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

Author

Listed:
  • Hao, Peng
  • Wei, Zhensong
  • Bai, Zhengwei
  • Barth, Matthew J.

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

Suggested Citation

  • Hao, Peng & Wei, Zhensong & Bai, Zhengwei & Barth, Matthew J., 2020. "Developing an Adaptive Strategy for Connected Eco-Driving Under Uncertain Traffic and Signal Conditions," Institute of Transportation Studies, Working Paper Series qt2fv5063b, Institute of Transportation Studies, UC Davis.
    • Handle: RePEc:cdl:itsdav:qt2fv5063b
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    References listed on IDEAS

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    1. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    2. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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    More about this item

    Keywords

    Engineering; Autonomous vehicles; Connected vehicles; Ecodriving; Energy consumption; Machine learning; Microsimulation; Signalized intersections; Vehicle mix;
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