3D Virtual Road Scene Modeling for Autonomous Driving Training

The patent creates virtual copies of real-world geographical areas, objects, and environments through 3D modeling and rendering. These virtual replicas serve as synthetic training data that mimics real-world conditions without requiring physical testing, thereby maintaining data authenticity while dramatically improving training efficiency and reducing resource consumption.

The patent replaces physical mechanical testing systems with virtual simulation systems. Instead of moving real vehicles through real geographical areas to collect training data, the system uses computer-generated virtual environments to provide the same training function, eliminating the need for physical resource deployment while maintaining training effectiveness.

The patent creates virtual copies of diverse real-world scenarios including different geographical areas, weather conditions, traffic patterns, and edge cases. These virtual scenarios provide comprehensive scenario diversity for training while eliminating physical risk, as failures in the virtual environment do not result in real-world accidents or damage.

The patent allows extensive testing and validation of autonomous driving systems in virtual environments before real-world deployment. This beforehand cushioning approach enables the system to fail safely in simulation, learn from errors, and be thoroughly validated against diverse scenarios including rare edge cases, thereby reducing risk when transitioning to real-world operation.

The patent enables continuous virtual training operations without the interruptions inherent in real-world testing. Virtual simulations can run continuously across multiple systems simultaneously, weather conditions, and geographical locations without safety constraints, maximizing the accumulation of training data and improving system reliability more efficiently than sequential real-world testing.

The patent merges multiple training functions into a unified virtual simulation platform that can simultaneously test diverse scenarios, collect training data, validate system performance, and stress-test edge cases. This consolidation achieves comprehensive reliability validation faster than separate real-world testing programs by parallelizing training operations across virtual environments.