Autonomous Vehicle Agent Training via Simulated Experience Tuples
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Solution Overview
Problem
Autonomous vehicles face challenges in efficiently generating reliable and realistic experience tuples for training reinforcement learning models, requiring extensive human-labeled data and posing safety risks during training, especially in dangerous environments.
Innovation Solution
The use of reinforcement learning to train interactive agents with a separation between quality and vehicle behavior models, allowing for automated data generation and simulation-based training, which reduces the need for human-labeled data and enhances safety by allowing training in simulated environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reinforcement learning is used to train autonomous vehicle agents with extensive human-labeled data, then the reliability of training data is improved, but the time required to build and train the system increases significantly
Solution Approach 1:
The patent uses simulated environments to create virtual copies of real-world driving scenarios, allowing the autonomous vehicle agent to be trained on synthetic data that mimics real driving conditions. This eliminates the need for extensive human-labeled real-world data while significantly reducing training time, as simulations can be generated and processed much faster than manual data collection and labeling.
Solution Approach 2:
The system pre-generates diverse driving scenarios and environments in simulation before actual deployment. By preparing training data and scenarios in advance through simulation, the system avoids the time-consuming process of collecting and labeling real-world data while ensuring comprehensive coverage of edge cases and dangerous situations.
2Reliability
If reinforcement learning training is conducted in real-world environments with physical vehicles, then the realism of training data is improved, but safety risks increase significantly
Solution Approach 1:
The patent trains autonomous vehicle agents in virtual copies of real-world environments using high-fidelity simulations. These simulated environments replicate physical laws, vehicle dynamics, and driving scenarios with sufficient realism to produce reliable training data, while eliminating all physical safety risks associated with real-world testing.
Solution Approach 2:
The simulation environment acts as an intermediary between the autonomous vehicle software and the physical world. It provides a safe intermediate layer where the agent can learn from realistic scenarios without direct exposure to physical dangers, allowing extensive training that would be unsafe or impractical in the real world.
3Productivity
If automated data generation is used instead of human-labeled data, then the productivity of data generation is improved, but the quality and realism of training data may deteriorate
Solution Approach 1:
The system generates synthetic training data by copying and simulating real-world driving scenarios in virtual environments. This automated approach maintains high data quality because the simulations are designed to replicate physical laws, vehicle behaviors, and environmental conditions accurately, producing realistic training data without manual labeling while achieving high productivity.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing an interactive autonomous vehicle agent. One of the methods includes receiving a request to generate an experience tuple for a vehicle in a particular driving context. A predicted environment observation representing a predicted environment of the autonomous vehicle after the candidate action is taken by the autonomous vehicle in an initial environment is generated, including providing an initial environment observation and the candidate action as input to a vehicle behavior model neural network trained to generate predicted environment observations. An immediate quality value is generated from a context-specific quality model that generates immediate quality values that are specific to the particular driving context. An experience tuple comprising the initial environment observation, the candidate action, and the immediate quality value is generated and used as input to a reinforcement learning system for the autonomous vehicle.