[2505.06737] Balancing Progress and Safety: A Novel Risk-Aware Objective for RL in Autonomous Driving

Computer Science > Robotics arXiv:2505.06737 (cs) [Submitted on 10 May 2025 (v1), last revised 5 Mar 2026 (this version, v3)] Title:Balancing Progress and Safety: A Novel Risk-Aware Objective for RL in Autonomous Driving Authors:Ahmed Abouelazm, Jonas Michel, Helen Gremmelmaier, Tim Joseph, Philip Schörner, J. Marius Zöllner View a PDF of the paper titled Balancing Progress and Safety: A Novel Risk-Aware Objective for RL in Autonomous Driving, by Ahmed Abouelazm and 5 other authors View PDF HTML (experimental) Abstract:Reinforcement Learning (RL) is a promising approach for achieving autonomous driving due to robust decision-making capabilities. RL learns a driving policy through trial and error in traffic scenarios, guided by a reward function that combines the driving objectives. The design of such reward function has received insufficient attention, yielding ill-defined rewards with various pitfalls. Safety, in particular, has long been regarded only as a penalty for collisions. This leaves the risks associated with actions leading up to a collision unaddressed, limiting the applicability of RL in real-world scenarios. To address these shortcomings, our work focuses on enhancing the reward formulation by defining a set of driving objectives and structuring them hierarchically. Furthermore, we discuss the formulation of these objectives in a normalized manner to transparently determine their contribution to the overall reward. Additionally, we introduce a novel risk-awar...