Toward Hazard or Action? Effects of Directional Vibrotactile Takeover Requests on Takeover Performance in Automated Driving

Abstract

The vibrotactile modality has great potential for presenting takeover requests (TORs) to get distracted drivers back into the control loop. However, few studies investigate the effectiveness of directional vibrotactile TORs. Whether TORs should be directed toward the direction of hazard (stimulus-response incompatibility) or the direction of avoidance action (stimulus-response compatibility) remains inconclusive. The present study explored the impact of directional vibrotactile TORs (toward-hazard, toward-action, and non-directional) on takeover performance. The influences of TORs lead time (3 s, 4 s, 6 s, and 8 s) and non-driving related tasks (NDRTs) (playing Tetris games and monitoring the road) on the effect of directional TORs were also probed. A total of 48 participants were recruited for our simulated driving study. Results showed that when drivers were engaged in NDRTs during automated driving, directional TORs were more effective than non-directional TORs. Specifically, at the lead times of 6 s and 8 s, both toward-hazard and toward-action TORs could shorten steering response times, compared with the non-directional TORs. At the lead times of 3 s and 4 s, toward-action TORs were more beneficial, as the maximum lateral acceleration was smaller than toward-hazard and non-directional TORs. However, when drivers monitored the road during automated driving, no obvious difference existed between directional and non-directional TORs, regardless of how long the lead time was. The findings in the present study shed light on the design and implementation of the tactile takeover system for automobile designers.

Disclosure statement

The authors declare that they have no conflict of interest.

Additional information

Funding

We appreciate the support from National Natural Science Foundation of China with the Main Research Project on Machine Behavior and Human-Machine Collaborated Decision Making Methodology [Project Number: 72192820], and the Second Research Project on Human Behavior in Human-Machine Collaboration [Project Number: 72192824].

Notes on contributors

Jinlei Shi

Jinlei Shi is a PhD candidate at the College of Computer Science and Technology, Zhejiang University. His research interests include human-computer interaction, ergonomics, and driving behavior.

Changxu Wu

Changxu Wu is a professor at the Department of Industrial Engineering, Tsinghua University. His research interests include cognitive science and engineering system design, especially modeling human cognition system and human behavior with its applications in system design, improving transportation safety, and promoting human performance in human-machine interaction.

Hanjia Zheng

Hanjia Zheng is an MD student at the College of Computer Science and Technology, Zhejiang University. Her research interests include human-computer interaction, virtual reality, and augmented reality.

Wei Zhang

Wei Zhang is a PhD student at the Department of Industrial Engineering, Tsinghua University. Her research interests include human-computer interaction and automated driving.

Xiyuan Zhang

Xiyuan Zhang is a PhD student at the College of Computer Science and Technology, Zhejiang University. Her research interests include human-computer interaction, designers’ perception, and AI-aided design.

Peng Lu

Peng Lu is currently a PhD in Design student at Politecnico di Milano with a focus on Automotive User Experience Design. His PhD research focus is on exploring Automative UX design Strategies to bridge meaningful human-car emotional relationships given the trends of autonomous and connected vehicle.

Chunlei Chai

Chunlei Chai is a professor at the College of Computer Science and Technology, Zhejiang University. His research interests include human-computer interaction, driving behavior, cultural design, AI-aided design, and intelligent design.