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Integrating Intervehicular Communications, Vehicle Localization, and a Digital Map for Cooperative Adaptive Cruise Control with Target Detection Loss
ISSN: 2574-0741, e-ISSN: 2574-075X
Published October 19, 2020 by SAE International in United States
Citation: Lin, Y. and Eskandarian, A., "Integrating Intervehicular Communications, Vehicle Localization, and a Digital Map for Cooperative Adaptive Cruise Control with Target Detection Loss," SAE Intl. J CAV 3(3):193-204, 2020, https://doi.org/10.4271/12-03-03-0015.
Adaptive cruise control (ACC) is an advanced driver assistance system (ADAS) that enables vehicle following with desired intervehicular distances. Cooperative adaptive cruise control (CACC) is upgraded ACC that utilizes additional intervehicular wireless communications to share vehicle states such as acceleration to enable shorter gap following. Both ACC and CACC rely on range sensors such as radar to obtain the actual intervehicular distance for gap-keeping control. The range sensor may lose detection of the target, the preceding vehicle, on curvy roads or steep hills due to limited angle of view. Unfavorable weather conditions, target selection failures, or hardware issues may also result in target detection loss. During target detection loss, the vehicle following system usually falls back to cruise control (CC), wherein the ego (following) vehicle maintains a constant speed. In this work, we propose an alternative way to obtain the intervehicular distance during target detection loss to continue vehicle following. The proposed algorithm integrates intervehicular communications, accurate vehicle localization, and a high-definition (HD) map with lane center information to approximate the intervehicular distance. In-lab robot following experiments demonstrated that the proposed algorithm provided desirable intervehicular distance approximation. Although the algorithm is intended for vehicle following, it can also be used for other scenarios that demand vehicles’ relative distance approximation. The work also showcases our in-lab development effort of robotic emulation of traffic for connected and automated vehicles.