Eco-Driving --- Current Strategies and issues, A Preliminary Survey

Li, Chunxiao; Ni, Aiping; Ding, Jie · 2015 · OpenAlex-citations

DOI: 10.2991/itoec-15.2015.46

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Summary

This paper presents a preliminary survey of current strategies and issues in eco-driving, defined as driving behavior modification aimed at energy conservation and emission reduction. Motivated by the significant contribution of transportation to global CO2 emissions and the need for sustainable energy policies, the authors review state-of-the-art research across four primary domains: emission estimation methods, traffic signal control, cruise control, and eco-driving assist systems. The core principle of eco-driving is maintaining constant vehicle speed to minimize acceleration, deceleration, and idling. The review categorizes emission estimation into microscopic and macroscopic levels. Microscopic methods couple traffic simulators like VISSIM with instantaneous emission models such as PHEM, offering precise assessments of environmental impact based on real-time speed and acceleration changes. Macroscopic methods, such as Willan’s model, utilize aggregate data like average speed and stop frequency, providing convenience for large-scale optimization but lacking the theoretical precision of microscopic approaches. The authors note that while microscopic models are theoretically robust, macroscopic models are often more practical for broad statistical analysis. In traffic signal control, the paper highlights the shift from fixed-cycle systems to adaptive controls that reduce vehicle stops and waiting times. Strategies include fuzzy logic controllers, decentralized systems using vehicle-to-vehicle communication or RFID, and multi-agent reinforcement learning for coordinated intersection management. Advanced techniques, such as Petri net modeling and stochastic dynamic programming, are employed to optimize signal timing based on real-time traffic data, thereby smoothing traffic flow and reducing emissions. Cruise control methods are examined for their role in smoothing travel and optimizing fuel efficiency. The survey covers Cooperative Adaptive Cruise Control (CACC) systems that utilize V2V communication to maintain safe spacing and reduce accidents. Innovations include predictive cruise control algorithms that adjust velocity based on upcoming traffic signals, linear model predictive control to directly minimize fuel consumption, and learning-based controllers that adapt to varying road conditions. These systems often integrate safety features, such as obstacle detection via sensor fusion, with energy optimization goals. Finally, the paper discusses eco-driving assist systems (EDAS) that provide drivers with real-time feedback to improve driving habits. These systems range from mobile applications like Artemisa to onboard diagnostics that calculate fuel economy based on vehicle loading and driving style. The authors conclude that effective eco-driving relies on a combination of optimal velocity control via vehicle algorithms and adaptive infrastructure design. Future opportunities include leveraging the Markov property of driving cycles, improving vehicle maintenance, and enhancing highway infrastructure to further support fuel-saving behaviors.

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• eco driving