Aggressiveness-regulated Multi-agent Stress Testing of Autonomous Vehicles

Abstract

The emerging era of autonomous vehicles (AVs) presents unprecedented potential for transforming global transportation. As these vehicles begin to permeate our streets, the challenge of ensuring their safety, especially in unprecedented scenarios, looms large, due to the infrequent occurrence of high-risk scenarios within an essentially infinite number of test cases. This Master's thesis explores the intricate challenge of stress testing autonomous vehicles in simulated environments. The study delves into the application of multi-agent reinforcement learning (MARL) as a tool for stress testing AVs. Although MARL demands higher computational resources, it demonstrates strong ability in uncovering complex accident scenarios. This marks a shift from the state-of-the-art which deploys single-agent reinforcement algorithms that encounter limitations both in the quality of the generated accident scenarios and in their ability to generate complex accident scenarios as the number of traffic participants increases. Central to our approach is the integration of constraints that regulate the level of aggressiveness of traffic participants to induce more realistic and insightful accident scenarios. The thesis also presents the highway-attack-env, an environment for black-box AV testing that allows the assessment of both single and multi-agent reinforcement learning algorithms. The contributions of this research include the introduction of the aforementioned environment and a comprehensive benchmark, as well as a comparative analysis of single-agent and MARL algorithms, underscoring the superiority of the proposed multi-agent, aggressiveness-regulated methodology for AV validation.