Non-contact driver attentiveness monitoring system

世界卫生组织（世卫组织）的数据显示，每年约有130万人死于道路交通事故，这被确定为儿童和年轻人死亡的主要原因。根据英国运输部（DfT）的估计，在英国，2021年有24530人死亡或重伤。除了对道路安全方面的担忧外，道路交通事故给大多数国家造成的损失占其国内生产总值的3%，给个人、家庭和整个国家造成了巨大的经济损失。同时，研究表明，在50%以上的交通事故中，人为失误是唯一因素，而在90%以上的交通事故中，人为失误是一个促成因素。常见的人为失误，如疲劳驾驶、分心驾驶、酒精或药物引起的化学损伤等，构成了当今道路交通状况的一部分，威胁着每个人的生命安全。然而，目前自动驾驶的发展并不能完全缓解这一问题，因为在SAE 5级达到之前，仍然需要人类驾驶员的接管，这需要几十年的时间。在社会压力和立法的推动下，汽车制造商推出了驾驶员监控系统（DMS）来解决这个长期存在的问题，该系统将从摄像头获得的驾驶员行为和车辆本身的驾驶行为相结合，以确定驾驶员的状态。尽管现有的商业系统是有效的，但缺乏直接测量仍然是进一步提高准确性的挑战。另一方面，在实验室环境中基于非接触式方法提取生命体征等生理信息的可行性开辟了一条新的途径。因此，该项目的重点是开发一种新型的非接触式驾驶员监测系统，通过非接触式传感器（如雷达、摄像头或超声波传感器）进行注意力检测。首先，通过信号处理获取人体的生理信息，然后与附着传感器的地面信息进行比较，建立鲁棒的非接触式生命体征监测系统。在此基础上，将提取的心率、呼吸频率、皮肤温度、身体运动等特征与真实驾驶实验的观察结果和脑电图测量的大脑活动相结合，建立驾驶员注意力的新模型。例如，心率、呼吸频率或眨眼频率的降低可能是注意力不集中的良好指标。预计此次研究结果将大大减少因人为失误而导致的交通事故，从而防止死亡和伤害，并减少相应的国家经济损失。从研究角度看，有利于非接触式生命体征监测系统、生物信号处理、驾驶员监测、注意力模型等方面的研究。除了典型的车载驾驶员监控用例外，该系统的变体还具有扩展到其他类似应用场景的潜力，例如航海、航空和航空航天。

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, study has shown 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 condition, 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 already proven feasibility of extracting physiological information such as vital signs based on contactless approaches in the lab environment opens up a new avenue. Therefore, the focus of this project is the development of a novel non-contact driver monitoring system for attentiveness detection via contactless sensors such as 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.The outcome of this research project is expected to significantly reduce the number of road crashes due to human error, thus preventing death, injuries, and the corresponding economical loss to the nation as a whole. From the research perspective, it will benefit the research in the non-contact vital sign monitoring system, bio-signal processing, driver monitoring, and attentiveness model. Besides the typical onboard driver monitoring use case, variants of this system have the potential to be expanded to other similar application scenarios, such as voyage, aviation, and aerospace.