Journal Home > Volume 5 , Issue 3
Journal of Intelligent and Connected Vehicles 2022, 5(3): 260-269 https://doi.org/10.1108/JICV-06-2022-0023
Literature review | Open Access | Issue | Published: 29 July 2022

Intersection control with connected and automated vehicles: a review

Show Author's Information Jiaming Wu1( ),  Xiaobo Qu2
Department of Architecture and Civil Engineering, Chalmers University of Technology, Gothenburg, Sweden
School of Vehicle and Mobility, Tsinghua University, Beijing, China
Keywords:
Optimization, Trajectory planning, Connected and automated vehicles, Intersection control
Cite this article:
Wu J, Qu X. Intersection control with connected and automated vehicles: a review. Journal of Intelligent and Connected Vehicles, 2022, 5(3): 260-269. https://doi.org/10.1108/JICV-06-2022-0023
Download citation
EndNote(RIS)
BibTeX

507

Views

38

Downloads

Citations

32

Crossref

N/A

WoS

24

Scopus

N/A

CSCD

Abstract Full text About this article
Purpose

This paper aims to review the studies on intersection control with connected and automated vehicles (CAVs).

Design/methodology/approach

The most seminal and recent research in this area is reviewed. This study specifically focuses on two categories: CAV trajectory planning and joint intersection and CAV control.

Findings

It is found that there is a lack of widely recognized benchmarks in this area, which hinders the validation and demonstration of new studies.

Originality/value

In this review, the authors focus on the methodological approaches taken to empower intersection control with CAVs. The authors hope the present review could shed light on the state-of-the-art methods, research gaps and future research directions.


menu
Abstract
Full text
Outline
About this article

Intersection control with connected and automated vehicles: a review

Show Author's information Jiaming Wu1( ), Xiaobo Qu2
Department of Architecture and Civil Engineering, Chalmers University of Technology, Gothenburg, Sweden
School of Vehicle and Mobility, Tsinghua University, Beijing, China

Abstract

Purpose

This paper aims to review the studies on intersection control with connected and automated vehicles (CAVs).

Design/methodology/approach

The most seminal and recent research in this area is reviewed. This study specifically focuses on two categories: CAV trajectory planning and joint intersection and CAV control.

Findings

It is found that there is a lack of widely recognized benchmarks in this area, which hinders the validation and demonstration of new studies.

Originality/value

In this review, the authors focus on the methodological approaches taken to empower intersection control with CAVs. The authors hope the present review could shed light on the state-of-the-art methods, research gaps and future research directions.

Keywords: Optimization, Trajectory planning, Connected and automated vehicles, Intersection control

References(73)

Agostinelli, F., McAleer, S., Shmakov, A. and Baldi, P. (2019), “Solving the Rubik’s cube with deep reinforcement learning and search”, Nature Machine Intelligence, Vol. 1 No. 8, pp. 356-363.
DOI Google Scholar

Ali, Y., Zheng, Z. and Haque, M.M. (2021), “Modelling lane-changing execution behaviour in a connected environment: a grouped random parameters with heterogeneity-in-means approach”, Communications in Transportation Research, Vol. 1, p. 100009.
DOI Google Scholar

Bai, Z., Hao, P., Shangguan, W., Cai, B. and Barth, M.J. (2022), “Hybrid reinforcement learning-based eco-driving strategy for connected and automated vehicles at signalized intersections”, IEEE Transactions on Intelligent Transportation Systems, Vol. 99, pp. 1-14.
Google Scholar

Cai, M., Xu, Q., Chen, C., Wang, J., Li, K., Wang, J. and Wu, X. (2022), “Formation control with lane preference for connected and automated vehicles in multi-lane scenarios”, Transportation Research Part C: Emerging Technologies, Vol. 136, p. 103513.
DOI Google Scholar

Cao, D., Wu, J., Wu, J., Kulcsár, B. and Qu, X. (2021), “A platoon regulation algorithm to improve the traffic performance of highway work zones”, Computer-Aided Civil and Infrastructure Engineering, Vol. 36 No. 7, pp. 941-956.
DOI Google Scholar

Chen, J., Wang, Z. and Tomizuka, M. (2018), “Deep hierarchical reinforcement learning for autonomous driving with distinct behaviors”, IEEE Intelligent Vehicles Symposium (IV), pp. 1239-1244.
DOI Google Scholar

Chen, R., Hu, J., Levin, M.W. and Rey, D. (2020), “Stability-based analysis of autonomous intersection management with pedestrians”, Transportation Research Part C: Emerging Technologies, Vol. 114, pp. 463-483.
DOI Google Scholar

Chen, C., Wang, J., Xu, Q., Wang, J. and Li, K. (2021), “Mixed platoon control of automated and human-driven vehicles at a signalized intersection: dynamical analysis and optimal control”, Transportation Research Part C: Emerging Technologies, Vol. 127, p. 103138.
DOI Google Scholar

Chowdhury, F.R., Wang, P. and Li, P. (2021), “Congestion-aware heterogeneous connected automated vehicles cooperative scheduling problems at intersections”, Journal of Intelligent Transportation Systems, Vol. 1, pp. 1-16.
DOI Google Scholar

Ding, C., Dai, R., Fan, Y., Zhang, Z. and Wu, X. (2021), “Collaborative control of traffic signal and variable guiding lane for isolated intersection under connected and automated vehicle environment”, Computer-Aided Civil and Infrastructure Engineering, Vol. 1, doi: 10.1111/mice.12780.
DOI Google Scholar

Dion, F., Rakha, H. and Kang, Y. -S. (2004), “Comparison of delay estimates at under-saturated and over-saturated pre-timed signalized intersections”, Transportation Research Part B: Methodological, Vol. 38 No. 2, pp. 99-122.
DOI Google Scholar
Dresner, K. and Stone, P. (2004), “Multiagent traffic management: a reservation-based intersection control Mechanis”, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, Vol. 3, IEEE Computer Society, New York, NY, pp. 530-537.
DOI

González, D., Pérez, J., Milanés, V. and Nashashibi, F. (2016), “A review of motion planning techniques for automated vehicles”, IEEE Transactions on Intelligent Transportation Systems, Vol. 17 No. 4, pp. 1135-1145.
DOI Google Scholar

Guillen-Perez, A. and Cano, M. -D. (2022), “Multi-Agent deep reinforcement learning to manage connected autonomous vehicles at tomorrows intersections”, IEEE Transactions on Vehicular Technology, Vol. 99, pp. 1-1.
Google Scholar

Guo, Q., Li, L. and Ban, X (J. ) (2019), “Urban traffic signal control with connected and automated vehicles: a survey”, Transportation Research Part C: Emerging Technologies, Vol. 101, pp. 313-334.
DOI Google Scholar

Guo, Y., Ma, J., Xiong, C., Li, X., Zhou, F. and Hao, W. (2019), “Joint optimization of vehicle trajectories and intersection controllers with connected automated vehicles: combined dynamic programming and shooting heuristic approach”, Transportation Research Part C: Emerging Technologies, Vol. 98, pp. 54-72.
DOI Google Scholar

Hadjigeorgiou, A. and Timotheou, S. (2022), “Real-time optimization of fuel-consumption and travel-time of CAVs for cooperative intersection crossing”, IEEE Transactions on Intelligent Vehicles, Vol. 99, pp. 1-1.
DOI Google Scholar

Haydari, A. and Yılmaz, Y. (2022), “Deep reinforcement learning for intelligent transportation systems: a survey”, IEEE Transactions on Intelligent Transportation Systems, Vol. 23 No. 1, pp. 11-32.
DOI Google Scholar

Jiang, H., Hu, J., An, S., Wang, M. and Park, B.B. (2017), “Eco approaching at an isolated signalized intersection under partially connected and automated vehicles environment”, Transportation Research Part C: Emerging Technologies, Vol. 79, pp. 290-307.
DOI Google Scholar

Larsson, J., Keskin, M.F., Peng, B., Kulcsár, B. and Wymeersch, H. (2021), “Pro-social control of connected automated vehicles in mixed-autonomy multi-lane highway traffic”, Communications in Transportation Research, Vol. 1, p. 100019.
DOI Google Scholar

Lee, J. and Park, B. (2012), “Development and evaluation of a cooperative vehicle intersection control algorithm under the connected vehicles environment”, IEEE Transactions on Intelligent Transportation Systems, Vol. 13 No. 1, pp. 81-90.
DOI Google Scholar

Lee, D. and Hess, D.J. (2020), “Regulations for on-road testing of connected and automated vehicles: assessing the potential for global safety harmonization”, Transportation Research Part A: Policy and Practice, Vol. 136, pp. 85-98.
DOI Google Scholar

Levin, M.W., Boyles, S.D. and Patel, R. (2016), “Paradoxes of reservation-based intersection controls in traffic networks”, Transportation Research Part A: Policy and Practice, Vol. 90, pp. 14-25.
DOI Google Scholar

Li, Z., Wu, Q., Yu, H., Chen, C., Zhang, G., Tian, Z.Z. and Prevedouros, P.D. (2019), “Temporal-spatial dimension extension-based intersection control formulation for connected and autonomous vehicle systems”, Transportation Research Part C: Emerging Technologies, Vol. 104, pp. 234-248.
DOI Google Scholar

Liang, X.S.I., Gayah, V.V. and Joyce, G. (2020), “An equitable traffic signal control scheme at isolated signalized intersections using connected vehicle technology”, Transportation Research Part C: Emerging Technologies, Vol. 110, pp. 81-97.
DOI Google Scholar

Liu, M., Zhao, J., Hoogendoorn, S. and Wang, M. (2022), “A single-layer approach for joint optimization of traffic signals and cooperative vehicle trajectories at isolated intersections”, Transportation Research Part C: Emerging Technologies, Vol. 134, p. 103459.
DOI Google Scholar

Liu, H., Flores, C.E., Spring, J., Shladover, S.E. and Lu, X. -Y. (2021), “Field assessment of intersection performance enhanced by traffic signal optimization and vehicle trajectory planning”, IEEE Transactions on Intelligent Transportation Systems, pp. 1-13.
Google Scholar

Lu, G., Shen, Z., Liu, X., Nie, Y., Z. and Marco, X. (2022), “Are autonomous vehicles better off without signals at intersections? A comparative computational study”, Transportation Research Part B: Methodological, Vol. 155, pp. 26-46.
DOI Google Scholar

Lukose, E., Levin, M.W. and Boyles, S.D. (2019), “Incorporating insights from signal optimization into reservation-based intersection controls”, Journal of Intelligent Transportation Systems, Vol. 23 No. 3, pp. 1-15.
DOI Google Scholar

Ma, C., Yu, C. and Yang, X. (2021), “Trajectory planning for connected and automated vehicles at isolated signalized intersections under mixed traffic environment”, Transportation Research Part C: Emerging Technologies, Vol. 130, p. 103309.
DOI Google Scholar

Ma, W., Li, J. and Yu, C. (2022), “Shared-phase-dedicated-lane based intersection control with mixed traffic of human-driven vehicles and connected and automated vehicles”, Transportation Research Part C: Emerging Technologies, Vol. 135, pp. 103509.
DOI Google Scholar

Ma, J., Li, X., Zhou, F., Hu, J. and Park, B.B. (2017), “Parsimonious shooting heuristic for trajectory design of connected automated traffic part Ⅱ: computational issues and optimization”, Transportation Research Part B: Methodological, Vol. 95, pp. 421-441.
DOI Google Scholar

Malikopoulos, A.A., Cassandras, C.G. and Zhang, Y.J. (2018), “A decentralized energy-optimal control framework for connected automated vehicles at signal-free intersections”, Automatica, Vol. 93, pp. 244-256.
DOI Google Scholar

Mirheli, A., Tajalli, M., Hajibabai, L. and Hajbabaie, A. (2019), “A consensus-based distributed trajectory control in a signal-free intersection”, Transportation Research Part C: Emerging Technologies, Vol. 100, pp. 161-176.
DOI Google Scholar

Mitrovic, N., Dakic, I. and Stevanovic, A. (2020), “Combined alternate-direction lane assignment and reservation-based intersection control”, IEEE Transactions on Intelligent Transportation Systems, Vol. 21 No. 4, pp. 1779-1789.
DOI Google Scholar

Montanino, M. and Punzo, V. (2021), “On string stability of a mixed and heterogeneous traffic flow: a unifying modelling framework”, Transportation Research Part B: Methodological, Vol. 144, pp. 133-154.
DOI Google Scholar

Mousa, S.R., Ishak, S., Mousa, R.M., Codjoe, J. and Elhenawy, M. (2020), “Deep reinforcement learning agent with varying actions strategy for solving the eco-approach and departure problem at signalized intersections”, Transportation Research Record: Journal of the Transportation Research Board, Vol. 2674 No. 8, pp. 119-131.
DOI Google Scholar

Olovsson, T., Svensson, T. and Wu, J. (2022), “Future connected vehicles: communications demands, privacy and cyber-security”, Communications in Transportation Research, Vol. 2, p. 100056.
DOI Google Scholar

Ortú zar, J.D. (2021), “Future transportation: sustainability, complexity and individualization of choices”, Communications in Transportation Research, Vol. 1, p. 100010.
DOI Google Scholar
Paden, B., Čáp, M., Yong, S. and Dmitry, Y. (2016), “A survey of motion planning and control techniques for self-driving urban vehicles”.
DOI

Peng, B., Keskin, M.F., Kulcsár, B. and Wymeersch, H. (2021), “Connected autonomous vehicles for improving mixed traffic efficiency in unsignalized intersections with deep reinforcement learning”, Communications in Transportation Research, Vol. 1, p. 100017.
DOI Google Scholar

Qu, X. and Wang, S. (2021), “Communications in transportation research: vision and scope”, Communications in Transportation Research, Vol. 1, p. 100001.
DOI Google Scholar

Rad, S.R., Farah, H., Taale, H., van Arem, B. and Hoogendoorn, S.P. (2021), “The impact of a dedicated lane for connected and automated vehicles on the behaviour of drivers of manual vehicles”, Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 82, pp. 141-153.
DOI Google Scholar

Rios-Torres, J. and Malikopoulos, A.A. (2017), “A survey on the coordination of connected and automated vehicles at intersections and merging at highway on-ramps”, IEEE Transactions on Intelligent Transportation Systems, Vol. 18 No. 5, pp. 1066-1077.
DOI Google Scholar

Shi, J., Qiao, F., Li, Q., Yu, L. and Hu, Y. (2018), “Application and evaluation of the reinforcement learning approach to eco-driving at intersections under infrastructure-to-vehicle communications”, Transportation Research Record: Journal of the Transportation Research Board, Vol. 2672 No. 25, pp. 89-98.
DOI Google Scholar

Soleimaniamiri, S., Ghiasi, A., Li, X. and Huang, Z. (2020), “An analytical optimization approach to the joint trajectory and signal optimization problem for connected automated vehicles”, Transportation Research Part C: Emerging Technologies, Vol. 120, p. 102759.
DOI Google Scholar

Sutton, R.S. and Barto, A.G. (2005), “Reinforcement learning: an introduction”, IEEE Transactions on Neural Networks, Vol. 16 No. 1, pp. 285-286.
DOI Google Scholar

Talebpour, A. and Mahmassani, H.S. (2016), “Influence of connected and autonomous vehicles on traffic flow stability and throughput”, Transportation Research Part C: Emerging Technologies, Vol. 71, pp. 143-163.
DOI Google Scholar

Tan, Z., Liu, F., Chan, H.K. and Gao, H.O. (2022), “Transportation systems management considering dynamic wireless charging electric vehicles: review and prospects”, Transportation Research Part E: Logistics and Transportation Review, Vol. 163, p. 102761.
DOI Google Scholar

Wadud, Z. and Mattioli, G. (2021), “Fully automated vehicles: a cost-based analysis of the share of ownership and mobility services, and its socio-economic determinants”, Transportation Research Part A: Policy and Practice, Vol. 151, pp. 228-244.
DOI Google Scholar

Wang, M., Daamen, W., Hoogendoorn, S.P. and Arem, B. (2014a), “Rolling horizon control framework for driver assistance systems part Ⅰ: mathematical formulation and non-cooperative systems”, Transportation Research Part C: Emerging Technologies, Vol. 40, pp. 271-289.
DOI Google Scholar

Wang, M., Daamen, W., Hoogendoorn, S.P. and Arem, B. (2014b), “Rolling horizon control framework for driver assistance systems part Ⅱ: cooperative sensing and cooperative control”, Transportation Research Part C: Emerging Technologies, Vol. 40, pp. 290-311.
DOI Google Scholar

Wang, C., Gong, S., Zhou, A., Li, T. and Peeta, S. (2019), “Cooperative adaptive cruise control for connected autonomous vehicles by factoring communication-related constraints”, Transportation Research Procedia, Vol. 38, pp. 242-262.
DOI Google Scholar

Wang, L., Wu, J., Li, X., Wu, Z. and Zhu, L. (2022a), “Longitudinal control for person-following robots”, Journal of Intelligent and Connected Vehicles, Vol. 5 No. 2, pp. 88-98.
DOI Google Scholar

Wang, S., Wang, Z., Jiang, R., Yan, R. and Du, L. (2022b), “Trajectory jerking suppression for mixed traffic flow at a signalized intersection: a trajectory prediction based deep reinforcement learning method”, IEEE Transactions on Intelligent Transportation Systems, pp. 1-12.
DOI Google Scholar

Wu, Y., Chen, H. and Zhu, F. (2019), “DCL-AIM: decentralized coordination learning of autonomous intersection management for connected and automated vehicles”, Transportation Research Part C: Emerging Technologies, Vol. 103, pp. 246-260.
DOI Google Scholar

Wu, J., Kulcsár, B., Ahn, S. and Qu, X. (2020), “Emergency vehicle lane pre-clearing: from microscopic cooperation to routing decision making”, Transportation Research Part B: Methodological, Vol. 141, pp. 223-239.
DOI Google Scholar

Wu, J., Ahn, S., Zhou, Y., Liu, P. and Qu, X. (2021), “The cooperative sorting strategy for connected and automated vehicle platoons”, Transportation Research Part C: Emerging Technologies, Vol. 123, p. 102986.
DOI Google Scholar

Wu, J., Liu, P., Qin, X., Zhou, H. and Yang, Z. (2019), “Developing an actuated signal control strategy to improve the operations of contraflow left-turn lane design at signalized intersections”, Transportation Research Part C: Emerging Technologies, Vol. 104, pp. 53-65.
DOI Google Scholar

Xu, L., Jin, S., Li, B. and Wu, J. (2022), “Traffic signal coordination control for arterials with dedicated CAV lanes”, Journal of Intelligent and Connected Vehicles, Vol. 5 No. 2, pp. 72-87.
DOI Google Scholar

Xu, B., Ban, X., Bian, Y., Li, W., Wang, J., Li, S. and Li, K. (2018), “Cooperative method of traffic signal optimization and speed control of connected vehicles at isolated intersections”, IEEE Transactions on Intelligent Transportation Systems, pp. 1-14.
Google Scholar

Xu, B., Ban, X.J., Bian, Y., Li, W., Wang, J., Li, S.E. and Li, K. (2019), “Cooperative method of traffic signal optimization and speed control of connected vehicles at isolated intersections”, IEEE Transactions on Intelligent Transportation Systems, Vol. 20 No. 4, pp. 1390-1403.
DOI Google Scholar

Yao, H. and Li, X. (2020), “Decentralized control of connected automated vehicle trajectories in mixed traffic at an isolated signalized intersection”, Transportation Research Part C: Emerging Technologies, Vol. 121, p. 102846.
DOI Google Scholar

Yao, H. and Li, X. (2021), “Lane-change-aware connected automated vehicle trajectory optimization at a signalized intersection with multi-lane roads”, Transportation Research Part C: Emerging Technologies, Vol. 129, p. 103182.
DOI Google Scholar

Yu, C., Sun, W., Liu, H.X. and Yang, X. (2019), “Managing connected and automated vehicles at isolated intersections: from reservation- to optimization-based methods”, Transportation Research Part B: Methodological, Vol. 122, pp. 416-435.
DOI Google Scholar

Yu, C., Feng, Y., Liu, H.X., Ma, W. and Yang, X. (2018), “Integrated optimization of traffic signals and vehicle trajectories at isolated urban intersections”, Transportation Research Part B: Methodological, Vol. 112, pp. 89-112.
DOI Google Scholar

Zhang, J., Tang, T.Q., Yan, Y. and Qu, X. (2021), “Eco-driving control for connected and automated electric vehicles at signalized intersections with wireless charging”, Applied Energy, Vol. 282, p. 116215.
DOI Google Scholar

Zheng, Z. (2021), “Reasons, challenges, and some tools for doing reproducible transportation research”, Communications in Transportation Research, Vol. 1, p. 100004.
DOI Google Scholar
Zhou, F., Li, X. and Methodological, M.J. (2017), “Parsimonious shooting heuristic for trajectory design of connected automated traffic part Ⅰ: theoretical analysis with generalized time geography”.
DOI

Zhou, Y., Wang, M. and Ahn, S. (2019), “Distributed model predictive control approach for cooperative car-following with guaranteed local and string stability”, Transportation Research Part B: Methodological, Vol. 128, pp. 69-86.
DOI Google Scholar

Zhou, M., Yu, Y. and Qu, X. (2020), “Development of an efficient driving strategy for connected and automated vehicles at signalized intersections: a reinforcement learning approach”, IEEE Transactions on Intelligent Transportation Systems, Vol. 21 No. 1, pp. 433-443.
DOI Google Scholar

Zhou, Y., Ahn, S., Chitturi, M. and Noyce, D.A. (2017), “Rolling horizon stochastic optimal control strategy for ACC and CACC under uncertainty”, Transportation Research Part C: Emerging Technologies, Vol. 83, pp. 61-76.
DOI Google Scholar

Zhou, Y., Ahn, S., Wang, M. and Hoogendoorn, S. (2020), “Stabilizing mixed vehicular platoons with connected automated vehicles: an H-infinity approach”, Transportation Research Part B: Methodological, Vol. 132, pp. 152-170.
DOI Google Scholar
Publication history
Copyright
Rights and permissions

Publication history

Received: 25 June 2022
Revised: 03 July 2022
Accepted: 03 July 2022
Published: 29 July 2022
Issue date: October 2022

Copyright

© 2022 Jiaming Wu and Xiaobo Qu. Published in Journal of Intelligent and Connected Vehicles. Published by Emerald Publishing Limited.

Rights and permissions

This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode

Return