Classification of Safety Driver Attention During Autonomous Vehicle
  Operation
Santiago Gerling Konrad Julie Stephany Berrio Mao Shan Favio Masson Stewart Worrall
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.