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Developing an Adaptive Strategy for Connected Eco-Driving Under Uncertain Traffic and Signal Conditions

Peng Hao, Zhensong Wei, Zhengwei Bai and Matthew J. Barth

Institute of Transportation Studies, Working Paper Series from Institute of Transportation Studies, UC Davis

Abstract: The Eco-Approach and Departure (EAD) application has been proved to be environmentally efficient for a Connected and Automated Vehicles (CAVs) system. In the real-world traffic, traffic conditions and signal timings are usually dynamic and uncertain due to mixed vehicle types, various driving behaviors and limited sensing range, which is challenging in EAD development. This research proposes an adaptive strategy for connected eco-driving towards a signalized intersection under real world conditions. Stochastic graph models are built to link the vehicle and external (e.g., traffic, signal) data and dynamic programing is applied to identify the optimal speed for each vehicle-state efficiently. From energy perspective, adaptive strategy using traffic data could double the effective sensor range in eco-driving. A hybrid reinforcement learning framework is also developed for EAD in mixed traffic condition using both short-term benefit and long-term benefit as the action reward. Micro-simulation is conducted in Unity to validate the method, showing over 20% energy saving. View the NCST Project Webpage

Keywords: Engineering; Autonomous vehicles; Connected vehicles; Ecodriving; Energy consumption; Machine learning; Microsimulation; Signalized intersections; Vehicle mix (search for similar items in EconPapers)
Date: 2020-01-01
New Economics Papers: this item is included in nep-ene, nep-reg and nep-tre
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