Noncontact Driver Attentiveness Detection System

: (Alternative Format Thesis)

Abstract

Road safety has been a persistent challenge in the mobility sector for over a century, with road traffic crashes (RTCs) accounting for significant numbers of fatalities and injuries worldwide each year. Beyond the devastating human toll, RTCs impose a substantial socioeconomic burden on individuals, their families, and national infrastructure through vehicle damage, medical expenses, and the loss of productivity. A substantial body of research indicates that human factors such as fatigue and distraction are primary contributors to these incidents, prompting a growing interest in the development of Driver Monitoring Systems (DMS) aimed at improving driver attentiveness and road safety.

Incumbent DMS technologies predominantly rely on vehicle dynamics and observable driver behaviours, often captured internally from the vehicle itself or externally via an in-cabin camera. Despite their reasonable effectiveness in driver distraction detection, these systems typically lack the capability to directly measure a driver’s physiological state – a factor that is proven to be strongly correlated with fatigue, resulting in limited robustness and accuracy when it comes to driver fatigue detection, particularly under real-world driving conditions. Hence, real-time monitoring of physiological data has the potential to improve driver fatigue detection and reduce the resultant accidents. However, conventional methods of physiological measurements often require body-attached electrodes and are designed for controlled, quasi-static scenarios, contrasting to the noisy and highly dynamic driving environments. Moreover, the use of body-attached electrodes will further introduce distractions and restrict body movements, all making them impractical for integration into real-world DMS applications.

To address this gap, this thesis focuses on the development of the noncontact driver attentiveness detection system by advancing noncontact driver physiological measurements techniques under driving conditions, ultimately contributing to the unresolved fatigue detection challenge in the current DMS regime. Noncontact physiological sensing in real-world driving conditions presents significant challenges due to factors such as the variable illumination, motion artefacts, and environmental interference found in vehicular environments. The work begins with a comparative study of the two leading noncontact modalities in this field – computer vision and radar – for heart rate monitoring, highlighting the characteristics of each modality, and identifying radar as a more robust alternative under realistic driving scenarios. On this basis, the thesis then explores the use of Frequency Modulated Continuous Wave (FMCW) radar, which is the most promising radar type in vehicular sensing, for noncontact driver cardiorespiratory monitoring, analysing the key challenges such as motion cancellation trade-offs, phase ambiguity, and radar cross-section (RCS) variability restricting its practical implementation and the corresponding reasons through implementation of state-of-the-art techniques. To address some of these issues, a novel noncontact cardiorespiratory monitoring system for drivers using FMCW radar is proposed, incorporating a phase continuity tracking algorithm and a signal quality index, enabling continuous and reliable extraction of physiological signals for drivers even under typical driving movements and environmental constraints. Recognising driver behaviour represent the state-of-the-art indicators for driver fatigue detection, the final part of the thesis compares the two modalities and complements the newly enabled physiological measurements with behavioural analysis, providing insights for multimodal DMS development.

Overall, this research addresses the critical challenges for driver fatigue detection in existing DMS technologies by enabling accurate, unobstructive, and real-time physiological measurements and linking them to existing behavioural domains. The work presented provides a foundation for next-generation DMS, thereby contributing to the long-standing road safety challenge.

Date of Award	18 Feb 2026
Original language	English
Awarding Institution	University of Bath
Supervisor	Adrian Evans (Supervisor), Robert Watson (Supervisor) & Benjamin Metcalfe (Supervisor)