Autonomous Vehicle Simulation Training for Edge-Case Control Recovery
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Solution Overview
Problem
Existing end-to-end trained neural networks for autonomous vehicles lack robustness and require vast amounts of expensive and dangerous training data, especially for edge cases like off-orientation positions and near collisions, and transferring policies from simulation to the real world remains a challenge.
Innovation Solution
A simulation-based training engine that synthesizes photorealistic driving trajectories from a small dataset of real-world data, enabling robust learning of lane-stable control policies without prior human knowledge, and allows direct deployment in real-world environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vast amounts of real-world training data are collected to train autonomous vehicle control policies, then the robustness and reliability of the vehicle improves, but the time, cost, and safety risks increase significantly
Solution Approach 1:
The patent creates photorealistic simulated driving environments that copy real-world road scenes, lighting conditions, and traffic patterns. By rendering synthetic training data that mimics real-world conditions, the system achieves robust policy training without physically collecting data in dangerous edge-case scenarios
Solution Approach 2:
The system performs preliminary synthesis of diverse driving scenarios including rare edge cases before training begins. By pre-generating a comprehensive dataset of challenging situations (off-orientation positions, near collisions, unusual weather), the training process can proceed efficiently without time-consuming real-world data collection
2Adaptability or versatility
If photorealistic simulation environments are created from limited real-world data, then the transferability of trained policies to real-world deployment improves, but the complexity of the simulation system increases
Solution Approach 1:
The simulation engine serves multiple functions: it renders photorealistic images, synthesizes diverse driving scenarios, generates training datasets, and evaluates policies. By making the simulation system multi-functional, the patent achieves high transferability without proportionally increasing complexity
Solution Approach 2:
The system varies parameters such as lighting conditions, weather patterns, road types, and vehicle positions to generate diverse training scenarios from limited base data. By systematically changing environmental parameters, the simulation creates transferable policies without requiring complex manual scene construction
3Object-affected harmful factors
If simulation-based training is used instead of real-world data collection, then safety and cost improve, but the challenge of transferring policies from simulation to real-world increases
Solution Approach 1:
By creating photorealistic copies of real-world environments in simulation, the patent bridges the simulation-reality gap. The synthesized images closely mimic actual camera feeds, ensuring that policies trained in simulation transfer reliably to real-world deployment while maintaining safety
Solution Approach 2:
The system uses reinforcement learning with reward signals that provide feedback on policy performance in simulation. This feedback mechanism allows iterative improvement of policies before real-world deployment, ensuring high transferability while eliminating safety risks of real-world trial-and-error training
Data Source
AI summary
A controller for an autonomous vehicle is trained using simulated paths on a roadway and simulated observations that are formed by transforming images previously acquired on similar paths on that roadway. Essentially an unlimited number of paths may be simulated, enabling optimization approaches including reinforcement learning to be applied to optimize the controller.