Adversarial Trajectory Generation for Autonomous Vehicle Edge Cases
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Solution Overview
Problem
Conventional testing methods for autonomous vehicle control software are expensive, time-consuming, and inefficient in identifying low-probability edge-case scenarios, often failing to discover defects due to the need for extensive real-world driving and random scenario parameterization.
Innovation Solution
A computer-implemented method using adversarial reinforcement learning to simulate scenarios, observe environmental interactions, and generate informed trajectories to identify and remedy defects in autonomous vehicle systems, incorporating techniques like rejection sampling and adversarial scenario generation to enhance defect discovery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional manual testing with real-world driving is used, then defect discovery capability is improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent creates virtual copies of real-world driving scenarios through simulation environments. Instead of physically driving vehicles to test software, the system generates synthetic test scenarios that replicate real-world conditions, allowing defect discovery without the time and cost of actual road testing.
Solution Approach 2:
The system performs preliminary scenario generation and defect identification through adversarial reinforcement learning before actual deployment. By pre-generating challenging scenarios and identifying potential defects in simulation, the system avoids the need for extensive post-deployment real-world testing.
2Adaptability or versatility
If random scenario parameterization is used, then scenario diversity is improved, but efficiency in identifying low-probability events deteriorates
Solution Approach 1:
The adversarial reinforcement learning agent autonomously generates diverse and challenging scenarios without human intervention. The agent self-learns to create low-probability edge cases by interacting with the simulated environment and receiving feedback, eliminating the need for manual scenario design while maintaining high diversity and efficiency.
Solution Approach 2:
The system dynamically changes scenario parameters through the reinforcement learning process. The adversarial agent modifies environmental parameters, actor behaviors, and scenario conditions to generate diverse test cases, achieving both scenario diversity and identification efficiency through automated parameter exploration.
3Reliability
If extensive real-world driving miles are accumulated, then low-probability event identification is improved, but cost and time requirements worsen
Solution Approach 1:
The system creates virtual replicas of low-probability events through simulation rather than accumulating physical miles. The adversarial reinforcement learning agent generates rare edge cases in silico, allowing identification of low-probability defects without the resource-intensive process of real-world fleet deployment and monitoring.
Solution Approach 2:
The system performs preliminary identification of low-probability events through simulated adversarial scenarios before actual deployment. By pre-generating and analyzing challenging scenarios in virtual environments, the system identifies potential defects without requiring extensive real-world mileage accumulation.
Data Source
AI summary
A computer-implemented method of generating trajectories of actors, the method comprising:simulating a first scenario comprising an environment having therein an ego-vehicle, a set of actors, including a first actor, and optionally a set of objects, including a first object, wherein simulating the first scenario comprises using a first trajectory of the first actor;observing, by a first adversarial reinforcement learning agent, a first observation of the environment, for example the ego-vehicle, a second actor of the set thereof and/or the first object of the set thereof, in response to the first trajectory of the first actor; andgenerating, by the first agent, a second trajectory of the first actor based on the observed first observation of the environment.