The implication of non-driving activities on situation awareness and take-over performance in level 3 automation

The driver's take-over performance is of great importance for driving safety in conditionally automated driving since the driver is required to respond appropriately to control the vehicle if there is a system failure. The engagement of different non-driving activities (NDAs), considered as the main factor of the driver's take-over performance has been investigated in this study from both perspectives of the driver's situation awareness and take-over quality. The activities are divided into 2 groups, which are active interaction mode and passive interaction mode based on the engagement of human and object. The results suggest that the engagement of NDAs could reduce the driver's situation awareness. Driver's attention level is different for each activity. Particularly, active interaction mode NDAs requests more mentally demanding and drivers are not sensitive to the driving situation change when they are doing such activities. In addition, there is no significant difference in the maximum lateral error with NDAs engagement. However, it takes more time to achieve a safe control transition for drivers who are doing the NDAs. The active interaction mode NDAs request even more time. Moreover, the transition process could benefit from steering wheel haptic feedback torque, which can be considered as an effective take-over assistance system

Automatisierte kooperative Transition einer Regelungsaufgabe zwischen Mensch und Maschine am Beispiel des hochautomatisierten Fahrens

In dieser Arbeit wird ein auf Haptic-Shared-Control basierendes Konzept vorgeschlagen, welches Menschen bei der Übernahme einer Aufgabe von einer Automation, wie beispielsweise am Ende einer hochautomatisierten Fahrt, unterstützt. Die Beschreibung der Interaktion durch zeitvariante Differentialspiele erlaubt einen modellbasierten Reglerentwurf sowie die Schätzung der aktuellen Bereitschaft des Menschen. Der Nachweis von performanteren und sicheren Transitionen erfolgt in mehreren Experimenten

Automatisierte kooperative Transition einer Regelungsaufgabe zwischen Mensch und Maschine am Beispiel des hochautomatisierten Fahrens

Diese Arbeit befasst sich mit der Frage, wie ein Transfer einer Aufgabe zwischen Mensch und Maschine gestaltet werden kann, um die Qualität und Sicherheit während des gesamten Übergabeprozesses zu gewährleisten. Dazu wird eine Beschreibung der Mensch-Maschine-Interaktion mit Hilfe von zeitvarianten Differentialspielen vorgeschlagen, welche einen modellbasierten Reglerentwurf ermöglicht. Durch haptische Interaktion am Stellglied wird der Mensch optimal unterstützt und sicher an die Aufgabe heranführt. Darüber hinaus wurde in dieser Arbeit eine Methodik entwickelt, um die aktuelle Bereitschaft des Menschen zur Laufzeit zu schätzen und den Transitionsprozess entsprechend anzupassen. Der Anwendung der Methoden auf der Übernahme der Fahraufgabe nach einer hochautomatisierten Fahrt resultiert im Gegensatz zum Stand der Technik in sicheren und kontrollierten Fahrmänövern

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