Automation

A Preliminary Investigation of Driver Vigilance in Automated Vehicles in Real Traffic Environments
Author(s):Ruobing Zhao Lamar University; Tianjian Li Lamar University; Yueqing Li Lamar University; Yi Liu Lamar University
Abstract: The study investigated driver vigilance in partially automated vehicles under different traffic density and visibility conditions. Twelve participants “drove” a simulated automated vehicle for 40 min on a two-lane rural highway. Data were aggregated from four different scenarios (2 (Traffic density: Low High) × 2 (Visibility: Clear Foggy). The results showed that the driver's detection accuracy in foggy conditions is significantly higher than in clear conditions. Shorter reaction time was found in low density with clear weather. However the reaction time in high density with clear weather was higher than that in foggy conditions. The findings indicated that vigilance should be a key safety concern in automated driving conditions.

Decision Aiding for Nautical Collision Avoidance: Trust Dependence and Implicit Understanding of the Decision Algorithm
Author(s):Christopher Wickens Colorado State University; Nicholas Fitzgerald Fort Colllins PD; Benjamin Clegg Colorado State University; Cap Smith Dept of Business Administration; Dylan Orth Colorado State University Psychology; Katie Kincaid Colorado State University Psychology
Abstract: Two experiments examined an imperfect automation decision aid for maritime collision avoidance. In Experiment 1 the algorithm was driven purely by safety recommending turning your ship in the direction that produced the greatest separation from a hazard ship. In Experiment 2 the algorithm incorporated two additional factors known to influence ship collision avoidance maneuvers: efficiency and procedural adherence to established “rules of the road”. In both experiments results revealed heavy but not total reliance on the aid. A strong influence of rules of the road was indicated by non-compliance to recommendations contrary to those conventions even as they maximized safety; particularly in Experiment 2 when the rules were incorporated in the algorithm. This illustrates the powerful influence of categorical procedural algorithm elements over continuous quantitative ones in affecting automation compliance. Results also revealed the dissociation between rated trust in and behavioral dependence on decision aiding automation.

Driver Arousal and Workload under Partial Vehicle Automation a Pilot Study
Author(s):Monika Lohani University of Utah; Joel Cooper University of Utah; Gus Erickson University of Utah; Trent Simmons University of Utah; Amy McDonnell University of Utah; Amanda Carriero University of Utah; Kaedyn Crabtree University of Utah; David Strayer University of Utah
Abstract: Semi-automated vehicles (Level-2) provide driving assistance but they still require driver supervision to maintain safe driving. However little is known about potential differences in drivers’ cognitive states during manual vs. Level-2 automated driving. The current study systematically examined the effects of manual and Level-2 driving on drivers’ arousal and workload during on-road driving. No differences between the two driving modes were found for the five outcomes that assessed cognitive arousal and workload (i.e. heart rate root mean square of successive heart period differences EEG alpha power and hit rate and reaction time on a secondary task). A Bayes Factor analysis suggested that there is strong evidence that cognitive arousal and workload during Level-2 driving did not differ from manual driving. These novel and theoretically meaningful findings provide strong evidence of similar cognitive arousal and workload states in Level-2 automation and manual driving.

Driver Logo Sign Detection and Hazard Responses Under Partial Vehicle Automation
Author(s):Jing Feng NCSU; David Kaber University of Florida; Yunmei Liu Department of Industrial and Systems Engineering University of Florida; Christopher Cunningham Institute for Transportation Research and Education; Yulin Deng North Carolina State University; Stephen Cauffman North Carolina State University
Abstract: This study investigates the presentation of service logo information under partially automated driving. Drivers completed simulated drives with adaptive cruise control and lane keeping functions during which they had to detect target logo signs and react to hazards by taking over vehicle control. Driver performance was measured in terms of sign detection rate crash rate and takeover response time. Many factors including information source information load and driver age group were investigated. Our findings support the delivery of service logo information via in-vehicle display under partially automated driving especially when the in-vehicle display occurred simultaneously with the on-road signage. Despite that drivers tend to crash more and take longer to take over during a hazardous event when service logo was present this distraction effect was more apparent when service logo information was only presented via in-vehicle display or on-road signage but not when the information was simultaneously presented on both.

Driving with Robots: Mind Perception and Propensity for Aggressive Driving
Author(s):Karl Nachmann George Mason University; Benjamin Pillot George Mason University; Petrina Pervall George Mason University; Yi-Ching Lee George Mason University; Eva Wiese George Mason University
Abstract: Mind perception or the tendency to ascribe agency (i.e. the ability to plan and act) and experience (i.e. the ability to sense and feel) to others is an important design consideration for human-robot interaction since an agent’s mind status affects how we interact with it and how we interpret its behavior. The current study examines whether observable behaviors of robot-piloted autonomous vehicles are interpreted differently lead to different emotional reactions and trigger different behaviors of the observer as a function of the robot driver’s perceived mind status. We expect that aggressive behavior of robot drivers perceived to be high in agency would be interpreted as more intentional and as such would lead to stronger negative reactions and retaliatory behaviors. Consistent with our expectations the robot driver high in agency was perceived as more intentional and elicited more irritation in participants.

Lights Camera Autonomy! Exploring the Opinions of Older Adults Regarding Autonomous Vehicles Through Enactment
Author(s):Aaron Gluck Clemson University; Earl Huff Clemson University; Mengyuan Zhang Clemson University; Julian Brinkley Clemson University
Abstract: Autonomous vehicles (AV) one of the transportation industry’s biggest innovations of the past few decades bring the promise of safer roads and significantly lower vehicle-related fatalities. While many studies have found largely positive consumer opinions regarding operating and owning such a vehicle older adults (55+) tend to express concerns about the safety and operational risks of a vehicle with unknown capabilities. To investigate how older adults and AVs may interact we conducted an improv-style enactment-based participatory design pilot study. We found that initial concerns about trust and safety can be diminished through training and repetitive successful vehicle operation. Additionally our participants provided insights into the AV design considerations needs and interactions for older adults. These findings add to the collective body of autonomous vehicle research by demonstrating that the needs of this growing population who may benefit significantly from access to AVs should be considered by manufacturers.