Enhancing Driver Safety in the Era of Automation

The automotive industry is rapidly evolving, transitioning from traditional mechanical systems to advanced, automated technologies. As vehicles become increasingly autonomous, the role of the driver is shifting from active control to passive supervision, which introduces new safety challenges. One critical area in ensuring road safety is the development of Driver Monitoring Systems (DMS), which are designed to detect driver states such as fatigue, distraction, and inattention. Despite the progress made in this field, existing DMS technologies still face several limitations, particularly in terms of real-time accuracy, reliability, and privacy concerns related to the collection of sensible data. This thesis examines the evolution of automotive technologies with a specific focus on Driver Monitoring Systems. It highlights the increasing need for predictive and efficient DMS due to the rise of vehicle automation. The research begins by tracing the historical development of vehicles using the Abernathy-Utterback model, showcasing the transition from the fluid phase to the highly specialized systems of today. Additionally, it explores the technological foundation of DMS, including face and eye-tracking cameras, behavioral and bio-metric sensors, and artificial intelligence (AI) algorithms, which are integral to assessing driver engagement and attention. Through a series of interviews with potential users, this research investigates current perceptions, benefits, and concerns associated with DMS. Key findings obtained from the thematic analysis used, indicate that while there is a broad acknowledgment of the safety benefits provided by DMS, privacy and data security remain significant barriers to wider adoption. Furthermore, the interviews reveal a desire for systems that are not only more accurate, but also transparent in their operations and data management. The thesis concludes by discussing the future prospects for DMS, emphasizing the need for innovation in AI-driven prediction models, better integration with vehicle systems, stronger regulatory frameworks to address privacy concerns, and the possibility of implementing comfort features like automatic personalized settings adjustment trough face recognition to balance the discomfort created by the previously cited problems. Suggestions for future development could include enhancing the precision of DMS technologies, exploring non-invasive monitoring methods, improving the public’s trust through clearer communication of data usage policies, and integrating safety monitoring functions with features that add tangible value to the driver’s experience. In summary, this thesis underscores the critical role of DMS in the next generation of vehicles, particularly as the industry moves towards higher levels of automation. By addressing the technical challenges and ethical concerns surrounding these systems, DMS can play a pivotal role in enhancing driver and road safety, shaping the future of automotive technologies.