Despite the continual advances in Advanced Driver Assistance Systems (ADAS) and the development of high-level autonomous vehicles (AV), there is a general consensus that for the short to medium term, there is a requirement for a human supervisor to handle the edge cases that inevitably arise. Given this requirement, it is essential that the state of the vehicle operator is monitored to ensure they are contributing to the vehicle's safe operation. This paper introduces a dual-source approach integrating data from an infrared camera facing the vehicle operator and vehicle perception systems to produce a metric for driver alertness in order to promote and ensure safe operator behaviour. The infrared camera detects the driver's head, enabling the calculation of head orientation, which is relevant as the head typically moves according to the individual's focus of attention. By incorporating environmental data from the perception system, it becomes possible to determine whether the vehicle operator observes objects in the surroundings. Experiments were conducted using data collected in Sydney, Australia, simulating AV operations in an urban environment. Our results demonstrate that the proposed system effectively determines a metric for the attention levels of the vehicle operator, enabling interventions such as warnings or reducing autonomous functionality as appropriate. This comprehensive solution shows promise in contributing to ADAS and AVs' overall safety and efficiency in a real-world setting.

翻译：尽管高级驾驶员辅助系统（ADAS）持续进步，且高级自动驾驶车辆（AV）不断发展，但业界普遍认为，在短期至中期内，仍需人类监督者处理不可避免的边界情况。基于这一需求，必须监控车辆操作者的状态，以确保其对车辆安全运行有所贡献。本文提出一种双源方法，整合面向车辆操作者的红外摄像头数据与车辆感知系统信息，生成驾驶员警觉性指标，从而促进并保障操作者的安全行为。红外摄像头可检测驾驶员头部，进而计算头部朝向——由于头部通常会随个体注意力焦点移动，此参数至关重要。通过融入感知系统提供的环境数据，可判断车辆操作者是否观察到周围物体。实验采用在澳大利亚悉尼市采集的数据，模拟城市环境下的自动驾驶运行场景。结果表明，所提出的系统能有效确定车辆操作者注意力水平的指标，从而实现适时干预，如发出警告或适当缩减自动驾驶功能。这一综合性解决方案有望在实际场景中提升ADAS及自动驾驶车辆的整体安全性与运行效率。