Adversarial 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 capture rare edge-case scenarios, which can lead to missed defects and increased risk.
Innovation Solution
A computer-implemented method using adversarial reinforcement learning to generate informed trajectories for autonomous vehicles, simulating scenarios to identify and remediate defects in the control software, thereby reducing the need for extensive real-world testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional manual testing methods are used to test autonomous vehicle control software, then testing can be performed with simple tools and procedures, but the testing process becomes extremely time-consuming and expensive requiring extensive real-world driving miles
Solution Approach 1:
The patent applies preliminary action by pre-training adversarial agents in simulation environments before real-world testing. These agents learn challenging scenarios and edge cases in advance through reinforcement learning, allowing the testing system to immediately evaluate control software against known difficult scenarios without requiring extensive real-world driving to discover them
Solution Approach 2:
The patent uses copying by creating virtual replicas of real-world scenarios in simulation environments. Adversarial agents are trained on simulated data that copies realistic driving conditions, traffic patterns, and environmental factors, allowing extensive testing to be performed on copied virtual scenarios rather than requiring equivalent real-world miles
2Adaptability or versatility
If random scenario parameters are used to expand test coverage, then more scenarios can be generated from initial scenarios, but the approach becomes inefficient due to the large number of miles required to identify rare edge-case scenarios
Solution Approach 1:
The patent applies dynamics by using adaptive reinforcement learning agents that dynamically adjust their behavior based on the control software being tested. Rather than using static random parameter generation, the adversarial agents learn and adapt their strategies through interaction with the system under test, automatically focusing on revealing edge cases and defects without requiring manual expansion of scenario parameters
Solution Approach 2:
The adversarial agents perform self-service by autonomously generating and executing test scenarios without human intervention. The agents independently learn challenging scenarios, generate adversarial trajectories, and evaluate control software performance, eliminating the need for manual scenario creation and parameter randomization while maintaining high scenario coverage
3Reliability
If extensive real-world driving is performed to capture rare edge-case scenarios, then low-probability events can be discovered, but the cost and time requirements become prohibitively high
Solution Approach 1:
The patent introduces an intermediary simulation environment that mediates between theoretical test cases and real-world validation. Adversarial agents are trained in this intermediate simulated world where rare edge cases can be efficiently generated and tested, then the learned scenarios are transferred to evaluate control software with high reliability without requiring equivalent real-world resource investment
Solution Approach 2:
The patent applies parameter changes by modifying scenario parameters through adversarial learning rather than randomization. The system systematically varies critical parameters such as actor trajectories, environmental conditions, and traffic patterns based on learned adversarial strategies, efficiently exploring the parameter space to discover rare edge cases that would be impossible to capture through conventional testing
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.