Connected Vehicle Identification System for Cooperative Control of Connected Automated Vehicles

A field evaluation of a recently developed cooperative platooning algorithm (developed by PI Park) that does not require connectivity in mixed traffic has shown significant benefits over adaptive cruise control. One of the critical challenges of implementing the CACC algorithm is appropriately identifying connected and automated vehicles to form a cooperative adaptive cruise control system. This identification is not a trivial task mainly because each connected vehicle does not share its vehicle information due to privacy concerns. In addition, this connectivity opens cybersecurity issues. As shown by PI Park, connected vehicles’ communications are vulnerable. The proposed connected vehicle identification system (CVIS) relies on relatively few sensors, including radar sensors, vehicle-to-vehicle communication devices (e.g., C-V2X), and a global position system (GPS). The preceding vehicle identification system (PVIS) was developed by PI Park, and is currently being expanded to improve its performance by considering surrounding vehicles (needed for the lane-change recommendations) and the use of GPS velocity. A simulation-based study showed significant improvements over the immediately preceding vehicle identification system using only GPS distance.