Public Trust in Autonomous Vehicles at the Intersection of Computer Vision Technology and Human Factors

Highly automated vehicles (AVs) also known as Level 4 vehicles can operate in self-driving mode in most circumstances and the driver is allowed to involve in any sort of non-driving activities, even sleeping. However, the automated driving mode is only expected to perform in limited spatial areas (geofenced) and under specific legislations; therefore, a human driver still has the option of getting ready to resume the driving control, whenever required. This project aims at promoting public trust in automated driving based on scientific investigations and findings on the role of Computer Vision Technology and Human Factors in a smooth technology adaptation and transition. Putting more weights on computer vision and machine learning, the research should investigate whether the driver is ready to safely and swiftly take over the control of the vehicle? what are the major contributing human factors in take over scenarios and to what extent? Under what circumstances the sensors and autonomous mode can perform safer than a human driver and vice-versa? The research may involve driver behaviour monitoring (eye, head pose, and body pose modelling), as well as hazard perception and traffic condition understanding (vehicle, pedestrian and cyclist detection & tracking).