UTFacultiesETEventsFULLY DIGITAL - NO PUBLIC : PhD Defence Bo Zhang | Taking back the wheel: Transition of control from automated cars and trucks to manual driving

FULLY DIGITAL - NO PUBLIC : PhD Defence Bo Zhang | Taking back the wheel: Transition of control from automated cars and trucks to manual driving

Taking back the wheel: Transition of control from automated cars and trucks to manual driving

Due to the COVID-19 crisis measures the PhD defence of Bo Zhang will take place online.

The PhD defence can be followed by a live stream.

Bo Zhang is a PhD student in the research group Transport Engineering and Management (TEM). Her supervisor is prof.dr. M.H. Martens from the Faculty of Engineering Technology.

Driving automation holds great promise for safer and more efficient road transportation, and is becoming a reality thanks to the rapid advancement of technology. However, before full driving automation arrives, the driver would have to take over control of the vehicle when the system fails or reaches its operational limits, which poses new road safety risks at different stages of development. When the system is less capable and reliable, the driver has to closely monitor the system and take over imminent control when necessary. This challenges humans’ inherent weak point of staying vigilant over a prolonged period of time. When the technology becomes more mature, the driver would be allowed to engage in a wide  range of non-driving tasks, but occasional human interventions still cannot be avoided. How to ensure drivers in various mental and physical states to take over control safely become a major challenge at this stage. A large number of studies have tackled human factors issues related to control transitions, and suggested that no single take-over time budget applies to all drivers in all situations. While an adaptive approach is called for to support individual drivers in taking over control, a better understanding of driver take-over process and the variability between and within drivers is still needed to achieve this goal.

This PhD thesis addresses the challenges stated above and contributes to designing safe and comfortable control transitions to manual. The first part of the thesis presents an exhaustive meta-review of 129 studies that reported mean take-over response times, aiming to provide the state of the art on driver take-over research, and to explore determinants of take-over times on an aggregated level. The meta-review showed that hardly any take-over studies were performed in platooning context. To fill in the research gap, the second part of the thesis presents four empirical driving simulator studies investigating driver take-over performance when leaving highly automated truck and car platoons. The aim was to explore if the specific features of platooning, such as the very short inter-vehicular distance and blocked front view, and the driver categories (professional or non-professional drivers), influence the way drivers take over control. In on-road settings, it is not feasible to always allow sufficient time budgets for drivers in various states to take over safely, neither is it realistic to require prolonged effective driver monitoring. In the third part of the thesis, an innovative design solution was proposed and evaluated that may bridge the gap between automated with a driver out of the loop and completely manual driving.

My PhD thesis provided a comprehensive research on factors influencing driver take-over response times, made an initial contribution to the understanding of driver behaviour during and right after decoupling from a highly automated platoon, and proposed an innovative HMI design to better prepare drivers for potential critical take-overs. The findings suggested that dynamically changing driver states and driving situation predominantly determine the driver’s capability to take over control safely at a specific moment. While a driving automation system that can adapt to individual drivers’ states has potential to increase driving safety during transitions, one should also foresee the limit in providing precise estimations of a specific drivers’ take-over readiness. Multiple approaches need to be combined to manage variability within and between drivers and ensure driving safety at different levels of automation, such as actively communicating with the driver the current status of the vehicle and uncertainties in the driving environment, designing cabin layout to regulate drivers’ non-driving posture and engagement in non-driving activities according to the current task demand, and providing fallback options whenever possible in case the driver cannot respond adequately in takeover.