Investigating Platoon Formation and Retention Using Reduced-Scale Mobile Robots with Controllers Based on Established Car-Following Models

Key Takeaways

• IDM controller yields highest safety-efficiency balance
• ACC performs second best; GHR caused collisions
• Physical robot tests reveal gaps versus simulations
• RSMRs enable reproducible baseline for controller evaluation
• Platoon stability varies across steady, congested, stop‑go traffic

Pulse Analysis

Platooning promises higher roadway capacity and reduced emissions, but its success hinges on reliable car‑following algorithms. Traditional validation relies on computer simulations, which can overlook nuances such as sensor latency, actuator dynamics, and unmodeled disturbances. By translating five well‑established car‑following models into controllers for reduced‑scale mobile robots, the study bridges the gap between theory and practice, offering a tangible platform where traffic‑flow phenomena can be observed in real time. This approach not only validates model assumptions but also uncovers emergent behaviors that pure numerical studies might miss.

The experimental results place the Intelligent Driver Model (IDM) at the forefront of platoon control, achieving compact vehicle spacing with minimal speed variance—a critical safety‑efficiency trade‑off for autonomous fleets. Adaptive Cruise Control (ACC) followed closely, while the classic Gazis‑Herman‑Rothery (GHR) model proved unstable, leading to collisions under certain traffic densities. These findings provide actionable insights for manufacturers and fleet operators seeking to fine‑tune longitudinal control systems, especially as mixed‑traffic environments demand robust performance across diverse flow regimes.

Beyond immediate engineering implications, the research showcases the broader utility of reduced‑scale mobile robots as a cost‑effective, reproducible testbed for traffic‑dynamics investigations. By delivering empirical data that align closely with theoretical fundamental diagrams, the RSMR platform can accelerate standard‑setting efforts and inform policy on connected‑vehicle deployments. Future work may expand to multi‑lane scenarios, integrate vehicle‑to‑infrastructure communication, and explore machine‑learning‑enhanced controllers, positioning physical robot experiments as a cornerstone of next‑generation mobility research.