Show simple item record

dc.contributor.authorWu, Chaoxianen
dc.contributor.authorLin, Yuanen
dc.contributor.authorEskandarian, Azimen
dc.description.abstractCooperative adaptive cruise control (CACC) communicates the relevant preceding vehicle state data to the follower (ego) vehicle to improve the vehicle following capabilities. In general, the CACC utilizes the preceding vehicle's desired acceleration from wireless communication as a feedforward term in the controller of the ego vehicle, which dominantly determines the total control input. However, communication loss would degrade CACC to adaptive cruise control (ACC), where the lack of the feedforward term during communication loss would increase the inter-vehicular distance or, otherwise, may lead to collision during vehicle emergency braking. This paper proposes a control algorithm with an adaptive Kalman filter estimating the acceleration of a preceding vehicle, and the estimated acceleration is implemented as a feedforward signal in the ego-vehicle CACC controller in case of communication loss. The proposed control algorithm is evaluated by the experiments using mobile robots that emulate driving. In addition, the simulations of real vehicles are also conducted. The results of simulations and robot experiments show that the performance of implementing the adaptive Kalman filter during communication loss is better than fallback to ACC and the normal Kalman filter based on the Singer model.en
dc.description.sponsorshipChina Scholarship Councilen
dc.rightsCreative Commons Attribution 3.0 Unporteden
dc.subjectCommunication lossen
dc.subjectadaptive Kalman filteren
dc.subjectstatistical modelen
dc.subjectcooperative adaptive cruise control (CACC)en
dc.subjectacceleration estimationen
dc.titleCooperative Adaptive Cruise Control With Adaptive Kalman Filter Subject to Temporary Communication Lossen
dc.typeArticle - Refereeden
dc.contributor.departmentMechanical Engineeringen
dc.description.notesThe work of C. Wu was supported by the China Scholarship Council for him to conduct this research as a visiting doctoral student in Autonomous Systems and Intelligent Machines Lab of the Department of Mechanical Engineering of Virginia Tech, Blacksburg, VA 24060 USA.en
dc.title.serialIEEE Accessen

Files in this item


This item appears in the following Collection(s)

Show simple item record

Creative Commons Attribution 3.0 Unported
License: Creative Commons Attribution 3.0 Unported