Preceding Vehicle Identification for Cooperative Adaptive Cruise Control Platoon Forming

Abstract

Cooperative adaptive cruise control (CACC) has shown great potential in enhancing traffic efficiency and sustainability. While past research efforts focused on the development of CACC systems and their demonstrations, very few of them considered in detail how to form a CACC platoon in real traffic, where the proper identification of preceding vehicle is required. To ensure safe and reliable CACC operations, the following vehicle needs to establish the correct connection with its preceding vehicle. Although this can be done by matching information shared by surrounding vehicles with the ego-vehicle’s radar measurements, the existence of sensor/global positioning system (GPS) errors makes it a challenging task. Considering possible sensor/GPS errors in real traffic, this paper proposes a procedure of identifying preceding vehicle under fully connected vehicle environment and evaluates three preceding vehicle identification systems (PVIS), namely, location-based PVIS, distance-based PVIS, and integrated PVIS combining both location and distance information. The mathematical models of PVISs are developed. The performance evaluation of the PVISs is conducted based on real vehicle trajectory data from the Next Generation Simulation (NGSIM) program, which reflects how vehicles’ relative positions change in a high-density segment of highway. The feasibility, performance, and potential of the three PVISs are compared. The results show that location-based PVIS requires a relative positioning accuracy below 1.1 m to ensure an acceptable identification time with zero failure rate. The integrated PVIS has the best performance, providing 99% confidence in identifying preceding vehicle within 1.3 s under typical sensor error settings.