Cooperative Adaptive Cruise Control System Identification in Multi-Brand Truck Platooning

Abstract This study examines the identification and evaluation of Cooperative Adaptive Cruise Control (CACC) systems created by four major Japanese truck manufacturers, focusing on data gathered from real-world platooning experiments. Utilizing information from a 2019 multi-vehicle field test, we identified CACC control rules by differentiating between acceleration and deceleration phases and implementing constrained linear least-squares estimation. The resulting models accurately replicated the commanded acceleration profiles for each manufacturer’s system with high fidelity. We verified the validity of these models through simulations using TruckMaker and Simulink, which showed that they aligned with experimental vehicle speed responses and effectively reproduced dynamic behaviors. To evaluate operational performance in multi-brand platooning scenarios, we conducted simulations that varied platoon order, deceleration levels, and target time headways. The results identified specific platoon configurations that exhibited decreased gap-keeping stability, indicating that high deceleration or short time headways could negatively affect performance. To enhance this performance, we proposed a control strategy that uses the desired acceleration of the lead vehicle as a feedforward input. Compared to traditional CACC, this proposed method significantly decreased settling time and jerk, particularly during emergency braking conditions, where responsiveness was greatly improved. These findings contribute to the advancement of practical and robust CACC systems for multi-brand truck platooning and lay a foundation for their early implementation in real-world freight logistics.