Toward Safety-First Human-Like Decision Making for Autonomous Vehicles in Time-Varying Traffic Flow

Le résumé fourni par la source

Despite the recent advancements in artificial intelligence (AI) technologies showing great potential in improving transport efficiency and safety, autonomous vehicles (AVs) still face great challenges when driving in time-varying traffic flow, especially in dense and interactive situations. Meanwhile, humans have free will and usually do not make the same decisions even situated in exactly the same scenarios, leading to data-driven methods suffering from poor migratability and high search cost problems, decreasing the efficiency and effectiveness of the behavior policy. In this article, we propose a safety-first human-like decision-making (SF-HLDM) framework for AVs to drive safely, comfortably, and with social compatibility and efficiency. The framework integrates a hierarchical progressive architecture, which combines a spatial–temporal attention (STA) mechanism for other road users’ intention inference, a social compliance estimation (SCE) module for behavior regulation, and a deep evolutionary reinforcement learning (DERL) model for expanding the search space efficiently and effectively to make avoidance of falling into the local optimal trap and reduce the risk of overfitting, thus make human-like decisions with interpretability and flexibility. The SF-HLDM framework enables autonomous driving AI agents to dynamically adjust decision parameters to maintain safety margins while adhering to contextually appropriate driving behaviors at the same time. Extensive experiments in car learning to act, an open-source autonomous-driving simulator (CARLA) validate the framework’s superior performance, which enlarges the minimum time to worst-case hazards (TWHs) by 41.8% to keep a safer distance away from others, while improving the average velocity by 2.5%, reducing the average acceleration and yaw rate by 23.5% and 60.5%, respectively. The results highlight the potential of SF-HLDM to bridge the gap between machine-driven precision and human-like flexibility in AV systems, paving the way for more interpretable and socially acceptable autonomous driving solutions.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.