Before deploying autonomous vehicles (AV), comprehensive safety assessments are essential. To enhance efficiency, generating safety-critical scenarios to accelerate testing is crucial. This paper proposes an adversarial strategy based on deep reinforcement learning to generate safety-critical scenarios for evaluating autonomous vehicle performance. First, a highway ramp merging scenario is modeled using a Markov process. Second, deep reinforcement learning is used to train the background vehicle (BV) to generate adversarial behaviors against the autonomous vehicle. A reasonable reward mechanism is introduced to prevent extreme dangerous behaviors and ensure scenario rationality. Simulation results show that the generated safety-critical scenarios significantly increase collision rates and reduce the performance of AV. Additionally, this method can generate scenarios with varying levels of danger, supporting more refined testing.
Adversarial Generation for Autonomous Vehicles in Safety-Critical Ramp Merging Scenarios
Lect. Notes Electrical Eng.
International Conference on Electric Vehicle and Vehicle Engineering ; 2024 ; Zhuhai, China December 20, 2024 - December 22, 2024
Selected Contributions of 2024 2nd International Conference on Electric Vehicle and Vehicle Engineering ; Chapter : 44 ; 427-434
2025-07-01
8 pages
Article/Chapter (Book)
Electronic Resource
English
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