Brain-SAD: A Brain-Inspired Safe Autonomous Driving Control Framework with Dynamic Fear-Oriented Constraint on Dual-Policy

Problem

Existing Constrained Reinforcement Learning (CRL) methods for Safe Autonomous Driving lack the ability to impose dynamic constraints that adapt to real-time vehicle interactions. This paper addresses this gap by introducing a framework that incorporates dynamic fear-oriented constraints, enhancing the safety and responsiveness of autonomous driving systems. The work is presented as a preprint and has not yet undergone peer review.

Method

The proposed framework, Brain-SAD, utilizes a dual-policy approach consisting of a long-term policy for regular interactions and a short-term policy for urgent collision defense. The core mechanism involves dynamic fear-oriented constraints that are informed by vehicle-interaction scene perception. These constraints are represented by a dynamic fear signal that reflects the overall fear cost associated with potential collisions and a dynamic fear boundary that is derived from the proximity of risky neighbors. This approach allows the system to adaptively adjust its behavior based on the perceived risk in the environment, thereby enhancing safety during operation.

Results

The available text does not report quantitative results. However, it claims that Brain-SAD achieves a higher success rate, shorter task-completion time, and shorter collision-recovery time compared to existing methods, along with stronger reliability across continuous intersections of varying complexity. All comparisons are made against unspecified baselines, which limits the ability to assess the magnitude of these improvements.

Limitations

The authors do not report any limitations in their work. However, the lack of specific baseline comparisons and quantitative metrics may hinder the evaluation of the framework's performance relative to existing methods.

Why it matters

The introduction of dynamic fear-oriented constraints in Brain-SAD has significant implications for the development of safer autonomous driving systems. By enabling vehicles to adapt their behavior based on real-time assessments of risk, this framework could lead to more robust and reliable autonomous driving solutions. Future work could explore the integration of this approach into existing CRL frameworks and its applicability in diverse driving environments.