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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.

Optimal energy management of hybrid electric vehicles has previously been shown to increase fuel economy (FE) by approximately 20% thus reducing dependence on foreign oil, reducing greenhouse gas (GHG) emissions, and reducing Carbon Monoxide (CO) and Mono Nitrogen Oxide (NOx) emissions. This demonstrated FE increase is a critical technology to be implemented in the real world as Hybrid Electric Vehicles (HEVs) rise in production and consumer popularity. This review identifies two research gaps preventing optimal energy management of hybrid electric vehicles from being implemented in the real world: sensor and signal technology and prediction scope and error impacts. Sensor and signal technology is required for the vehicle to understand and respond to its environment; information such as chosen route, speed limit, stop light locations, traffic, and weather needs to be communicated to the vehicle.