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Determining the Greenhouse Gas Emissions Benefit of an Adaptive Cruise Control System Using Real-World Driving Data
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 2, 2019 by SAE International in United States
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Adaptive cruise control is an advanced vehicle technology that is unique in its ability to govern vehicle behavior for extended periods of distance and time. As opposed to standard cruise control, adaptive cruise control can remain active through moderate to heavy traffic congestion, and can more effectively reduce greenhouse gas emissions. Its ability to reduce greenhouse gas emissions is derived primarily from two physical phenomena: platooning and controlled acceleration. Platooning refers to reductions in aerodynamic drag resulting from opportunistic following distances from the vehicle ahead, and controlled acceleration refers to the ability of adaptive cruise control to accelerate the vehicle in an energy efficient manner. This research calculates the measured greenhouse gas emissions benefit of adaptive cruise control on a fleet of 51 vehicles over 62 days and 199,300 miles. To our knowledge, the greenhouse gas emissions benefit of an advanced vehicle technology has never been demonstrated in this manner, and no automaker has published such extensive data pertaining to adaptive cruise control. These results highlight the opportunity to further reduce consumer fuel use and global greenhouse gas emissions using autonomous driving technology, provided that the design considers vehicle energy consumption. If such a benefit is validated through a statistically meaningful set of real-world studies, we believe it may mark a breakthrough in the industry and have implications in vehicle design and product planning around the globe. This paper draws attention to the need for an extended research project involving a larger and more diverse vehicle fleet across North America.
CitationDvorkin, W., King, J., Gray, M., and Jao, S., "Determining the Greenhouse Gas Emissions Benefit of an Adaptive Cruise Control System Using Real-World Driving Data," SAE Technical Paper 2019-01-0310, 2019, https://doi.org/10.4271/2019-01-0310.
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