3 research outputs found
Driver behaviour characterization using artificial intelligence techniques in level 3 automated vehicle.
Brighton, James L. - Associate SupervisorAutonomous vehicles free drivers from driving and allow them to engage in some
non-driving related activities. However, the engagement in such activities could
reduce their awareness of the driving environment, which could bring a potential
risk for the takeover process in the current automation level of the intelligent
vehicle. Therefore, it is of great importance to monitor the driver's behaviour when
the vehicle is in automated driving mode.
This research aims to develop a computer vision-based driver monitoring system
for autonomous vehicles, which characterises driver behaviour inside the vehicle
cabin by their visual attention and hand movement and proves the feasibility of
using such features to identify the driver's non-driving related activities. This
research further proposes a system, which employs both information to identify
driving related activities and non-driving related activities. A novel deep learning-
based model has been developed for the classification of such activities. A
lightweight model has also been developed for the edge computing device, which
compromises the recognition accuracy but is more suitable for further in-vehicle
applications. The developed models outperform the state-of-the-art methods in
terms of classification accuracy. This research also investigates the impact of the
engagement in non-driving related activities on the takeover process and
proposes a category method to group the activities to improve the extendibility of
the driving monitoring system for unevaluated activities. The finding of this
research is important for the design of the takeover strategy to improve driving
safety during the control transition in Level 3 automated vehicles.PhD in Manufacturin