Route-Based VR Driving Scenery with Dynamic 3D Object Models
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Solution Overview
Problem
Existing virtual reality (VR) systems face challenges in dynamically generating realistic driving environments for vehicle test drives, requiring significant processing resources and memory due to the need for pre-generated models of various routes and vehicle viewpoints, which also consume fuel and increase wear on actual vehicles.
Innovation Solution
A system that generates VR driving scenery on demand using route information, incorporating machine learning to identify objects from images and determine a visual reference point based on vehicle and user data, allowing for dynamic creation of realistic environments without pre-generated models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pre-generated models of various routes and vehicle viewpoints are used, then realistic driving environments can be provided, but processing resources and memory are significantly consumed
Solution Approach 1:
The system performs preliminary actions by generating route models and identifying objects along the route before the VR driving session begins. Geographic coordinate information and topographical data are processed in advance to create the route model, and images are pre-analyzed to identify objects and their locations. This allows the actual VR session to use these pre-prepared elements without consuming excessive processing resources during runtime.
Solution Approach 2:
The system creates virtual copies of the driving environment by generating a model of the route with scenery based on geographic coordinate information and topographical information. Instead of using real vehicles for test drives, virtual representations are created that replicate the essential characteristics of the actual driving environment, allowing users to experience realistic driving conditions without physical vehicle consumption.
2Reliability
If pre-generated models of various routes and vehicle viewpoints are used, then realistic driving environments can be provided, but memory consumption increases significantly
Solution Approach 1:
The system segments the driving environment into distinct components: route model (based on geographic coordinate information and topographical information), objects (identified from images with their geographic locations and three-dimensional spatial information), and scenery. This segmentation allows memory to be allocated efficiently for each component rather than loading entire pre-generated environments, reducing overall memory consumption while maintaining realism.
Solution Approach 2:
The system employs dynamic generation of the route model and scenery based on user-selected routes rather than maintaining static pre-generated models for all possible routes. The route model is created on-demand using geographic coordinate information and topographical information, and objects are dynamically identified from images specific to each route, allowing memory usage to adapt to actual needs rather than allocating for all potential scenarios.
3Reliability
If physical test drives are conducted to provide realistic driving experience, then users can experience vehicles, but fuel is consumed and vehicle wear increases
Solution Approach 1:
The system creates virtual copies of the driving experience by generating a model of the selected route with identified objects and scenery, placing a vehicle model in the route, and providing the VR driving session from the perspective of the visual reference point within the vehicle model. This virtual replication allows users to experience driving the vehicle in familiar environments without consuming fuel or causing physical wear to actual vehicles.
Solution Approach 2:
The system replaces the mechanical system of physical test drives with a virtual reality-based system. Instead of physically driving vehicles to provide test drive experiences, the system uses computer-generated models, image processing, and VR technology to simulate the driving experience, eliminating fuel consumption and mechanical wear while maintaining the essential function of vehicle evaluation.
Data Source
AI summary
In some implementations, a device may identify objects associated with the route based on one or more images associated with a selected route. The device may generate a model associated with the route including the scenery, wherein the scenery includes models of the objects based on geographic locations of the objects and three-dimensional spatial information of the objects. The device may determine a visual reference point for the virtual reality driving session based on at least one of vehicle information associated with the vehicle or user information. The device may provide, to a virtual reality device, presentation information that causes the virtual reality driving session to be displayed by the virtual reality device from a perspective of the visual reference point within a vehicle model associated with a selected vehicle placed in the model of the route with the scenery.