Safety Driver Attention on Autonomous Vehicle Operation Based on Head Pose and Vehicle Perception

Santiago Gerling Konrad, Julie Stephany Berrio, Mao Shan, Favio R. Masson, Eduardo M. Nebot, Stewart Worrall

Abstract: Despite the continual advances in Advanced Driver Assistance Systems (ADAS) and the development of high-level autonomous vehicles (AV), there is a 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, the state of the autonomous vehicle operator (referred to as the safety driver) must be monitored to ensure their contribution to the vehicle's safe operation. This paper introduces a dual-source approach integrating data from an infrared camera facing the safety driver and vehicle perception systems to produce a metric for safety driver alertness to promote and ensure safe operator behaviour. The infrared camera detects the safety 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 safety driver 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 safety driver, enabling interventions such as warnings or reducing autonomous functionality as appropriate. The results indicate reduced awareness on subsequent laps during the study, demonstrating the "automation complacency" phenomenon. This comprehensive solution shows promise in contributing to ADAS and AVs’ overall safety and efficiency in a real-world setting.