The effect of information from dash-based human-machine interfaces on drivers' gaze patterns and lane-change manoeuvres after conditionally automated driving

Investigating looming tactile takeover requests with various levels of urgency in automated vehicles

Designing an effective takeover request (TOR) in conditionally automated vehicles is crucial to ensure driving safety when the system reaches its limit. In our study, we aimed to investigate the effects of looming tactile TORs (whose urgency is dynamically mapped to the situation's criticality as the vehicle approaches the upcoming obstacle) on takeover performance and subjective experience compared with conventional non-looming TORs (several tactile pulses with consistent inter-pulse intervals). In addition, the impact of the TOR urgency level (with urgency levels matched or unmatched to the situation's criticality) was considered. A total of 30 participants were recruited for this study. They were first asked to map the urgency of tactile signals to the criticality of takeover situations with various times to collision according to the recorded video clips. The looming TORs were constructed based on these mapping results. Then, a simulated driving experiment, employing a within-subject design, was conducted to explore the effects of the tactile TOR type (looming vs. non-looming) and urgency level (less urgency vs. matched urgency vs. greater urgency) on takeover performance and drivers' subjective experience. The results showed that the looming TOR can lead to a shorter takeover time and less maximum lateral acceleration compared with the non-looming TOR. Drivers also rated the looming TOR as more useful. Therefore, the looming TOR has great application potential for enhancing driving safety in automated vehicles. In addition, we found that as the TOR's level of urgency increased, the takeover time decreased. However, the TOR with an urgency level matched to the situation's criticality received higher usefulness and satisfaction ratings, suggesting that there was an important trade-off between the advantage of high-urgency TORs in speeding up driver responses and its cost of a poor experience. The findings of our study shed some light on the design and implementation of the takeover warning system for related practitioners.

Impact of non-driving related tasks while operating automated driving systems (ADS): A systematic review

Automated Driving Systems (ADS) (SAE, 2021), promise improved safety and comfort for drivers. Current technological advances have resulted in increased automation capabilities. However, with the increase in automation capabilities, there is a shift in how drivers interact with their vehicles. Drivers can now temporarily hand over the control of the driving task to ADS under certain conditions. However, with ADS in temporary control of the vehicle, drivers may choose to engage in non-driving related tasks (NDRT). The current capabilities of ADS do not allow drivers to hand over control of the driving task indefinitely. Drivers must remain aware and be ready to take back control if necessary. There is a need to better understand drivers' performance and behaviors when driving with ADS, especially when engaged in NDRTs. This literature review, therefore, aims to understand the state of knowledge on automated vehicle systems and driver distraction. This review was conducted as per PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies found a significant increase in takeover times while engaging in NDRTs and driving with automation active. Studies also discuss a change in driver's visual attention, with more focus given to NDRTs as compared to the front roadway. The concerning effects of increasing reaction times and decreases in visual attention can be mitigated by using interventions and studies have had success in redirecting drivers attention and reorient them to the task of driving. The review, therefore, includes a discussion of ADS and NDRT engagement and its impact on driving behaviors such as take-over times, visual attention, trust, and workload. Implications on driver safety and performance are discussed in light of this synthesis.

What Attracts the Driver’s Eye? Attention as a Function of Task and Events

This study explores how drivers of an automated vehicle distribute their attention as a function of environmental events and driving task instructions. Twenty participants were asked to monitor pre-recorded videos of a simulated driving trip while their eye movements were recorded using an eye-tracker. The results showed that eye movements are strongly situation-dependent, with areas of interest (windshield, mirrors, and dashboard) attracting attention when events (e.g., passing vehicles) occurred in those areas. Furthermore, the task instructions provided to participants (i.e., speed monitoring or hazard monitoring) affected their attention distribution in an interpretable manner. It is concluded that eye movements while supervising an automated vehicle are strongly ‘top-down’, i.e., based on an expected value. The results are discussed in the context of the development of driver availability monitoring systems.