CaDRE: Controllable and Diverse Generation of Safety-Critical Driving Scenarios using Real-World Trajectories
CoRR(2024)
摘要
Simulation is an indispensable tool in the development and testing of
autonomous vehicles (AVs), offering an efficient and safe alternative to road
testing by allowing the exploration of a wide range of scenarios. Despite its
advantages, a significant challenge within simulation-based testing is the
generation of safety-critical scenarios, which are essential to ensure that AVs
can handle rare but potentially fatal situations. This paper addresses this
challenge by introducing a novel generative framework, CaDRE, which is
specifically designed for generating diverse and controllable safety-critical
scenarios using real-world trajectories. Our approach optimizes for both the
quality and diversity of scenarios by employing a unique formulation and
algorithm that integrates real-world data, domain knowledge, and black-box
optimization techniques. We validate the effectiveness of our framework through
extensive testing in three representative types of traffic scenarios. The
results demonstrate superior performance in generating diverse and high-quality
scenarios with greater sample efficiency than existing reinforcement learning
and sampling-based methods.
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