Non-contact driver attentiveness monitoring system

非接触式驾驶员注意力监控系统

• 批准号: 2598331
• 金额: --
• 依托单位: University of Bath
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2598331
• 关键词: Non; contact; driver; attentiveness; monitoring

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.

世界卫生组织 (WHO) 的数据显示，每年约有 130 万人死于道路交通事故，道路交通事故被认为是儿童和年轻人死亡的主要原因。据英国交通部（DfT）估计，2021年英国有24,530人死亡或重伤。除了对道路安全方面的担忧外，道路交通事故造成大多数国家国内生产总值 (GDP) 3% 的损失，给个人、家庭和整个国家带来巨大的经济损失。同时，研究表明，人为错误是导致 50% 以上道路事故的唯一因素，并且是 90% 以上道路事故的促成因素。疲劳驾驶、分心驾驶、酒精或药物引起的化学损伤等常见的人为错误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个问题，因为距离达到 SAE 5 级（距离达到 SAE 5 级）还有几十年的时间，仍然需要由人类驾驶员接管。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，结合摄像头获取的驾驶员行为和车辆本身的驾驶行为来确定驾驶员的状态。尽管现有商业系统有效，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已经被证明是可行的，这开辟了一条新途径。因此，该项目的重点是开发一种新型非接触式驾驶员监控系统，通过雷达、摄像头或超声波传感器等非接触式传感器进行注意力检测。首先，通过信号处理获得生理信息，然后与身体附着传感器的地面实况进行比较，以开发强大的非接触式生命体征监测系统。在此基础上，提取的心率、呼吸频率、皮肤温度和身体运动等特征与现实世界驾驶实验的观察结果和脑电图测量的大脑活动相结合，开发了一种新的驾驶员注意力模型。例如，心率、呼吸频率或眨眼频率的降低可能是注意力低下的良好指标。该研究项目的成果预计将显着减少因人为错误造成的道路交通事故的数量，从而防止整个国家的死亡、受伤和相应的经济损失。从研究角度来看，将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之外，该系统的变体还有可能扩展到其他类似的应用场景，例如航行、航空和航天。