Virtual Reality Vehicle Training Platform Using Commodity Hardware
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current virtual reality vehicle training simulators are expensive, inflexible, and limited in their ability to provide low-to-medium fidelity training on multiple vehicles and environments, lacking the capability for rapid prototyping and effective responsiveness measurement and assessment.
Innovation Solution
A virtual reality vehicle training platform and network that utilizes commodity hardware, allows for flexible vehicle and environment simulation, and includes responsiveness measurement devices to assess trainee performance, enabling cost-effective, adaptable, and comprehensive training across various vehicle types and scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full motion or 6 degrees of axis high fidelity simulators are used for VR training, then training realism and immersion are improved, but cost, portability, and maintenance requirements worsen
Solution Approach 1:
The patent uses virtual copies and digital representations of vehicles and environments instead of physical replicas. The system creates virtual models of vehicles, terrains, and operating conditions that can be replicated infinitely without additional physical infrastructure, resolving the contradiction between training realism and device complexity
Solution Approach 2:
The patent replaces complex mechanical simulation systems with software-based virtual reality environments. Instead of using physical 6-degree-of-freedom motion platforms and mechanical vehicle replicas, the system uses computer-generated visuals, physics simulations, and virtual sensors to replicate driving experiences, significantly reducing hardware complexity while maintaining training effectiveness
2Manufacturing precision
If vehicle-specific custom interfaces are created for each vehicle type, then training accuracy for that vehicle is improved, but development time, cost, and adaptability worsen
Solution Approach 1:
The patent implements a universal virtual training platform that can accommodate multiple vehicle types through software configuration rather than hardware redesign. The system uses parameterized vehicle models and configurable interface templates that can be adapted to different vehicle types (trucks, buses, specialized equipment) without creating entirely new custom interfaces for each, thus achieving both training accuracy and versatility
Solution Approach 2:
The patent employs parameter-driven vehicle models where vehicle-specific characteristics (dimensions, weight, control characteristics, performance parameters) are defined as configurable variables. By changing these parameters, the same base simulation engine can accurately represent different vehicle types, eliminating the need for bespoke development for each vehicle while maintaining training fidelity
3Ease of operation
If traditional VR simulators are used, then single-vehicle training capability is provided, but flexibility in vehicle, specification and VR environment worsens
Solution Approach 1:
The patent implements dynamic reconfigurability where the training environment can be changed on-the-fly through software settings. Instructors can dynamically adjust vehicle types, terrain conditions, weather, time of day, and training scenarios without physical reconfiguration, allowing the same hardware platform to adapt to diverse training needs across different vehicle types and environmental conditions
4Measurement precision
If conventional assessment methods are used, then training performance is monitored, but responsiveness measurement and prediction capability worsens
Solution Approach 1:
The patent implements comprehensive feedback systems that capture not only whether a trainee succeeds or fails a task, but also measure response times, reaction patterns, and behavioral metrics. The system provides real-time feedback on trainee performance and uses this data to predict future responsiveness and learning curves, enabling more precise and predictive assessment than traditional pass/fail methods
Data Source
AI summary
A virtual reality vehicle and/or equipment training system and platform within contextually relevant simulated environments. The system and platform simulates accurate vehicle and/or equipment dynamics of various vehicles through vehicle modules. The simulated vehicles and/or equipment are placed within contextually relevant simulated physical environments, and communicate with other vehicle modules and users of the platform. The system and platform allows for multiple simulated vehicles and/or equipment to exist and interact in the same simulated physical environment. The system allows for rapid development of trainers for different vehicles, different equipment, different physical environments, and different scenarios. The characteristics of the system which most directly enable this rapid development are the use of commodity computer hardware, use of common interface-deployment toolkits, and use of a data-driven architecture where participating modules are generic until being configured at runtime. The system and platform is capable of responsiveness measurement and assessment of a simulator occupant.