As AI advances, and can draw upon previously disparate information sources, automated vehicles are gaining the ability to proactively collaborate with humans to accomplish everyday tasks. This collaboration will be based on increased sensing and information and will feature greater proactive behavior by the vehicle. However, there is little guidance on how vehicles should behave proactively, or even a shared understanding of what that means. Using prior work, we develop a framework of proactive interaction comprising three dimensions: initiation of action, alignment with users’ goals, and communication of context awareness. We apply the framework in a video-based online study (N=351) of a vehicle navigation system that exhibits proactive behavior. The system advises or acts, supports or counters users’ goals in two scenarios (to reach a work meeting quickly or carry groceries without walking too far), and provides a more or less detailed rationale. We find that when goals are misaligned, communicating contextual awareness decreases user experience and acceptance. As a result, if alignment of (immediate) goals is not possible—such as when drivers want to reduce walking distance but the closest parking lot is full—systems should exercise care in communicating their situation awareness, as this can reinforce users’ negative perceptions. Dispositional traits such as propensity to trust and locus of control do predict users’ trust, distrust, and experience in our scenarios. We also find that participants’ primary rationales for accepting the system’s decision are largely similar (saving time, trusting, and making sense) but rationales overall, especially for rejecting the system’s decision, are more varied than expected and often based on experience. Therefore, systems that look to personalize decision-making—such as whether to proactively execute versus merely suggest an action—could take into account users’ dispositional traits and other preferences, such as those as exemplified by these rationales.

Conditionally automated driving systems face two main safety challenges: the inability to autonomously handle all situations the vehicle encounters, and the allowed inattention of drivers during these critical moments. Our study focuses on enhancing drivers’ situation awareness at such times by embedding information about system status and the road environment in the visual signals displayed when control is transferred from the automated driving system. Six visual signals, each including different levels of situation awareness information, were compared to examine how they influence drivers’ levels of situation awareness in a simulated environment. The results show that signals incorporating higher levels of situation awareness information about the environment significantly facilitate the recovery of situation awareness after engaging in non-driving related tasks. This research provides insights into how visual cues can be optimized to facilitate quicker recovery of situation awareness for drivers transitioning from non-driving tasks in conditionally automated vehicles.

Reading large tables on small mobile screens presents serious usability challenges that can be addressed, in part, by better table formatting. However, there are few evidenced-based guidelines for formatting mobile tables to improve readability. For this work, we first conducted a survey to investigate how people interact with tables on mobile devices and conducted a study with designers to identify which design considerations are most critical. Based on these findings, we designed and conducted three large scale studies with remote crowdworker participants. Across the studies, we analyze over 14,000 trials from 590 participants who each viewed and answered questions about 28 diverse tables rendered in different formats. We find that smaller cell padding and frozen headers lead to faster task completion, and that while zebra striping and row borders do not speed up tasks, they are still subjectively preferred by participants.

In our age of ubiquitous digital displays, adults often read in short, opportunistic interludes. In this context of Interlude Reading , we consider if manipulating font choice can improve adult readers’ reading outcomes. Our studies normalize font size by human perception and use hundreds of crowdsourced participants to provide a foundation for understanding, which fonts people prefer and which fonts make them more effective readers. Participants’ reading speeds (measured in words-per-minute (WPM)) increased by 35% when comparing fastest and slowest fonts without affecting reading comprehension. High WPM variability across fonts suggests that one font does not fit all. We provide font recommendations related to higher reading speed and discuss the need for individuation, allowing digital devices to match their readers’ needs in the moment. We provide recommendations from one of the most significant online reading efforts to date. To complement this, we release our materials and tools with this article.

Automated emergency braking (AEB) systems-which alert a driver to approaching hazards and automatically brake-are currently available in some vehicles and will soon be widespread. Due to the uncertainties inherent in any environment and difficulties in processing sensor data, these systems are prone to both false alarms and system misses of hazards. A pressing design concern is whether to bias these systems toward a higher likelihood of false alarms versus system misses for non-fatal events. We investigated how drivers form mental models of the AEB system and how that influences their reliance on the system during a critical, potentially fatal, failure. In a full vehicle driving simulator, participants experienced nine interactions that reflected the system's level of bias toward false alarms or toward misses for non-fatal events or that demonstrated perfect performance. When a potentially fatal event occurred, participants trained to expect misses were better able to avoid a pedestrian in the road after a detection failure than those using a system with perfect performance or false alarms. These findings suggest that systems biased toward misses in non-fatal events encourage driver vigilance and preparedness for potentially fatal events.

In partially automated driving, rapid transitions of control present a severe hazard. How long does it take a driver to take back control of the vehicle when engaged with other non-driving tasks? In this driving simulator study, we examined the performance of participants (N=30) after an abrupt loss of automated vehicle control. We tested three transition time conditions, with an unstructured transition of control occurring 2s, 5s, or 8s before entering a curve. As participants were occupied with an active secondary task (playing a game on a tablet) while the automated driving mode was enabled, they needed to disengage from the task and regain control of the car when the transition occurred. Few drivers in the 2 second condition were able to safely negotiate the road hazard situation, while the majority of drivers in the 5 or 8 second conditions were able to navigate the hazard situation safely.

With automated driving systems, drivers may still be expected to resume full control of the vehicle. While structured transitions where drivers are given warning are desirable, it is critical to benchmark how drivers perform when transition of control is unstructured and occurs without advanced warning. In this study, we observed how participants (N=27) in a driving simulator performed after they were subjected to an emergency loss of automation. We tested three transition time conditions, with an unstructured transition of vehicle control occurring 2 seconds, 5 seconds, or 8 seconds before the participants encountered a road hazard that required the drivers' intervention. Few drivers in the 2 second condition were able to safely negotiate the road hazard situation, while the majority of drivers in 5 or 8 second conditions were able to navigate the hazard safely. Similarly, drivers in 2 second condition rated the vehicle to be less likeable than drivers in 5 and 8 second conditions. From the study results, we are able to narrow in on a minimum amount of time in which drivers can take over the control of vehicle safely and comfortably from the automated system in the advent of an impending road hazard.

What effect will periods of automated driving will have on driver performance after transfer of control? In our driving simulator experiment (N = 48) participants in four different automation conditions (fully autonomous vehicle, autonomous steering, autonomous speed control, no automation) were evaluated based on their post-transition accident avoidance, situational awareness, and feelings of trust in and comfort with autonomous or partially autonomous driving. Preliminary results from behavioral data show significant differences in time to initiate evasive action across conditions. Participants in the fully autonomous condition showed greater trust and comfort with the car's autonomous features than those in the autonomous speed control condition.