal structure respectively, the texture of the model M after mapping is: M = M ext ·T ext +M int ·T int And use a physically based lighting model to enhance realism. The lighting equation is: L(o) = k d ·(N·L i )·I i +k s ·(R·V) n I i , where L(o) is the output brightness, k d , k s are diffuse reflection and specular reflection coefficients respectively, N is the normal vector, L i is the incident light, R is the reflection vector, V is the sight vector, I i is the incident light intensity, n is the highlight coefficient; in addition, real-time rendering is achieved based on deferred shading technology, and geometric information, normal vectors, textures and other data are first rendered to the buffer, and then lighting calculation is performed to optimize rendering efficiency. The real-time rendering frame rate target is F ≥ 90FPS to meet the smoothness of the immersive experience; finally, spatial positioning technology is used to track the user's head position and viewing angle, and the initial position is recorded as (x 0 ,y 0 ,z 0 ), the real-time position is (x, y, z), then the view position update formula is: Δx = xx 0 ,Δy=yy 0 ,Δz=zz 0 The model rendering perspective is adjusted through view matrix transformation to achieve immersive viewing effects in virtual space. High-precision modeling and physical lighting are used to enhance the realism of vehicles. Deferred shading ensures high frame rates for real-time rendering and enhances the user's immersive experience.

[0034] Next, considering recording the user's browsing behavior to better generate personalized vehicle model recommendation services for users, the present invention adopts an improved recommendation algorithm to first convert user preferences into feature vectors: V user =(S 1 ,S 2 ,...S m ), where S i Represents the user's preference score for the i-th type of vehicle. Vectorize the description of each vehicle model in the vehicle model data set to obtain: V model =(M1 ,M 2 ,...M m ), where M i represents the characteristic description of the i-th model; secondly, the weighted cosine similarity calculation is introduced, and the weighted cosine similarity formula is: where ω i The weight of each feature is given, and the models with the highest scores are recommended to users based on similarity sorting. The recommended content is dynamically adjusted according to the user's real-time interactive behavior. Finally, the user's feedback on the recommended content is recorded, and the preference feature vector is fine-tuned so that the improved recommendation algorithm can quickly adapt to the user's latest interests. The preference update formula is: V user =αV user-old +(1-α)V feedback , where α is the adjustment coefficient, V user-old For the original preference setting, V feedback Provides user feedback vectors. It analyzes user preferences, recommends car models in real time, dynamically adjusts content, optimizes personalized experience, and improves user engagement and display interactivity.

[0035] After selecting a car model, the user can configure it. The system provides color, wheel, and seat configuration options and renders them in real time. Then the user chooses to test drive. The system loads different driving scenarios, simulates the control experience, and provides feedback on acceleration and steering effects, allowing the user to feel the vehicle performance. First, an intuitive interface is provided, and the user can choose different driving scenarios. According to the scenario selected by the user, the system loads the corresponding 3D environment model in real time, and uses scene rendering technology to make the environment have a real visual effect. Specific environmental characteristics are set for each scene, including weather, time, and road conditions to enhance the sense of immersion. The characteristics are expressed as: E si ={w i ,t i ,r i}, where w is the weather, t is the time, and r is the road condition; secondly, it supports users to use steering wheels, pedals or handle devices for control, and collects user control inputs in real time. The input data is expressed as: I = {a, s, t}, where a is acceleration, s is the steering angle, and t is braking; finally, while the user is controlling, the acceleration and steering effects are fed back in real time, including steering wheel vibration, seat movement and visual effects, to enhance the sense of immersion. This operation supports a variety of driving scenarios and real-time feedback, simulates real driving experience through device interaction, demonstrates vehicle performance, and enhances the fun of test driving.

[0036] Finally, in order to optimize the recognition of user preferences, the present invention tracks and analyzes the sight trajectory based on the clustering algorithm, captures the specific area of ​​user attention and generates a visual heat map to identify the focus of user preferences. First, the user's sight trajectory data in the virtual exhibition hall is obtained in real time through the VR device, including the viewing angle position, the duration of stay and the sight path. The sight data point set is G = {g 1 ,g 2 ,...g n}, where g i Contains the coordinates of the data point (x i ,y i ) and timestamp t i ; Then, by calculating the data point density at each location, we can obtain the high-frequency area where the user’s gaze stays. The stay point is calculated as: Among them, δ(g i ,g j ) represents the sight point g i and g j The larger the value, the longer the stay time and the higher the attention. Then, the density-based clustering algorithm is used to cluster the sight data points in real time, and the area where the user's sight focuses is divided into several high-density hot spots. The clustering results are used to identify the hot spots in the virtual exhibition hall. The c of each cluster center is i Attention intensity H i It includes the number of sight points and assigns a weight to each hotspot area, with the attention duration and sight point density of the hotspot area as the core indicators: where ω j Indicates the time weight of the sight point. The greater the regional intensity, the higher the user's attention. Finally, based on the clustering results of the sight hotspot area, the visual heat map is generated through the interpolation algorithm. The color depth represents the hotspot intensity, and the user's preferred hotspot in the virtual exhibition hall is updated in real time. The sight heat map formula is: Where (x i ,y i ) represents the location of the cluster center, and σ controls the range of diffusion. Visual heat maps are generated through line of sight trajectory analysis to identify user preference areas, provide data support for subsequent marketing, improve the efficiency of exhibition data collection, and enhance the responsiveness of exhibition activities to user needs.

[0037] The above description is only a preferred embodiment of the present invention and does not limit the present invention in other forms. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for interactive experience of automobile exhibition based on virtual reality, characterized in that: The following steps are involved: S1. First, use 3D modeling technology to build a virtual exhibition hall and a 3D model of the model on display in the exhibition hall, and optimize the model to allow users to immersively view the appearance and internal structure of the displayed model; S2. Secondly, record the user's browsing behavior, analyze preferences through the recommendation algorithm, generate personalized car model recommendations in real time, and display them in the user's field of view for further review; S3. After the user selects a car model, the system provides color, wheel, and seat configuration options and renders them in real time. The personalized configuration can be saved and used for sales communication; S4. The user chooses to test drive. The system loads different driving scenarios, simulates the control experience, and provides feedback on acceleration and steering effects, allowing the user to experience the vehicle performance. S5. Finally, we use the eye tracking technology based on clustering algorithm to capture the specific areas of users’ attention and generate visual heat maps to identify users’ preferences and provide data support for subsequent marketing and exhibition planning. The specific implementation of using the 3D modeling technology to construct the 3D model of the virtual exhibition hall and the vehicle model displayed in the exhibition hall and optimizing the model in step S1 is as follows: S11. First, the exhibition hall environment and the vehicle are 3D modeled using refined modeling technology. The model is divided into an appearance geometry model and an internal structure model. The overall model M is expressed as: M = M ext +M int , where M ext represents the vehicle appearance model, M int represents the vehicle interior model; S12, then applying detail enhancement technology to the vehicle model M, increasing polygon details through a surface subdivision method, so that the surface is smoother; S13, then use the high-resolution map to texture map the surface of the model, the map is represented by T ext and T int , corresponding to the appearance and internal structure respectively, the texture of the model M after mapping is: M = M ext ·T ext +M int ·T int And use a physically based lighting model to enhance realism. The lighting equation is: L(o) = k d ·(N·L i )·I i +k s ·(R·V) n I i , where L(o) is the output brightness, k d , k s are diffuse reflection and specular reflection coefficients respectively, N is the normal vector, L i is the incident light, R is the reflection vector, V is the sight vector, I i is the incident light intensity, n is the specular coefficient; S14. In addition, real-time rendering is achieved based on deferred shading technology. The geometric information, normal vectors and texture data are first rendered to the buffer, and then the lighting calculation is performed to optimize the rendering efficiency. The real-time rendering frame rate target is F ≥ 90FPS to meet the smoothness of the immersive experience; S15. Finally, use spatial positioning technology to track the user's head position and viewing angle. The initial position is (x0, y0, z0), and the real-time position is (x, y, z). The viewing angle position update formula is: Δx = x-x0, Δy = y-y0, Δz = z-z0, and the model presentation perspective is adjusted through view matrix transformation to achieve an immersive viewing effect for the user in the virtual space.

2. The method for interactive experience of automobile exhibition based on virtual reality according to claim 1, characterized in that: The specific implementation of analyzing preferences by improving the recommendation algorithm in step S2 and generating personalized vehicle model recommendations in real time is as follows: S21. First, convert user preferences into feature vectors: V user =(S1,S2,...S m ), where S i Represents the user's preference score for the i-th type of vehicle. Vectorize the description of each vehicle model in the vehicle model data set to obtain: V model =(M1,M2,...M m ), where M i represents the characteristic description of the i-th model; S22. Next, weighted cosine similarity calculation is introduced. The weighted cosine similarity formula is: where ω i The system assigns a weight to each feature, sorts the features based on similarity, recommends the top-scoring models to users, and dynamically adjusts the recommendations based on the user's real-time interactive behavior. S23. Finally, record the user's feedback on the recommended content and fine-tune the preference feature vector so that the improved recommendation algorithm can quickly adapt to the user's latest interests. The preference update formula is: V user =αV user-old +(1-α)V feedback , where α is the adjustment coefficient, V user-old For the original preference setting, V feedback Feedback vector for users.

3. The method for interactive experience of automobile exhibition based on virtual reality according to claim 1, characterized in that: In step S4, a highly immersive virtual reality experience system is constructed to simulate the control experience for the user's test drive, and the specific implementation steps are as follows: S41. First, an intuitive interface is provided, where users can select different driving scenarios. According to the scenario selected by the user, the system loads the corresponding 3D environment model in real time, uses scene rendering technology to make the environment have a realistic visual effect, and sets specific environmental characteristics for each scene, including weather, time, and road conditions, to enhance the sense of immersion. The characteristics are expressed as: E si ={w i ,t i ,r i }, where w is the weather, t is the time, and r is the road condition; S42, secondly, supporting the user to use a steering wheel, pedals or handle device for control, and collecting the user's control input in real time, the input data is expressed as: I = {a, s, t}, where a is acceleration, s is the steering angle, and t is braking; S43. Finally, while the user is controlling the vehicle, the acceleration and steering effects are fed back in real time, including steering wheel vibration, seat movement and visual effects, to enhance the sense of immersion.

4. The method for interactive experience of automobile exhibition based on virtual reality according to claim 1, characterized in that: The eye tracking technology based on the clustering algorithm in step S5 captures the specific area of ​​user attention and generates a visual heat map to identify the user's preferred focus. The specific operation is: S51, firstly, obtain the user's sight trajectory data in the virtual exhibition hall in real time through the VR device, including the viewing angle position, the duration of stay and the sight path. The sight data point set is G = {g1, g2, ... g n }, where g i Contains the coordinates of the data point (x i ,y i ) and timestamp t i ; S52, then by calculating the data point density at each position, obtain the high-frequency area where the user's sight stays, and the stay point is calculated as: Where δ(g i ,g j ) represents the sight point g i and g j The larger the value, the longer the stay time and the higher the attention; S53, then use the density-based clustering algorithm to cluster the sight data points in real time, divide the area where the user's sight focuses into several high-density hot spots, and use the clustering results to identify the hot spots in the virtual exhibition hall. The c of each cluster center is i Attention intensity H i It includes the number of sight points and assigns a weight to each hotspot area, with the attention duration and sight point density of the hotspot area as the core indicators: where ω j Indicates the time weight of the sight point. The larger the regional intensity, the higher the user's attention. S54. Finally, based on the clustering results of the sightline hotspot area, a visual heat map is generated through an interpolation algorithm. The intensity of the hotspot is represented by the color depth, and the user's preferred hotspot in the virtual exhibition hall is updated in real time. The sightline heat map formula is: Where (x i ,y i ) represents the location of the cluster center, and σ controls the range of diffusion.

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