3D Virtual Driving World Simulation for Scalable AV Training
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Solution Overview
Problem
Current methods for training and validating autonomous driving systems require extensive real-world testing, which is resource-intensive, risky, and limits scalability due to the need for multiple geographical locations and varied environmental conditions.
Innovation Solution
A computer-implemented method and system for creating a simulated realistic virtual model of a geographical area, incorporating geographic map data, visual imagery, and dynamic objects, which generates synthetic 3D imaging feeds to train autonomous driving systems, allowing for automated and concurrent testing across various scenarios without real-world vehicle movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-world testing is used to train and validate autonomous driving systems, then the training data reflects actual driving conditions, but the resource consumption and time required increase significantly
Solution Approach 1:
The patent creates virtual copies of real-world geographical areas, objects, and driving scenarios through 3D modeling and photogrammetry. These virtual replicas serve as substitutes for physical real-world testing, allowing autonomous driving systems to be trained and validated in simulated environments that accurately mirror actual driving conditions without the time and resource costs of physical testing.
Solution Approach 2:
The patent replaces the mechanical system of physical vehicle testing with a computational simulation system. Instead of moving real vehicles through real geographical areas to collect training data, the system uses computer-generated virtual environments and synthetic imaging feeds to provide the same training function, thereby eliminating the need for physical resource consumption and extensive time requirements.
2Adaptability or versatility
If real-world testing is conducted across multiple geographical locations and environmental conditions, then the system validates performance under diverse scenarios, but the complexity and resource requirements increase
Solution Approach 1:
The patent creates a universal virtual testing platform that can simulate multiple geographical locations, environmental conditions, and driving scenarios within a single system. This multi-functional simulation environment allows the autonomous driving system to be validated across diverse conditions without requiring separate physical testing infrastructures for each location or scenario, thereby reducing overall system complexity while maintaining scenario diversity.
Solution Approach 2:
The patent adds a virtual dimension to the testing process by creating three-dimensional simulated environments that replicate real-world geographical areas. This dimensional transformation allows multiple testing scenarios to coexist and be executed concurrently in the virtual space, eliminating the need for physically relocating test vehicles across multiple geographical locations and reducing the complexity of coordinating diverse testing operations.
3Reliability
If extensive real-world testing is performed to ensure safety and reliability, then the autonomous driving system achieves high reliability, but the risk of accidents and damages during testing increases
Solution Approach 1:
The patent implements a virtual simulation environment that serves as a protective buffer before real-world deployment. By thoroughly training and validating the autonomous driving system in the risk-free virtual environment first, the system achieves high reliability without exposing real vehicles and personnel to the harmful risks of testing accidents. The virtual cushioning layer allows extensive testing to be performed without any physical danger.
4Reliability
If multiple geographical areas and environmental conditions are tested using real vehicles, then comprehensive validation is achieved, but the scalability of the training process is limited
Solution Approach 1:
The patent performs preliminary creation of virtual models and environments before conducting any validation testing. By pre-building three-dimensional virtual replicas of multiple geographical areas and environmental conditions, the system enables concurrent execution of multiple validation scenarios without the logistical constraints of physical testing. This preliminary virtual preparation allows comprehensive validation across diverse conditions while maintaining high scalability, as additional scenarios can be added to the virtual environment without requiring additional physical resources.
Data Source
AI summary
A computer implemented method of creating a simulated realistic virtual model of a geographical area for training an autonomous driving system, comprising obtaining geographic map data of a geographical area, obtaining visual imagery data of the geographical area, classifying static objects identified in the visual imagery data to corresponding labels to designate labeled objects, superimposing the labeled objects over the geographic map data, generating a virtual 3D realistic model emulating the geographical area by synthesizing a corresponding visual texture for each of the labeled objects and injecting synthetic 3D imaging feed of the realistic model to imaging sensor(s) input(s) of the autonomous driving system controlling movement of an emulated vehicle in the realistic model where the synthetic 3D imaging feed is generated to depict the realistic model from a point of view of emulated imaging sensor(s) mounted on the emulated vehicle.