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

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.