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.
| Comments: | 16 pages, 12 figures. Under review at IEEE Transactions on Intelligent Vehicles |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.13878 [cs.LG] |
| (or arXiv:2604.13878v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.13878 arXiv-issued DOI via DataCite |
From: Hossem Eddine Hafidi [view email]
[v1]
Wed, 15 Apr 2026 13:39:56 UTC (9,593 KB)
[v2]
Thu, 16 Apr 2026 09:51:50 UTC (9,593 KB)
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