In-Vehicle Human-Machine Interface to Support Drivers in Conditionally Automated Platooning

2026-05-18

AI Summary

This study addresses the challenges of insufficient situational awareness and unstable coordination in conditional automated platooning, where drivers are required to continuously supervise the system. The authors propose and validate a novel in-vehicle human–machine interface (HMI) that enhances supervisory effectiveness by continuously visualizing system status and inter-vehicle spacing. For the first time, an immersive platooning environment was implemented on a six-degree-of-freedom high-fidelity motion simulator. Empirical results demonstrate that the proposed HMI reduces manual interventions by approximately 80%, effectively suppressing unnecessary takeovers. Although collision rates and disconnection response delays did not show significant improvement, the findings underscore the stabilizing role of continuous information feedback on driver supervision behavior, offering critical insights for human–machine interaction design in conditionally automated platooning systems.

Abstract

Vehicle platooning enables close-gap driving and offers potential benefits for traffic efficiency and safety. In conditionally automated platooning, drivers remain responsible for supervising the system and intervening when necessary, making effective Human-Machine Interfaces (HMIs) critical for maintaining situational awareness and stable driver-automation coordination. This paper investigates whether an in-vehicle HMI providing continuous system-state and inter-vehicle distance information improves supervisory behavior, safety, and platoon stability. We conducted a simulation-based experiment integrated with a 6-degree-of-freedom motion system to enhance scenario realism. Dependent variables included collision occurrence, response latency following platoon disconnection, and the number of manual interventions during intact platooning. Results showed significantly fewer manual interventions when the HMI was active, with intervention rates about 80% higher without it. No significant effects were found for collision occurrence or response latency, indicating that additional information improves supervisory stability during platooning but does not substantially affect emergency reactions or collision rates.

Problem

• Human-Machine Interface
• Conditionally Automated Driving
• Vehicle Platooning
• Situational Awareness
• Driver Supervision

Innovation

• Human-Machine Interface
• Automated Platooning
• Situational Awareness
• Driving Simulation
• Conditional Automation