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Adaptive Cruise Control (ACC) is an Advanced Driver Assistance System (ADAS)that enables vehicle following with desired inter-vehicular distances.Cooperative Adaptive Cruise Control (CACC) is upgraded ACC that utilizesadditional inter-vehicular wireless communication to share vehicle states suchas acceleration to enable shorter gap following. Both ACC and CACC rely onrange sensors such as radar to obtain the actual inter-vehicular distance forgap-keeping control. The range sensor may lose detection of the target, thepreceding vehicle, on curvy roads or steep hills due to limited angle of view.Unfavourable weather conditions, target selection failure, or hardware issuemay also result in target detection loss. During target detection loss, thevehicle following system usually falls back to Cruise Control (CC) wherein thefollower vehicle maintains a constant speed. In this work, we propose analternative way to obtain the inter-vehicular distance during target detectionloss to continue vehicle following. The proposed algorithm integratesinter-vehicular communication, accurate vehicle localization, and a digital mapwith lane center information to approximate the inter-vehicular distance.In-lab robot following experiments demonstrated that the proposed algorithmprovided desirable inter-vehicular distance approximation. Although thealgorithm is intended for vehicle following application, it can also be usedfor other scenarios that demand vehicles' relative distance approximation. Thework also showcases our in-lab development effort of robotic emulation oftraffic for connected and automated vehicles.

sae international journal of connected and automated vehicles

Integrating Inter-vehicular Communication, Vehicle Localization, and a  Digital Map for Cooperative Adaptive Cruise Control with Target Detection  Loss