Gengqian received the Bachelor's degree in Telecommunication Engineering from Zhengzhou University, China, in 2019, the MSc degree with distinction in Robotics and Autonomous Systems from the University of Bath, UK, in 2020.During the final year, he went for a placement as a hardware engineer in a company. In his spare time, he enjoys movies, music, and hiking. Having a drink with several friends in a pub is also one of his favourites.
I joined the CDT with an interest of investigating the driver and user behaviour by developing the driver monitoring system. Hope my knowledge in electronics, AI and telecommunication can actually help us find a feasible approach to drive in a safer way.
Data from the World Health Organisation (WHO) shows approximately 1.3 million people die annually from road crashes, which are identified as the leading cause of death for children and young adults. In the UK, there were 24,530 people killed or seriously injured in 2021 according to the estimation of the Department for Transport (DfT). Besides concerns on the road safety aspect, road traffic crashes cost most countries 3% of their gross domestic product, leading to considerable financial loss to individuals, their families, and the entire nation. Meanwhile, various studies prove that human error was the sole factor in more than 50% of road accidents, and was a contributing factor in over 90%. Commonly seen human errors such as drowsy driving, distracted driving, and chemical impairment caused by alcohol or drugs form part of today’s road traffic system, threatening everyone’s life safety. However, the current development in autonomous driving can’t fully mitigate this issue since the takeover by a human driver is still needed before the SAE level 5 is reached, which is decades away. Propelled by societal pressure and legislation, Driver Monitoring System (DMS) was introduced by car manufacturers to tackle this long-existing problem, combining driver behaviour obtained from a camera and driving behaviour from the vehicle itself to determine the driver’s state. Despite the effectiveness of existing commercial systems, the lack of direct measurement remains a challenge to further improve the accuracy. On the other hand, the feasibility of extracting physiological information such as vital signs based on non-contact approaches in the lab environment has been proven.
Therefore, the focus of this project is the development of a novel non-contact driver monitoring system for attentiveness detection via radar, camera, or ultrasonic sensors. Firstly, physiological information is obtained by signal processing and then compared with the ground truth from body-attached sensors to develop a robust non-contact vital sign monitoring system. On this basis, extracted features such as heart rate, respiratory rate, skin temperature, and body movements are combined with observations from real-world driving experiments and brain activity measured by EEG to develop a new model of driver attentiveness. For example, a reduction in heart rate, respiratory rate, or blink rate could be good indicators of low attentiveness.
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