Driver Profile and Driving Pattern Recognition for Road Safety Assessment: Main Challenges and Future Directions

This study reviews the Artificial Intelligence and Machine Learning approaches developed thus far for driver profile and driving pattern recognition, representing a set of macroscopic and microscopic behaviors respectively, to enhance the understanding of human factors in road safety, and therefore reduce the number of crashes. It provides a...

Parallel autonomy in automated vehicles: Safe motion generation with minimal intervention

Current state-of-the-art vehicle safety systems, such as assistive braking or automatic lane following, are still only able to help in relatively simple driving situations. We introduce a Parallel Autonomy shared-control framework that produces safe trajectories based on human inputs even in much more complex driving scenarios, such as those...

Safe nonlinear trajectory generation for parallel autonomy with a dynamic vehicle model

High-end vehicles are already equipped with safety systems, such as assistive braking and automatic lane following, enhancing vehicle safety. Yet, these current solutions can only help in low-complexity driving situations. In this paper, we introduce a parallel autonomy, or shared control, framework that computes safe trajectories for an...

Eye-based driver state monitor of distraction, drowsiness, and cognitive load for transitions of control in automated driving

Automated driving vehicles of the future will most likely include multiple modes and levels of operation and thus include various transitions of control (ToC) between human and machine. Traditional activation devices (e.g., knobs, switches, buttons, and touchscreens) may be confused by operators among other system setting manipulators and...