An Adaptive Strategy for Connected Eco-Driving under Uncertain Traffic and Signal Conditions

Connected and automated vehicle technology could bring about transformative reductions in traffic congestion, greenhouse gas emissions, air pollution, and energy consumption. Connected and automated vehicles (CAVs) can directly communicate with other vehicles and road infrastructure and use sensing technology and artificial intelligence to respond to traffic conditions and optimize fuel consumption. An eco-approach and departure application for connected and automated vehicles has been widely studied as a means of calculating the most energy-efficient speed profile and guiding a vehicle through signalized intersections without unnecessary stops and starts. Simulations using this application on roads with fixed-timing traffic signals have produced 12% reductions in fuel consumption and greenhouse gas emissions. But real-world traffic conditions are much more complex—uncertainties and the limited sensing range of automated vehicles create challenges for determining the most energy-efficient speed.

To account for this uncertainty, researchers from the University of California, Riverside, propose a prediction-based, adaptive connected eco-driving strategy. The proposed strategy analyzes the possible upcoming traffic and signal scenarios based on historical data and live information collected from communication and sensing devices, and then chooses the most energy-efficient speed. This approach can be extended to accommodate different vehicle powertrains and types of roadway infrastructure. This research brief summarizes findings from the research and provides research implications.