Optimizing followers’ car-following behaviors using rear-facing eHMIs

Rear-end collisions are closely related to car-following (CF) behaviors and account for a large proportion of road crashes. Existing countermeasures mainly enhance drivers' perception of the direct leading vehicle; however, growing evidence suggests that CF decisions rely on information beyond the direct leading vehicle. Thus, expanding drivers' perceptual range by providing information of the indirect leading vehicle (i.e., vehicle ahead of the direct leading vehicle) may enhance CF safety. Unlike in-vehicle human-machine interfaces (HMIs) that rely on vehicle-to-vehicle (V2V) communications, external HMIs (eHMIs) without relying on V2V technologies are more feasible at this stage. However, no research has explored whether eHMIs can improve CF safety. Thus, four rear-facing eHMIs providing information regarding the indirect leading vehicle in CF events were designed, including Brake-eHMI showing only brake action of the indirect leading vehicle, Distance-eHMI and Headway-eHMI showing the relative distance and time headway between the indirect leading vehicle and direct leading vehicle, and Video-eHMI showing the live-stream video ahead of the direct leading vehicle. A field experiment with 30 participants was conducted to evaluate the impact of eHMIs on driving safety and efficiency in CF events. We found that, in general, indirect leading vehicle information could improve CF safety in chain-braking events by enabling quicker brake responses and increasing minimum time-to-collision, without overloading drivers in CF events. This research provides insights into the design of innovative vehicle systems that leverage smart vehicles' perception capabilities to enhance driving safety.

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Publication Details

Journal
Accident Analysis & Prevention
Published
2026-03-07
DOI
https://doi.org/10.1016/j.aap.2026.108475
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
0.00
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article

Optimizing followers’ car-following behaviors using rear-facing eHMIs

Z. D. Wang, Zhenyu Wang, Jiahui Li, Yufan Chen et al.
Accident Analysis & Prevention
Human-Automation Interaction and Safety
article

Optimizing followers’ car-following behaviors using rear-facing eHMIs

Z. D. Wang, Zhenyu Wang, Jiahui Li, Yufan Chen, Feiqi Gu, Dengbo He, Hongling Sheng
article en

Abstract

Rear-end collisions are closely related to car-following (CF) behaviors and account for a large proportion of road crashes. Existing countermeasures mainly enhance drivers' perception of the direct leading vehicle; however, growing evidence suggests that CF decisions rely on information beyond the direct leading vehicle. Thus, expanding drivers' perceptual range by providing information of the indirect leading vehicle (i.e., vehicle ahead of the direct leading vehicle) may enhance CF safety. Unlike in-vehicle human-machine interfaces (HMIs) that rely on vehicle-to-vehicle (V2V) communications, external HMIs (eHMIs) without relying on V2V technologies are more feasible at this stage. However, no research has explored whether eHMIs can improve CF safety. Thus, four rear-facing eHMIs providing information regarding the indirect leading vehicle in CF events were designed, including Brake-eHMI showing only brake action of the indirect leading vehicle, Distance-eHMI and Headway-eHMI showing the relative distance and time headway between the indirect leading vehicle and direct leading vehicle, and Video-eHMI showing the live-stream video ahead of the direct leading vehicle. A field experiment with 30 participants was conducted to evaluate the impact of eHMIs on driving safety and efficiency in CF events. We found that, in general, indirect leading vehicle information could improve CF safety in chain-braking events by enabling quicker brake responses and increasing minimum time-to-collision, without overloading drivers in CF events. This research provides insights into the design of innovative vehicle systems that leverage smart vehicles' perception capabilities to enhance driving safety.

Accident Analysis & PreventionVol. 231
Hong Kong University of Science and Technology (HK), Shanghai Innovative Research Center of Traditional Chinese Medicine (CN), Guangzhou University (CN), University of Hong Kong (HK), South China University of Technology (CN)
Openalex Percentile: Top 39%
Human-Automation Interaction and Safety
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