There is an existing issue in human-machine interaction, such that drivers of semi-autonomous vehicles are still required to take over control of the vehicle during system limitations. A possible solution may lie in tactile displays, which can present status, direction, and position information while avoiding sensory (e.g., visual and auditory) channels overload to reliably help drivers make timely decisions and execute actions to successfully take over. However, limited work has investigated the effects of meaningful tactile signals on takeover performance. This study synthesizes literature investigating the effects of tactile displays on takeover performance in automated vehicles and conducts a human-subject study to design and test the effects of six meaningful tactile signal types and two pattern durations on drivers’ perception and performance during automated driving. The research team performed a literature review of 18 articles that conducted human-subjects experiments on takeover performance utilizing tactile displays as takeover requests. Takeover performance in these studies were highlighted, such as response times, workload, and accuracy. The team then conducted a human-subject experiment, which included 16 participants that used a driving simulator to present 30 meaningful vibrotactile signals, randomly across four driving sessions measuring for reaction times (RTs), interpretation accuracy, and subjective ratings. Results from the literature suggest that tactile displays can present meaningful vibrotactile patterns via various in-vehicle locations to help improve drivers’ performance during the takeover and can be used to assist in the design of human-machine interfaces (HMI) for automated vehicles. The experiment yielded results illustrating higher urgency patterns were associated with shorter RTs and higher intuitive ratings. Also, pedestrian status and headway reduction signals presented shorter RTs and increased confidence ratings compared to other tactile signal types. Finally, the signal types that yielded the highest accuracy were the surrounding vehicle and navigation signal types. Implications of these findings may lie in informing the design of next-generation in-vehicle HMIs and future human factors studies on human-automation interactions.

Publication Date


Publication Type



Transportation Technology

Digital Object Identifier


MTI Project



Human factors, Driver vehicle interfaces, Intelligent vehicles, Tactile perception, Autonomous vehicle handover


Ergonomics | Industrial Engineering | Transportation