Date of Award

8-2019

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Electrical and Computer Engineering (Holcomb Dept. of)

Committee Member

James Martin, Committee Chair

Committee Member

Harlan B Russell

Committee Member

Daniel L Noneaker

Committee Member

Kuang Ching Wang

Committee Member

James Westall

Abstract

Cooperative adaptive cruise control (CACC), a form of vehicle platooning, is a well known connected vehicle application. It extends adaptive cruise control (ACC) by incorporating vehicle-to-vehicle communications. A vehicle periodically broadcasts a small message that includes in the least a unique vehicle identifier, its current geo-location, speed, and acceleration. A vehicle might pay attention to the message stream of only the car ahead. While CACC is under intense study by the academic community, the vast majority of the relevant published literature has been limited to theoretical studies that make many simplifying assumptions. The research presented in this dissertation has been motivated by our observation that there is limited understanding of how platoons actually work under a range of realistic operating conditions. Our research includes a performance study of V2V communications based on actual V2V radios supplemented by simulation. These results are in turn applied to the analysis of CACC. In order to understand a platoon at scale, we resort to simulations and analysis using the ns3 simulator. Assessment criteria includes network reliability measures as well as application oriented measures. Network assessment involves latency and first and second order loss dynamics. CACC performance is based on stability, frequency of crashes, and the rate of traffic flow. The primary goal of CACC is to maximize traffic flow subject to a maximum allowed speed. This requires maintaining smaller inter-vehicle distances which can be problematic as a platoon can become unstable as the target headway between cars is reduced. The main contribution of this dissertation is the development and evaluation of two heuristic approaches for dynamically adapting headway both of which attempt to minimize the headway while ensure stability. We present the design and analysis of a centralized and a distributed implementation of the algorithm. Our results suggest that dynamically adapting the headway time can improve the overall platoon traffic flow without the platoon becoming unstable.

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