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

Key Takeaways

• IDM controller yields best safety‑efficiency balance
• ACC controller ranks second in performance
• GHR model caused collisions in tests
• Physical robot tests differ from simulations
• RSMRs provide reproducible baseline for controller evaluation

Pulse Analysis

Physical validation of traffic‑flow models is gaining traction as autonomous vehicle (AV) manufacturers seek reliable platooning solutions. While computer simulations offer speed and flexibility, they often overlook sensor noise, actuator lag, and unmodeled dynamics that emerge in real hardware. By deploying car‑following algorithms on reduced‑scale mobile robots, the researchers created a controlled yet tangible environment that captures these nuances, providing a more trustworthy assessment of controller robustness across diverse traffic conditions.

The experimental results highlight the intelligent driver model (IDM) as the most promising candidate for platoon control, delivering compact formations with minimal speed variance—key metrics for safety and fuel efficiency. Adaptive cruise control (ACC) followed closely, suggesting that commercially available ACC systems could be refined for tighter coordination. In contrast, the classic Gazis‑Herman‑Rothery (GHR) model provoked collisions, and the PID and Gipps controllers exhibited pronounced oscillations, indicating that legacy models may require substantial tuning before real‑world deployment. These insights help engineers prioritize algorithmic refinements and allocate testing resources more effectively.

Beyond the immediate technical takeaways, the study reinforces the strategic importance of hardware‑in‑the‑loop testing for the broader AV ecosystem. As traffic authorities contemplate regulations for mixed‑traffic platoons, evidence from physical experiments can inform safety standards and certification pathways. Moreover, the reproducible baseline established by the RSMR platform offers a shared reference point for academia and industry, fostering collaborative advancements in cooperative adaptive cruise control, traffic‑management policies, and ultimately, smoother, greener roadways.