Optimal Eco-Driving Control of Connected and Autonomous Vehicles Through Signalized Intersections

Sun, Chao; Guanetti, Jacopo; Borrelli, Francesco; Moura, Scott J.

doi:10.1109/jiot.2020.2968120

Cited by 156 publications

(54 citation statements)

References 38 publications

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“…Asadi et al [ 56 ] used short-range radar to obtain the distance to the vehicle in front, combined with traffic signal information, and adjust the speed trajectory with the minimum braking amount and maintaining the safety distance between vehicles as the control objectives. A data-driven opportunity-constrained eco-driving control method [ 57 ] was proposed for speed trajectory planning under uncertain signal timing to improve the robustness of the optimal speed trajectory to random traffic light times. The results showed that it can significantly reduce fuel consumption while sacrificing less than 5% of the time cost.…”

Section: Eco-driving Theorymentioning

confidence: 99%

An Overview of Eco-Driving Theory, Capability Evaluation, and Training Applications

et al. 2021

Sensors

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Constrained by traditional fuel-saving technologies that have almost reached the limit of fuel-saving potential, the difficulty in changing urban congestion, and the low market penetration rate of new energy vehicles, in the short term, eco-driving seems to be an effective way to achieve energy-saving and emissions reduction in the transportation industry. This paper reviews the energy-saving theory and technology of eco-driving, eco-driving capability evaluation, and the practical application of eco-driving, and points out some limitations of previous studies. Specifically, the research on eco-driving theory mostly focuses on a single vehicle in a single scene, and there is a lack of eco-driving research for fleets or regions. In addition, the parameters used to evaluate eco-driving capabilities mainly focus on speed, acceleration, and fuel consumption, but external factors that are not related to the driver will affect these parameters, making the evaluation results unreasonable. Fortunately, vehicle big data and the Internet of Vehicles (V2I) provides an information basis for solving regional eco-driving, and it also provides a data basis for the study of data-driven methods for the fair evaluation of eco-driving. In general, the development of new technologies provides new ideas for solving some problems in the field of eco-driving.

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Section: Eco-driving Theorymentioning

confidence: 99%

An Overview of Eco-Driving Theory, Capability Evaluation, and Training Applications

et al. 2021

Sensors

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“…It can not only avoid boring waiting and unnecessary energy loss during emergency brake, but also ensure the driving comfort. Moreover, for signalised intersections with uncertain signal timing, literature [115] proposes a data‐driven chance constrained eco‐driving control approach to plan velocity trajectory, which endow the optimal velocity trajectory with robustness for stochastic red light delays.…”

Section: Emss For Hev/phev Under Itsmentioning

confidence: 99%

Efficient energy management strategy for hybrid electric vehicles/plug‐in hybrid electric vehicles: review and recent advances under intelligent transportation system

Yang

Zha

Wang

et al. 2020

IET Intelligent Transport Systems

166

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“…In signalized intersections, trajectory optimization in [19] provides a smooth path for CAVs to cross the signals without stopping at red signals in static environments. Also, optimal eco-driving control is presented in [20] which employed a data-driven approach to account for the uncertainty in signalized time phasing based on dynamic programming for optimization. Lastly, RL-based velocity agents are developed to locally control CAVs for avoiding obstacles [21] and risky behaviors [22].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-Based Control of Signalized Intersections Having Platoons

et al. 2022

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Smart transportation cities are based on intelligent systems and data sharing while human drivers generally have limited capabilities and imperfect observations in traffics. The perception of Connected and Autonomous Vehicle (CAV) utilizes data sharing through Vehicle-To-Vehicle (V2V) and Vehicle-To-Infrastructure (V2I) communications to improve driving behaviors and reduce traffic delays and fuel consumption. This paper proposes a Double Agent (DA) intelligent traffic signal module based on the Reinforcement Learning (RL) method where the first agent named as Velocity Agent (VA) aims to minimize the fuel consumption by controlling the speed of platoons and single CAVs crossing a signalized intersection, while the second agent named as Signal Agent (SA) proceeds to efficiently reduce traffic delays through signal sequencing and phasing. Several simulation studies are conducted for a signalized intersection with different traffic flows and the performance of a single-agent with only the VA, DA with both VA and SA, and Intelligent Driver Model (IDM) are compared. It is shown that the proposed DA solution improves the average delay by 47.3% and the fuel efficiency by 13.6% compared to the Intelligent Driver Model (IDM).

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Optimal Eco-Driving Control of Connected and Autonomous Vehicles Through Signalized Intersections

Cited by 156 publications

References 38 publications

An Overview of Eco-Driving Theory, Capability Evaluation, and Training Applications

An Overview of Eco-Driving Theory, Capability Evaluation, and Training Applications

Efficient energy management strategy for hybrid electric vehicles/plug‐in hybrid electric vehicles: review and recent advances under intelligent transportation system

Reinforcement Learning-Based Control of Signalized Intersections Having Platoons

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