Enhancing safety in conditionally automated driving: Can more takeover request visual information make a difference in hazard scenarios with varied hazard visibility?

Autonomous driving technology has the potential to significantly reduce the number of traffic accidents. However, before achieving full automation, drivers still need to take control of the vehicle in complex and diverse scenarios that the autonomous driving system cannot handle. Therefore, appropriate takeover request (TOR) designs are necessary to enhance takeover performance and driving safety. This study focuses on takeover tasks in hazard scenarios with varied hazard visibility, which can be categorized as overt hazards and covert hazards. Through ergonomic experiments, the impact of TOR interface visual information, including takeover warning, hazard direction, and time to collision, on takeover performance is investigated, and specific analyses are conducted using eye-tracking data. The following conclusions are drawn from the experiments: (1) The visibility of hazards significantly affects takeover performance. (2) Providing more TOR visual information in hazards with different visibility has varying effects on drivers' visual attention allocation but can improve takeover performance. (3) More TOR visual information helps reduce takeover workload and increase human-machine trust. Based on these findings, this paper proposes the following TOR visual interface design strategies: (1) In overt hazard scenarios, only takeover warning is necessary, as additional visual information may distract drivers' attention. (2) In covert hazard scenarios, the TOR visual interface should better assist drivers in understanding the current hazard situation by providing information on hazard direction and time to collision to enhance takeover performance.

Improving Eye-Computer Interaction Interface Design: Ergonomic Investigations of the Optimum Target Size and Gaze-triggering Dwell Time.

Interactive feedback of interface elements and low level of spatial accuracy are two main key points for the interaction research in the Eye-computer interaction system. This study tried to solve these two problems from the perspective of human-computer interactions and ergonomics. Two experiments were conducted to explore the optimum target size and gaze-triggering dwell time of the eye-computer interaction (ECI) system. Experimental Series 1 was used as the pre-experiment to identify the size that has a greater task completion rate. Experimental Series 2 was used as the main experiment to investigate the optimum gaze-triggering dwell time by using a comprehensive evaluation of the task completion rate, reaction time, and NASA-TLX (Task Load Index). In Experimental Series 1, the optimal element size was determined to be 256 × 256p x 2. The conclusion of Experimental Series 2 was that when the dwell time is set to 600 ms, the efficiency of the interface is the highest, and the task load of subjects is minimal as well. Finally, the results of Experiment Series 1 and 2 have positive effects on improving the usability of the interface. The optimal control size and the optimal dwell time obtained from the experiments have certain reference and application value for interface design and software development of the ECI system.