Optimized Cooperative Car-Following Through Lightweight Vehicle-to-Vehicle Intent Sharing

Cooperative driving systems are expected to enhance safety, mobility, and efficiency through vehicle connectivity technologies. Lower-level vehicle-to-vehicle (V2V) communication transmits high-frequency status information, such as location, velocity, and acceleration, between vehicles. This approach contributes limitedly to prediction accuracy, requires high-frequency hardware, and is sensitive to communication delays. Recent studies have shown that intent sharing, which conveys planning trajectories, significantly improves prediction accuracy and control performance but requires higher band-width. However, mainstream vehicle communication methods struggle to balance cost and bandwidth for effective intent sharing. High-bandwidth wireless communication methods such as dedicated short-range communication (DSRC) and cellular vehicle-to-everything (C-V2X) cost much for devices, while low-cost visible light communication (VLC) can hardly support the necessary bandwidth. To address this challenge, we propose a lightweight intent sharing approach that reduces data transmission volume while maintaining prediction accuracy. Specifically, intended velocity trajectories are represented using regressed polynomial functions over a fixed time period, requiring only the transmission of polynomial coefficients and a timestamp for synchronization. The feasibility of this approach is demonstrated through simulations of car-following behavior using a Linear–Quadratic Regulator (LQR). Additionally, real vehicle experiments using a designated velocity cycle further validate the method. Results show that both planned and actual trajectories of the following vehicle closely align with those using ideal intent sharing approaches under significantly reduced communication data volume.