Drowsiness-Aware Adaptive Autonomous Braking System based on Deep Reinforcement Learning for Enhanced Road Safety

TL;DR

This paper introduces a deep reinforcement learning-based autonomous braking system that detects driver drowsiness from ECG signals and adapts braking actions to improve road safety, validated in a high-fidelity simulation.

Contribution

It presents a novel physiology-aware reinforcement learning approach integrating ECG-based drowsiness detection into adaptive braking control.

Findings

Achieved 99.99% success rate in collision avoidance in simulation.

Demonstrated effective drowsiness detection using RNN on ECG signals.

Enhanced safety by incorporating physiological data into autonomous driving systems.

Abstract

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.