Autonomous Driving Agent Using Adversarial Reward Map Recovery
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Solution Overview
Problem
Current autonomous driving systems require extensive labeled training data and supervised learning to match human driving abilities, which is time-consuming and inefficient, and lack the ability to recover the reasoning behind driving decisions.
Innovation Solution
The system employs a neural network-based method using deep inverse reinforcement learning to recover a reward map from human driving data, with a generator and discriminator module trained adversarially to generate and validate environmental state interpretations, allowing for unsupervised training of autonomous driving agents without the need for extensive labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If supervised learning with extensive labeled training data is used, then the autonomous driving agent can match human driving abilities, but the training process becomes time-consuming and inefficient
Solution Approach 1:
The system employs unsupervised learning where the autonomous driving agent learns to make driving decisions by observing and analyzing raw driving data without requiring extensive manual labeling. The agent self-trains by processing unlabeled demonstrations of driving behavior, significantly reducing the time and resources needed for data preparation while maintaining the ability to match human driving abilities
Solution Approach 2:
Instead of using supervised learning where labeled data guides the training, the patent inverts the approach by using unsupervised learning where the agent independently discovers patterns and decision-making strategies from unlabeled data. This inversion eliminates the need for time-consuming data labeling while still achieving reliable driving decisions
2Productivity
If supervised learning is used to train autonomous driving agents, then driving decisions can be optimized, but the ability to recover reasoning behind driving decisions is lost
Solution Approach 1:
The patent introduces an environmental state interpretation module that acts as an intermediary between the driving decision-making process and the training data. This module generates human-interpretable descriptions of the environmental states and reasoning behind each driving decision, allowing the system to both optimize driving decisions and preserve the underlying reasoning logic for analysis and improvement
3Measurement precision
If extensive labeling of training data is performed, then training accuracy improves, but the complexity and resource requirements increase significantly
Solution Approach 1:
The system eliminates the need for manual data labeling by implementing unsupervised learning algorithms that automatically process raw driving data. The autonomous driving agent independently identifies patterns, features, and decision-making strategies from unlabeled demonstrations, significantly reducing the complexity and resource requirements associated with extensive data labeling while maintaining training effectiveness
Data Source
AI summary
Systems and method are provided for controlling a vehicle. In one embodiment, a computer-implemented method includes: training an autonomous driving agent is provided, the method including the steps of: extracting, by a processor, information from demonstrations of driving behavior using a neural network; transmitting the extracted information to a generator module; transmitting a real environmental state associated with the demonstrations of driving behavior to a discriminator module; generating, by a processor, environmental state interpretations from the extracted information using the generator module; training, by a processor, the discriminator module to better determine whether the generated environmental state interpretations correspond to the real environmental state, whilst training, by a processor, the generator module to generate an improved environmental state interpretation that the discriminator determines to correspond to the real environmental state; and recovering, by a processor, a reward map using generated environmental state interpretations from the trained generator module.