用深度强化学习让刹车系统感知司机困倦，提升安全响应能力
Drowsiness-Aware Adaptive Autonomous Braking System based on Deep Reinforcement Learning for Enhanced Road Safety
- 结合心电图与车辆动态，用强化学习建模困倦导致的反应延迟
- 在模拟环境中碰撞规避成功率高达99.99%，无论是否困倦
- 适合智能驾驶、车载安全系统研发人员参考
司机困倦会显著影响对安全制动距离的判断，据估计占欧洲道路事故的10%-20%。传统辅助系统无法适应实时生理状态变化。本文提出一种基于深度强化学习的自主刹车系统，融合车辆动力学与驾驶员生理数据。通过循环神经网络（RNN）从心电图（ECG）信号中检测困倦状态，基于2分钟窗口的多种分割与重叠配置进行广泛基准测试。将推断出的困倦状态纳入双双重致郁深度Q网络（Double-Dueling DQN）代理的可观测状态空间，将司机能力下降建模为动作延迟。系统在高保真CARLA仿真环境中实现并评估。实验结果表明，所提代理在困倦与非困倦条件下均实现了99.99%的碰撞规避成功率。研究证明了生理感知控制策略在增强自适应智能驾驶安全系统中的有效性。
原文摘要 · Abstract (English)
Driver drowsiness significantly impairs the ability to accurately judge safe braking distances and is estimated to contribute to 10%-20% of road accidents in Europe. Traditional driver-assistance systems lack adaptability to real-time physiological states such as drowsiness. This paper proposes a deep reinforcement learning-based autonomous braking system that integrates vehicle dynamics with driver physiological data. Drowsiness is detected from ECG signals using a Recurrent Neural Network (RNN), selected through an extensive benchmark analysis of 2-minute windows with varying segmentation and overlap configurations. The inferred drowsiness state is incorporated into the observable state space of a Double-Dueling Deep Q-Network (DQN) agent, where driver impairment is modeled as an action delay. The system is implemented and evaluated in a high-fidelity CARLA simulation environment. Experimental results show that the proposed agent achieves a 99.99% success rate in avoiding collisions under both drowsy and non-drowsy conditions. These findings demonstrate the effectiveness of physiology-aware control strategies for enhancing adaptive and intelligent driving safety systems.
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