Exploring Critical Testing Scenarios for Decision-Making Policies: An LLM Approach

Abstract

Recent advances in decision-making policies have led to notable progress in various fields such as autonomous driving and robotics. However, testing these policies remains crucial with the existence of critical scenarios that may threaten their reliability. Despite ongoing research, challenges such as low testing efficiency and limited scenario diversity persist, primarily due to the complexity of the policies and their operating environments. To address these challenges, this paper proposes an adaptable Large Language Model (LLM)-driven adaptive testing framework to explore critical and diverse testing scenarios for decision-making policies. Specifically, we introduce a “generate-test-feedback” pipeline, leveraging templated prompt engineering to harness the world knowledge and reasoning abilities of LLMs. Additionally, a multi-scale scenario generation strategy is proposed to overcome the limitations of LLMs in making fine-grained adjustments, thereby further enhancing testing efficiency. Furthermore, during the feedback phase, a well-designed scenario evaluation module assesses the criticality and diversity of the generated scenarios for adaptive testing. The proposed LLM-driven method is extensively evaluated on five widely recognized benchmarks, demonstrating that our proposed method significantly outperforms baseline methods in uncovering both critical and diverse scenarios. These findings highlight the great promise of LLM-driven schemes in advancing the testing of decision-making policies.