Adversarial Evaluation of Autonomous Vehicles in Lane-Change Scenarios

Understand

Autonomous vehicles must be comprehensively evaluated before deployed in cities and highways.

• However, most existing evaluation approaches for autonomous vehicles are static and lack adaptability, so they are usually inefficient in generating challenging scenarios for tested vehicles.
• In this paper, we propose an adaptive evaluation framework to efficiently evaluate autonomous vehicles in adversarial environments generated by deep reinforcement learning.
• Considering the multimodal nature of dangerous scenarios, we use ensemble models to represent different local optimums for diversity.