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Related Experiment Video

Updated: Mar 10, 2026

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Optimizing followers' car-following behaviors using rear-facing eHMIs.

Feiqi Gu1, Yufan Chen1, Zhenyu Wang2

  • 1Thrust of Robotics and Autonomous Systems, Systems Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China.

Accident; Analysis and Prevention
|March 9, 2026
PubMed
Summary
This summary is machine-generated.

Providing drivers with information about the vehicle ahead of the one they are following (indirect leading vehicle) can improve car-following (CF) safety. External human-machine interfaces (eHMIs) effectively deliver this information without vehicle-to-vehicle communication.

Keywords:
Beyond-visual-range informationCar-followingDriver behaviorExternal human-machine interfaceHuman-machine interface

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Area of Science:

  • Road Safety Engineering
  • Human-Computer Interaction
  • Automotive Systems

Background:

  • Rear-end collisions are a major cause of road crashes, often linked to car-following (CF) behaviors.
  • Current safety measures focus on the immediate leading vehicle, but drivers use broader information for CF decisions.
  • External human-machine interfaces (eHMIs) offer a feasible way to provide indirect leading vehicle information without vehicle-to-vehicle (V2V) communication.

Purpose of the Study:

  • To investigate the impact of external human-machine interfaces (eHMIs) on car-following (CF) safety by providing indirect leading vehicle information.
  • To design and evaluate four types of rear-facing eHMIs: Brake-eHMI, Distance-eHMI, Headway-eHMI, and Video-eHMI.
  • To assess if eHMIs can enhance driver response and reduce risks in car-following scenarios.

Main Methods:

  • A field experiment was conducted with 30 participants driving in car-following (CF) events.
  • Four rear-facing eHMIs were designed to convey information about the indirect leading vehicle.
  • Participant driving safety and efficiency were evaluated using the implemented eHMIs.

Main Results:

  • Indirect leading vehicle information, delivered via eHMIs, generally improved car-following (CF) safety during chain-braking events.
  • Drivers demonstrated quicker brake responses and increased minimum time-to-collision.
  • The tested eHMIs did not overload drivers during car-following events.

Conclusions:

  • External human-machine interfaces (eHMIs) can effectively enhance car-following (CF) safety by providing indirect leading vehicle information.
  • This approach is feasible and does not require vehicle-to-vehicle (V2V) communication.
  • The findings support the development of innovative vehicle systems utilizing smart perception for improved road safety.