Design and Evaluation of Cooperative Adaptive Cruise Control System for Heavy Freight Vehicles
Type of DegreeMaster's Thesis
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This thesis describes the design, implementation, and evaluation of a Cooperative Adaptive Cruise Control (CACC) system for heavy freight trucks that seeks to provide a platform for future cooperative control and estimation schemes. The freight trucking industry is the primary method of transporting goods in the United States. Approximately 70 percent of all transported goods travel by freight trucking. It is estimated that the freight trucking industry in the US spends close to 10 billion dollars on fuel each year. Most of the industrys time and fuel is consumed on long interstate corridors. Due to the high volume of these vehicles, there is huge potential benefit for a collaborative scheme of control. This control algorithm can help vehicles reduce fuel consumption, emissions, driver fatigue, and traffic congestion. By reducing the inter-vehicle spacing and automating throttle and brakes, all of these goals can be accomplished. This control system is known as CACC and is particularity valuable for freight vehicles as they can see significant value from reduced air drag by staying within the wake of a preceding truck. CACC systems have been in development for many years now and are beginning to be tested on real-world convoys. There is still some potential savings to be gained through controller optimization. To validate new control and estimation techniques a vehicle testing platform is required. The primary contribution of this work is in the development of a platform for future research and validation as a proof of concept. The system is comprised of a communication network between vehicles, a low-level brake and throttle controller, a range estimation scheme and a cascaded gap controller. Testing results from this system both in simulation and on-highway driving are presented and show fuel savings of approximately 2-3 percent. While the fuel savings achieved under this work are not as high as predicted, it is expected that with further controller optimization will yield results in line with that of other researchers. The vehicle platform developed, however, was shown to be stable and will provide a good basis for future research.