Actor Trajectory Generation for Autonomous Vehicle Edge-Case Testing
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Solution Overview
Problem
Conventional testing methods for autonomous vehicle control software are inefficient and costly, requiring extensive real-world driving to identify rare edge-case scenarios, which can lead to increased risk due to undetected defects.
Innovation Solution
A computer-implemented method using adversarial reinforcement learning to generate and simulate scenarios, identifying defects in autonomous vehicle software by training agents to explore environments effectively and efficiently, thereby reducing the need for extensive real-world testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional manual testing methods are used to identify rare edge-case scenarios, then testing thoroughness 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 testing millions of miles on real roads, the system generates synthetic scenario data that replicates edge-case conditions, allowing thorough testing without the time and cost of extensive real-world driving.
Solution Approach 2:
The system performs preliminary generation of adversarial scenarios and identification of edge cases before actual testing begins. By pre-generating challenging scenarios and pre-identifying potential defect conditions through simulation, the system avoids the need for time-consuming trial-and-error testing in real-world conditions.
2Adaptability or versatility
If extensive real-world driving is conducted to capture low-probability events, then scenario coverage is improved, but productivity decreases due to the large number of miles required
Solution Approach 1:
The system changes the parameters of scenario generation by introducing adversarial perturbations to scene descriptions, object positions, and environmental conditions. This allows the simulation to explore a wider range of scenario variations and edge cases without requiring proportional increases in real-world driving miles, thereby improving scenario coverage while maintaining productivity.
Solution Approach 2:
The patent replaces the mechanical system of physical vehicle testing with a computational simulation system. Instead of physically driving vehicles to capture scenarios, the system uses computer-generated simulations with adversarial reinforcement learning agents to generate and explore scenarios, dramatically improving productivity while maintaining comprehensive scenario coverage.
3Adaptability or versatility
If random noise is added to scenario parameters to expand scenario variety, then scenario diversity is improved, but testing efficiency decreases due to the inefficiency of random exploration
Solution Approach 1:
The adversarial reinforcement learning agents receive feedback from the simulation environment about which scenarios trigger defects or challenging behaviors. This feedback mechanism allows the system to learn from previous testing results and focus subsequent scenario generation on high-value areas, improving testing efficiency while maintaining scenario diversity through targeted rather than random exploration.
Solution Approach 2:
The system dynamically adjusts scenario generation parameters based on learning from previous testing outcomes. Instead of using static random noise addition, the adversarial agents dynamically modify scenario parameters to maximize their ability to uncover defects, thereby improving testing efficiency while maintaining diverse scenario coverage through adaptive exploration.
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; and generating, by the first agent, a second trajectory of the first actor based on the observed first observation of the environment.