The Comfort of the Soft-Safety Driver Alerts: Measurements and Evaluation

Abstract

With the development of automated driving systems and V2I (vehicle-to-infrastructure) communications, soft-safety driver alerts can be implemented to supplement imminent driver alerts. This type of alert improves drivers’ situational awareness of emerging risks over a more extended period with more detailed incident information and longer response time. However, compared to the large number of studies focusing on imminent risks, there are insufficient studies on the evaluation and effectiveness of soft-safety alerts. This study proposed an innovative metric to assess the comfort and safety of soft-safety driver alerts by constructing an ideal speed profile and calculating the deviation between the actual and ideal profile. We select the highway end-of-queue event as the experiment scenario, which is a leading cause of fatal highway crashes. Human subjects’ experiments are conducted in the driving simulator to validate the proposed metrics. The results have proved that the proposed metrics have good potential to assess driving comfort objectively. We also found that soft-safety alerts tend to improve driving comfort. However, there is insufficient evidence to conclude statistically about the prototype soft-safety alerts implemented in the experiments.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Zhengming Zhang

Zhengming Zhang is a PhD candidate at the School of Industrial Engineering, Purdue University, West Lafayette. His research interests are human-computer interaction, human factors, and deep learning for intelligent transportation systems.

Renran Tian

Renran Tian is an Assistant Professor at the Indiana University-Purdue University Indianapolis (IUPUI). He received his Ph.D. from the School of Industrial Engineering at Purdue University – West Lafayette in 2013. His research interests include human-centered computing, human-AI teaming, artificial intelligence, cognitive ergonomics, and autonomous driving.

Vincent G. Duffy

Vincent G. Duffy is a Professor of Industrial Engineering and Agricultural & Biological Engineering at Purdue University. He has served as a faculty member at Purdue since 2005 and is a Fellow of the UK Ergonomics Society (CIEHF) Chartered Institute of Ergonomics and Human Factors in the United Kingdom.

Lingxi Li

Lingxi Li is a Professor of Electrical and Computer Engineering at Indiana University-Purdue University Indianapolis. Dr. Li’s research focuses on connected and automated vehicles, intelligent transportation systems, and human-machine interaction. He has authored/co-authored one book and over 130 research articles in refereed journals and conferences.