Drivers want to retain a sense of control when driving (partially) automated vehicles (AVs). Future AVs will continue to offer the possibility to drive manually, potentially leading to challenging driver-initiated takeovers (DITs) due to the "out-of-the-loop problem" and reduced driving performance. A driving simulator study (N=24) was conducted to explore whether cooperative systems, without full control of driving tasks, provide a sense of control to mitigate DITs in varying conflict situations. Conflict levels were operationalized by an AV performing overtaking maneuvers under free, 100m, and 50m visibility on a two-lane rural road. Participants experienced three systems: no intervention-, a cooperative choice-, and a manual control system. Results showed that participants had a similar sense of control with the cooperative system compared to the manual one and preferred it over the manual system. The likelihood of DITs increased with conflict intensity, and trust in the AV moderated the conflict-DIT association.

Automated vehicles are about to enter the mass market. However, such systems regularly meet limitations of varying criticality. Even basic tasks such as Object Identification can be challenging, for example, under bad weather or lighting conditions or for (partially) occluded objects. One common approach is to shift control to manual driving in such circumstances, however, post-automation effects can occur in these control transitions. Therefore, we present ORIAS, a system capable of asking the driver to (1) identify/label unrecognized objects or to (2) select an appropriate action to be automatically executed. ORIAS extends the automation capabilities, prevents unnecessary takeovers, and thus reduces post-automation effects. This work defines the capabilities and limitations of ORIAS and presents the results of a study in a driving simulator (N=20). Results indicate high usability and input correctness.

In (partially) automated vehicles, users will sometimes have to take over control due to system failure or reach of an operational driving domain end. In such scenarios, the user becomes the driver and has to quickly gain situational awareness. With advanced sensory, an automated vehicle could aid the user in building situational awareness by providing information. A literature analysis found no commonly conveyed information. Therefore, to evaluate the effects of four different abstraction levels (high, medium-high, medium, and low) and used modalities (visual vs. visual+auditory) on situational awareness and accompanying usability scores, we evaluated eight systems and a baseline without information display. In the between-subjects online monitor-based study (N=225), we found that while subjective measures are higher and a warning is required, providing abstract information does not improve objective situational awareness, and only being provided with visual information was preferred.

Automated vehicles will eventually operate safely without the need of human supervision and fallback, nevertheless, scenarios will remain that are managed more efficiently by a human driver. A common approach to overcome such weaknesses is to shift control to the driver. Control transitions are challenging due to human factor issues like post-automation behavior changes. We thus investigated cooperative overtaking wherein driver and vehicle complement each other: drivers support the vehicle to perceive the traffic scene and decide when to execute a maneuver whereas the system steers. We explored two maneuver approval and cancel techniques on touchscreens, and show that cooperative overtaking is feasible, both interaction techniques provide good usability and were preferred over manual maneuver execution. However, participants disregarded rear traffic in more complex situations. Consequently, system weaknesses can be overcome with cooperation, but drivers should be assisted by an adaptive system.