Brain Sad Brain Inspired Safe Autonomous Driving

---
name: brain-sad-brain-inspired-safe-autonomous-driving
description: Brain-inspired safe autonomous driving framework with dynamic fear-oriented constraints on dual-policy for constrained reinforcement learning
version: 1.0.0
tags: [cs.AI, cs.CV, cs.NE, cs.RO, safe-autonomous-driving, constrained-rl, brain-inspired]
source: arxiv
arxiv_id: 2609.38016v1
utility: 0.98
---

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

**Authors:** Huan Rong, Chao Yin, Anouar Imel
**Published:** 2026-09-29
**Categories:** cs.AI, cs.CV, cs.NE, cs.RO
**arXiv:** https://arxiv.org/abs/2609.38016v1

## Summary

Constrained Reinforcement Learning has recently gained increasing attention in the field of Safe Autonomous Driving, where the general mechanism is to maximize the expected reward while keeping the overall action risk bounded. In this way, the safety issues arising in AD can be mitigated through constrained actions. However, existing Constrained RL methods still lack dynamics on the imposed constraints. For instance, the action cost adopted by the existing Primal-Dual/soft-constrained methods is static and doesn't adapt to changing driving scenarios. This paper proposes a brain-inspired approach that introduces dynamic fear-oriented constraints that adapt to the driving context, enabling safer and more responsive autonomous driving control.

## Key Contributions

- Introduces dynamic fear-oriented constraints inspired by brain mechanisms for safe autonomous driving
- Addresses the limitation of static action costs in existing Primal-Dual/soft-constrained methods
- Proposes a dual-policy framework that adapts constraints based on driving scenarios
- Provides a more responsive and context-aware safety mechanism for autonomous vehicles

## Relevance

This paper is highly useful for researchers and practitioners working on safe autonomous driving systems. It addresses a critical gap in constrained reinforcement learning by introducing dynamic, adaptive constraints that better reflect real-world driving scenarios. The brain-inspired approach offers a novel perspective on safety that could lead to more robust and human-like decision-making in autonomous vehicles.