Objective This study evaluates the benefits and costs associated with providing drivers continuous feedback on the limits and behavior of imperfect vehicle control automation. Background In-vehicle automated systems remove drivers from active vehicle control, often at the expense of timely interventions when failures occur. Discrete warnings, as a type of feedback to inform drivers about automated system behavior, fail to keep drivers aware of its proximity to operating limits. Method In a fixed-based simulator, 48 drivers drove using Adaptive Cruise Control (ACC)—a form of control automation that maintains a set speed, or a set headway if the vehicle encounters a slower moving vehicle. A first experiment compared ACC with discrete warnings to ACC with continuous information, which indicated moment-to-moment ACC state relative to its operating limits. Three display conditions, designed to provide non-obtrusive, ecologically-valid information, were evaluated in a second experiment: 1) a visual interface; 2) an auditory interface; and 3) a combined visual-auditory interface. Results Drivers provided with continuous feedback relied more appropriately on ACC than did those with discrete warnings. Continuous feedback increased the frequency of proactive responses to automation failures and improved system understanding. Of the three displays, the combined visual-auditory interface performed the best. Conclusion Continuous feedback helped communicate to drivers the evolving relationship between system performance and operating limits. Application Displays for increasingly automated vehicles should inform about the automation's situation-specific behavior rather than simply alert drivers to failures and/or the need to resume vehicle control in order to promote appropriate understanding and trust.

Vehicle technology naming has the potential to influence drivers' expectations (mental model) of the level of autonomous operation supported by semi-automated technologies that are rapidly becoming available in new vehicles. If divergence exists between expectations and actual design specifications, it may make it harder to develop trust or clear expectations of systems, thus mitigating potential benefits. Alternately, over-trust and misuse due to misunderstanding increase the potential for adverse events. An online survey investigated whether and how names of advanced driver assistance systems (ADAS) and automation features relate to expected automation levels. Systems with "Cruise" in their names were associated with lower levels of automation. "Assist" systems appeared to create confusion between whether the driver is assisting the system or vice versa. Survey findings indicate the importance of vehicle technology naming and its impact in influencing drivers' expectations of responsibility between the driver and system in who performs individual driving functions.

Voice interfaces offer promise in allowing drivers to keep their eyes on-road and hands on-wheel. In relieving visualmanual demand, there is the potential for voice-enabled interfaces to inadvertently shift the burden of load to cognitive resources. Measurement approaches are needed that can identify when and to what extent cognitive load is present during driving. A modified form of the AttenD algorithm was applied to assess the amount of cognitive load present in a set of auditory-vocal task interactions. These tasks were subset from a larger on-road study conducted in the Boston area of driver response during use of an in-vehicle voice system [22]. The modified algorithm differentiated among the set of auditory-vocal tasks examined -- and may be useful to HMI practitioners who are working to develop and evaluate HMIs to support drivers in managing their attention to the road, and in the development of real-time driver attention monitoring systems.

We consider a large dataset of real-world, on-road driving from a 100-car naturalistic study to explore the predictive power of driver glances and, specifically, to answer the following question: what can be predicted about the state of the driver and the state of the driving environment from a 6-second sequence of macro-glances? The context-based nature of such glances allows for application of supervised learning to the problem of vision-based gaze estimation, making it robust, accurate, and reliable in messy, real-world conditions. So, it's valuable to ask whether such macro-glances can be used to infer behavioral, environmental, and demographic variables? We analyze 27 binary classification problems based on these variables. The takeaway is that glance can be used as part of a multi-sensor real-time system to predict radio-tuning, fatigue state, failure to signal, talking, and several environment variables.

This study explores the effects of minor changes in automation level on drivers' engagement in secondary activities. Three levels of automation were tested: manual, semi-autonomous, and fully-autonomous. Potential distractor items were present and participants were instructed they could use them if they felt it was safe. Hand positions and engagement in secondary activities were manually coded. Participants were significantly less likely to engage in a secondary activity in semi-autonomous than fully-autonomous mode. Likewise, they were significantly less likely to use two hands to interact with a secondary activity in semi-autonomous mode than fully-autonomous mode. Gaze classification for each of the driver roles revealed that increasing levels of automation resulted in an increasing percentage of off-road glance durations. These observations suggest that in the event of automation failures, a driver in semi-autonomous driving may be in a somewhat better position to retake control and avoid collisions than during fully autonomous driving.