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

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

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Abstract

This article focuses on the speed planning problem for connected and automated vehicles (CAVs) communicating to traffic lights. The uncertainty of traffic signal timing for signalized intersections on the road is considered. The eco-driving problem is formulated as a data-driven chance-constrained robust optimization problem. Effective red-light duration (ERD) is defined as a random variable, and describes the feasible passing time through the signalized intersections. Usually, the true probability distribution for ERD is unknown. Consequently, a data-driven approach is adopted to formulate chance constraints based on empirical sample data. This incorporates robustness into the eco-driving control problem with respect to uncertain signal timing. Dynamic programming (DP) is employed to solve the optimization problem. The simulation results demonstrate that the proposed method can generate optimal speed reference trajectories with 40% less vehicle fuel consumption, while maintaining the arrival time at a similar level compared to a modified intelligent driver model (IDM). The proposed control approach significantly improves the controller's robustness in the face of uncertain signal timing, without requiring to know the distribution of the random variable a priori.

Authors:

^[1];

^[2]; Borrelli, Francesco ^[2];

^[2]

Beijing Inst. of Technology (China)
Univ. of California, Berkeley, CA (United States)

Publication Date:: Tue Jan 21 00:00:00 EST 2020

Research Org.:: Univ. of California, Berkeley, CA (United States)

Sponsoring Org.:: USDOE Advanced Research Projects Agency - Energy (ARPA-E)

OSTI Identifier:: 1799085

Grant/Contract Number:: AR0000791

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Internet of Things Journal (Online)

Additional Journal Information:: Journal Name: IEEE Internet of Things Journal (Online); Journal Volume: 7; Journal Issue: 5; Journal ID: ISSN 2327-4662

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 33 ADVANCED PROPULSION SYSTEMS; planning; timing; optimization; fuels; clocks; roads; real-time systems; connected and automated vehicle (CAV); data-driven; eco-driving; robust control; traffic signal

Citation Formats


                    Sun, Chao, Guanetti, Jacopo, Borrelli, Francesco, and Moura, Scott J. Optimal Eco-Driving Control of Connected and Autonomous Vehicles Through Signalized Intersections.  United States: N. p., 2020. 
Web.  doi:10.1109/jiot.2020.2968120.

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                    Sun, Chao, Guanetti, Jacopo, Borrelli, Francesco, & Moura, Scott J. Optimal Eco-Driving Control of Connected and Autonomous Vehicles Through Signalized Intersections.  United States.  https://doi.org/10.1109/jiot.2020.2968120

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                    Sun, Chao, Guanetti, Jacopo, Borrelli, Francesco, and Moura, Scott J. Tue .  
"Optimal Eco-Driving Control of Connected and Autonomous Vehicles Through Signalized Intersections".  United States.  https://doi.org/10.1109/jiot.2020.2968120.  https://www.osti.gov/servlets/purl/1799085.

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@article{osti_1799085,

  title        = {Optimal Eco-Driving Control of Connected and Autonomous Vehicles Through Signalized Intersections},

  author       = {Sun, Chao and Guanetti, Jacopo and Borrelli, Francesco and Moura, Scott J.},

  abstractNote = {This article focuses on the speed planning problem for connected and automated vehicles (CAVs) communicating to traffic lights. The uncertainty of traffic signal timing for signalized intersections on the road is considered. The eco-driving problem is formulated as a data-driven chance-constrained robust optimization problem. Effective red-light duration (ERD) is defined as a random variable, and describes the feasible passing time through the signalized intersections. Usually, the true probability distribution for ERD is unknown. Consequently, a data-driven approach is adopted to formulate chance constraints based on empirical sample data. This incorporates robustness into the eco-driving control problem with respect to uncertain signal timing. Dynamic programming (DP) is employed to solve the optimization problem. The simulation results demonstrate that the proposed method can generate optimal speed reference trajectories with 40% less vehicle fuel consumption, while maintaining the arrival time at a similar level compared to a modified intelligent driver model (IDM). The proposed control approach significantly improves the controller's robustness in the face of uncertain signal timing, without requiring to know the distribution of the random variable a priori.},

  doi          = {10.1109/jiot.2020.2968120},

  journal      = {IEEE Internet of Things Journal (Online)},

  number       = 5,

  volume       = 7,

  place        = {United States},

  year         = {Tue Jan 21 00:00:00 EST 2020},

  month        = {Tue Jan 21 00:00:00 EST 2020}

}

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