Taking Over Control From Highly Automated Vehicles in Complex Traffic Situations The Role of Traffic Density

• Published in: Human factors Vol. 58; no. 4; pp. 642 - 652
• Main Authors: Gold, Christian, Körber, Moritz, Lechner, David, Bengler, Klaus
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.06.2016; Human Factors and Ergonomics Society
• Subjects: Adult; Automation; Automobile Driving; Collisions; Design; Human factors research; Human-computer interaction; Humans; Man-Machine Systems; Motor Vehicles; Psychomotor Performance - physiology; Traffic; Traffic flow; Visual task performance; vehicle automation; mental workload; driver behavior; autonomous driving; human–automation interaction; phoning while driving
• ISSN: 0018-7208; 1547-8181
• DOI: 10.1177/0018720816634226

Objective: The aim of this study was to quantify the impact of traffic density and verbal tasks on takeover performance in highly automated driving. Background: In highly automated vehicles, the driver has to occasionally take over vehicle control when approaching system limits. To ensure safety, the ability of the driver to regain control of the driving task under various driving situations and different driver states needs to be quantified. Methods: Seventy-two participants experienced takeover situations requiring an evasive maneuver on a three-lane highway with varying traffic density (zero, 10, and 20 vehicles per kilometer). In a between-subjects design, half of the participants were engaged in a verbal 20-Questions Task, representing speaking on the phone while driving in a highly automated vehicle. Results: The presence of traffic in takeover situations led to longer takeover times and worse takeover quality in the form of shorter time to collision and more collisions. The 20-Questions Task did not influence takeover time but seemed to have minor effects on the takeover quality. Conclusions: For the design and evaluation of human–machine interaction in takeover situations of highly automated vehicles, the traffic state seems to play a major role, compared to the driver state, manipulated by the 20-Questions Task. Application: The present results can be used by developers of highly automated systems to appropriately design human–machine interfaces and to assess the driver’s time budget for regaining control.