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

TL;DR

This paper introduces a risk-aware reward function for reinforcement learning in autonomous driving, improving safety and performance by addressing the limitations of traditional reward designs and incorporating a novel safety metric.

Contribution

It proposes a hierarchical, normalized reward formulation with a new risk-aware objective based on an ellipsoid function and RSS extension, enhancing safety in RL-driven autonomous driving.

Findings

Collision rates decreased by 21% on average.

The approach outperforms baseline rewards in route progress.

Maintains high cumulative rewards while improving safety.

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-aware objective for various driving interactions based on a two-dimensional ellipsoid function and an extension of Responsibility-Sensitive Safety (RSS) concepts. We evaluate the efficacy of our proposed reward in unsignalized intersection scenarios with varying traffic densities. The approach decreases collision rates by 21\% on average compared to baseline rewards and consistently surpasses them in route progress and cumulative reward, demonstrating its capability to promote safer driving behaviors while maintaining high-performance levels.