Balancing Progress and Safety: A Novel Risk-Aware Objective for RL in Autonomous Driving (2505.06737v1)

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.

• The paper introduces a novel risk-aware objective and a hierarchical reward structure to address the challenge of balancing progress and safety in Reinforcement Learning for autonomous driving.
• The proposed method structures the reward function hierarchically, incorporating a dynamic risk field alongside objectives for progress, comfort, and traffic rule adherence.
• Experimental results in simulation demonstrate that agents trained with the risk-aware framework achieve lower accident rates and improved efficiency compared to agents trained with simpler reward functions.

Balancing Progress and Safety in Reinforcement Learning for Autonomous Driving

The paper presents a novel framework for improving Reinforcement Learning (RL) applications in autonomous driving, focusing on balancing progress and safety through enhanced reward function design. It highlights the need for a robust reward formulation to guide RL agents in real-world driving scenarios, addressing a primary issue in existing approaches: the oversimplification and inadequate design of reward functions.

Hierarchical Reward Structuring

Central to this paper is the employment of a hierarchical structure in the reward function that organizes driving objectives according to their priority. This structured approach allows distinct components—safety, progress, comfort, and traffic rule conformity—to be weighted and assessed systematically. The reward hierarchy is inspired by Rulebooks, offering a transparent method to balance conflicting objectives effectively. The hierarchy's top levels are reserved for terminal conditions crucial in determining the course of a driving scenario, such as collision penalties and off-road violations.

Key Objectives and Innovations

1. Risk-Aware Objective: The paper introduces a risk-aware objective, forming the cornerstone of the proposed reward structure. It leverages a two-dimensional ellipsoid risk field to dynamically calculate penalties based on interactions' geometric and dynamic properties, responding to the challenges previously unaddressed by metrics like Time-to-Collision (TTC) or headway. This approach draws from safety frameworks like Responsibility-Sensitive Safety (RSS) and Nvidia's Safety Force Field, dynamically assessing safe distances and worst-case scenarios considering real-world driving complexity.
2. Progress and Comfort: The reward function incorporates multiple objectives beyond safety. The progress objective incentivizes the agent to navigate effectively towards its destination, while the comfort objective penalizes excessive steering rates, accelerations, and jerks. This combination aims to ensure not only timely arrival but also adherence to a driving style that aligns with safe, smooth operational norms.
3. Traffic Rule Conformance: The reward includes a component dedicated to maintaining compliance with traffic rules, such as obeying speed limits and adhering to lane discipline. This soft constraint complements safety-focused objectives, ensuring agents respect social norms and regulatory requirements.

Experimental Evaluation

Implemented within a simulation environment featuring unsignalized intersections and varying traffic densities, the framework's efficacy is evaluated by training RL agents using Deep Q-Networks (DQN) across different combinations of the proposed reward levels. Results demonstrate that agents trained with the complete reward function incorporating risk awareness perform superiorly across various safety and efficiency metrics, showcasing notably lower accident rates and enhancing overall route progression and task completion success.

Practical Implications and Future Directions

The findings indicate significant potential for integrating sophisticated risk-aware formulations in RL reward functions for autonomous driving. This work contributes to advancing safer, more reliable autonomous navigation, addressing the critical need to balance rapid progress with risk mitigation. Future developments could expand on the integration of these principles into more complex and diverse driving scenarios, potentially adapting and deploying risk-awareness frameworks into real-world autonomous systems as part of standard operational protocols.

The paper lays crucial groundwork for further exploration into hierarchical reward structures, promoting robust frameworks in autonomous decision-making that reflect real-world complexities and dynamic interactions. Continued exploration into scalable implementations of these concepts may catalyze transformative advancements in the efficacy and safety of AI-driven vehicles.