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A Planning Tool to Assess Advanced Vehicle Sensor Technologies on Traffic Flow, Fuel Economy, and Emissions
Technical Paper
2018-01-1100
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Light-duty vehicles are responsible for over 16% greenhouse gas (GHG) emissions in the United States. Human driving behavior has a significant impact on vehicle efficiency, the emission of GHG and primary pollutants, and safety. With environmental health in mind, both academia and industry have the opportunity to develop advanced sensor and complementary control technologies to manage the human role.
To explore this hypothesis, the research reported herein began with a comprehensive study of demonstration projects and academic publications which test and evaluate modern technologies to mitigate threats associated with safety and efficiency. The research identified the environmental signals to detect, the corresponding sensors to detect these signals, and the sensor technologies to study in greater depth. Of all the sensor technologies, vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications technologies emerged as the most promising. A major requirement identified is a planning tool designed to assess advanced vehicle sensor technologies on traffic flow, fuel economy, and emissions.
In response, a major focus of the research was then directed to (1) developing the Fuel Economy and Traffic of Connected Hybrids (FETCH) planning tool, and (2) evaluating the utility of FETCH for a simple V2V-enabled automatic re-routing control on a custom roadway. The major outcomes of this research work are (1) the FETCH tool, (2) a research plan for utilizing FETCH to explore the variety of scenarios evolving for the advanced control of hybrid vehicles, and (3) an example research result of FETCH.
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Wifvat, V., Shaffer, B., and Samuelsen, S., "A Planning Tool to Assess Advanced Vehicle Sensor Technologies on Traffic Flow, Fuel Economy, and Emissions," SAE Technical Paper 2018-01-1100, 2018, https://doi.org/10.4271/2018-01-1100.Data Sets - Support Documents
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