Journal Home > Volume 29 , Issue 1
Tsinghua Science and Technology 2024, 29(1): 257-270 https://doi.org/10.26599/TST.2022.9010069
Open Access | Issue | Published: 21 August 2023

Theoretical Analysis of Cooperative Driving at Idealized Unsignalized Intersections

Show Author's Information Shen Li1,  Jiawei Zhang2,  Zhenwu Chen3( ),  Li Li2( )
Department of Civil Engineering, Tsinghua University, Beijing 100084, China
Department of Automation, Tsinghua University, Beijing 100084, China
Shenzhen Urban Transport Planning Center Co., Ltd., Shenzhen 518000, China
Keywords:
connected and automated vehicles, cooperative driving, unsignalized intersection
Cite this article:
Li S, Zhang J, Chen Z, et al. Theoretical Analysis of Cooperative Driving at Idealized Unsignalized Intersections. Tsinghua Science and Technology, 2024, 29(1): 257-270. https://doi.org/10.26599/TST.2022.9010069
Download citation
EndNote(RIS)
BibTeX

235

Views

19

Downloads

Citations

1

Crossref

2

WoS

1

Scopus

0

CSCD

Abstract Full text About this article

Cooperative driving is widely viewed as a promising method to better utilize limited road resources and alleviate traffic congestion. In recent years, several cooperative driving approaches for idealized traffic scenarios (i.e., uniform vehicle arrivals, lengths, and speeds) have been proposed. However, theoretical analyses and comparisons of these approaches are lacking. In this study, we propose a unified group-by-group zipper-style movement model to describe different approaches synthetically and evaluate their performance. We derive the maximum throughput for cooperative driving plans of idealized unsignalized intersections and discuss how to minimize the delay of vehicles. The obtained conclusions shed light on future cooperative driving studies.


menu
Abstract
Full text
Outline
About this article

Theoretical Analysis of Cooperative Driving at Idealized Unsignalized Intersections

Show Author's information Shen Li1, Jiawei Zhang2, Zhenwu Chen3( ), Li Li2( )
Department of Civil Engineering, Tsinghua University, Beijing 100084, China
Department of Automation, Tsinghua University, Beijing 100084, China
Shenzhen Urban Transport Planning Center Co., Ltd., Shenzhen 518000, China

Abstract

Cooperative driving is widely viewed as a promising method to better utilize limited road resources and alleviate traffic congestion. In recent years, several cooperative driving approaches for idealized traffic scenarios (i.e., uniform vehicle arrivals, lengths, and speeds) have been proposed. However, theoretical analyses and comparisons of these approaches are lacking. In this study, we propose a unified group-by-group zipper-style movement model to describe different approaches synthetically and evaluate their performance. We derive the maximum throughput for cooperative driving plans of idealized unsignalized intersections and discuss how to minimize the delay of vehicles. The obtained conclusions shed light on future cooperative driving studies.

Keywords: connected and automated vehicles, cooperative driving, unsignalized intersection

References(57)

[1]
L. Li, D. Wen, and D. Y. Yao, A survey of traffic control with vehicular communications, IEEE Trans. Intell. Transport. Syst., vol. 15, no. 1, pp. 425–432, 2014.
DOI Google Scholar
[2]
Q. Guo, L. Li, and X. Ban, Urban traffic signal control with connected and automated vehicles: A survey, Transp. Res. Part C: Emerg. Technol., vol. 101, pp. 313–334, 2019.
DOI Google Scholar
[3]
J. Zhang, C. Chang, H. Pei, X. Peng, Y. Guo, R. Lian, Z. Chen, and L. Li, CAVSim: A microscope traffic simulator for connected and automated vehicles environment, in Proc. 2022 IEEE 25th Int. Conf. Intelligent Transportation Systems (ITSC), Macau, China, 2022, pp. 3719–3724.
DOI Google Scholar
[4]
C. Chang, K. Zhang, J. Zhang, S. Li, and L. Li, Driving safety monitoring and warning for connected and automated vehicles via edge computing, in Proc. of the 2022 IEEE 25th Int. Conf. Intelligent Transportation Systems (ITSC), Macau, China, 2022, pp. 3940–3947.
DOI Google Scholar
[5]
S. Chen, J. Hu, Y. Shi, L. Zhao, and W. Li, A vision of C-V2X: Technologies, field testing, and challenges with Chinese development, IEEE Internet of Things J., vol. 7, no. 5, pp. 3872–3881, 2020.
DOI Google Scholar
[6]
H. Zhou, W. Xu, J. Chen, and W. Wang, Evolutionary V2X technologies toward the internet of vehicles: Challenges and opportunities, in Proc. IEEE, vol. 108, no. 2, pp. 308–323, 2020.
DOI Google Scholar
[7]
J. Zhang, C. Chang, X. Zeng, and L. Li, Multi-agent DRL-based lane change with right-of-way collaboration awareness, IEEE Trans. Intell. Transport. Syst., vol. 24, no. 1, pp. 854–869, 2023.
DOI Google Scholar
[8]
L. Li and F. Y. Wang, Cooperative driving at blind crossings using intervehicle communication, IEEE Trans. Veh. Technol., vol. 55, no. 6, pp. 1712–1724, 2006.
DOI Google Scholar
[9]
K. Dresner and P. Stone, A multiagent approach to autonomous intersection management, J. Artif. Intell. Res., vol. 31, no. 1, pp. 591–656, 2008.
DOI Google Scholar
[10]
Y. Meng, L. Li, F. Y. Wang, K. Li, and Z. Li, Analysis of cooperative driving strategies for nonsignalized intersections, IEEE Trans. Veh. Technol., vol. 67, no. 4, pp. 2900–2911, 2018.
DOI Google Scholar
[11]
H. Pei, J. Zhang, Y. Zhang, X. Pei, S. Feng, and L. Li, Fault-tolerant cooperative driving at signal-free intersections, IEEE Trans. Intell. Veh., vol. 8, no. 1, pp. 121–134, 2023.
DOI Google Scholar
[12]
H. Xu, Y. Zhang, L. Li, and W. Li, Cooperative driving at unsignalized intersections using tree search, IEEE Trans. Intell. Transport. Syst., vol. 21, no. 11, pp. 4563–4571, 2020.
DOI Google Scholar
[13]
H. Xu, C. G. Cassandras, L. Li, and Y. Zhang, Comparison of cooperative driving strategies for CAVs at signal-free intersections, IEEE Trans. Intell. Transport. Syst., vol. 23, no. 7, pp. 7614–7627, 2022.
DOI Google Scholar
[14]
J. Zhang, H. Pei, X. Ban, and L. Li, Analysis of cooperative driving strategies at road network level with macroscopic fundamental diagram, Transp. Res. Part C: Emerg. Technol., vol. 135, p. 103503, 2022.
DOI Google Scholar
[15]
S. Ilgin Guler, M. Menendez, and L. Meier, Using connected vehicle technology to improve the efficiency of intersections, Transp. Res. Part C: Emerg. Technol., vol. 46, pp. 121–131, 2014.
DOI Google Scholar
[16]
B. Xu, S. E. Li, Y. Bian, S. Li, X. J. Ban, J. Wang, and K. Li, Distributed conflict-free cooperation for multiple connected vehicles at unsignalized intersections, Transp. Res. Part C: Emerg. Technol., vol. 93, pp. 322–334, 2018.
DOI Google Scholar
[17]
H. Xu, Y. Zhang, C. G. Cassandras, L. Li, and S. Feng, A bi-level cooperative driving strategy allowing Lane changes, Transp. Res. Part C: Emerg. Technol., vol. 120, p. 102773, 2020.
DOI Google Scholar
[18]
J. Zhang, Z. Li, L. Li, Y. Li, and H. Dong, A bi-level cooperative operation approach for AGV based automated valet parking, Transp. Res. Part C: Emerg. Technol., vol. 128, p. 103140, 2021.
DOI Google Scholar
[19]
L. Chen and C. Englund, Cooperative intersection management: A survey, IEEE Trans. Intell. Transport. Syst., vol. 17, no. 2, pp. 570–586, 2016.
DOI Google Scholar
[20]
Z. He, L. Zheng, L. Lu, and W. Guan, Erasing lane changes from roads: A design of future road intersections, IEEE Trans. Intell. Veh., vol. 3, no. 2, pp. 173–184, 2018.
DOI Google Scholar
[21]
Z. Chen and X. Li, Designing corridor systems with modular autonomous vehicles enabling station-wise docking: Discrete modeling method, Transp. Res. Part E: Logist. Transp. Rev., vol. 152, p. 102388, 2021.
DOI Google Scholar
[22]
X. Lin, M. Li, Z. J. M. Shen, Y. Yin, and F. He, Rhythmic control of automated traffic-part II: Grid network rhythm and online routing, Transp. Sci., vol. 55, no. 5, pp. 988–1009, 2021.
DOI Google Scholar
[23]
X. Shi and X. Li, Operations design of modular vehicles on an oversaturated corridor with first-in, first-out passenger queueing, Transp. Sci., vol. 55, no. 5, pp. 1187–1205, 2021.
DOI Google Scholar
[24]
Q. He, K. L. Head, and J. Ding, PAMSCOD: Platoon-based arterial multi-modal signal control with online data, Procedia Soc. and Behav. Sci., vol. 17, pp. 462–489, 2011.
DOI Google Scholar
[25]
X. F. Xie, S. F. Smith, L. Lu, and G. J. Barlow, Schedule-driven intersection control, Transp. Res. Part C: Emerg. Technol., vol. 24, pp. 168–189, 2012.
DOI Google Scholar
[26]
H. Liu, X. Y. Lu, and S. E. Shladover, Traffic signal control by leveraging Cooperative Adaptive Cruise Control (CACC) vehicle platooning capabilities, Transp. Res. Part C: Emerg. Technol., vol. 104, pp. 390–407, 2019.
DOI Google Scholar
[27]
H. Xu, S. Feng, Y. Zhang, and L. Li, A grouping-based cooperative driving strategy for CAVs merging problems, IEEE Trans. Veh. Technol., vol. 68, no. 6, pp. 6125–6136, 2019.
DOI Google Scholar
[28]
G. Lu, Z. Shen, X. Liu, M. Nie, and Z. Xiong, Are autonomous vehicles better off without signals at intersections? A comparative computational study, Transp. Res. Part B: Methodol., vol. 155, pp. 26–46, 2022.
DOI Google Scholar
[29]
Q. Ge, Q. Sun, Z. Wang, S. E. Li, Z. Gu, S. Zheng, and L. Liao, Real-time coordination of connected vehicles at intersections using graphical mixed integer optimization, IET Intell. Trans. Syst., vol. 15, no. 6, pp. 795–807, 2021.
DOI Google Scholar
[30]
M. W. Levin, S. D. Boyles, and R. Patel, Paradoxes of reservation-based intersection controls in traffic networks, Transp. Res. Part A: Policy Pract., vol. 90, pp. 14–25, 2016.
DOI Google Scholar
[31]
M. W. Levin and D. Rey, Conflict-point formulation of intersection control for autonomous vehicles, Transp. Res. Part C: Emerg. Technol., vol. 85, pp. 528–547, 2017.
DOI Google Scholar
[32]
Y. Wu, H. Chen, and F. Zhu, DCL-AIM: Decentralized coordination learning of autonomous intersection management for connected and automated vehicles, Transp. Res. Part C: Emerg. Technol., vol. 103, pp. 246–260, 2019.
DOI Google Scholar
[33]
E. Lukose, M. W. Levin, and S. D. Boyles, Incorporating insights from signal optimization into reservation-based intersection controls, J. Intell. Transp. Syst., vol. 23, no. 3, pp. 250–264, 2019.
DOI Google Scholar
[34]
A. Stevanovic and N. Mitrovic, Combined alternate-direction lane assignment and reservation-based intersection control, in Proc. 2018 21st Int. Conf. Intelligent Transportation Systems (ITSC), Maui, HI, USA, 2018, pp. 14–19.
DOI Google Scholar
[35]
R. Tachet, P. Santi, S. Sobolevsky, L. I. Reyes-Castro, E. Frazzoli, D. Helbing, and C. Ratti, Revisiting street intersections using slot-based systems, PLoS ONE, vol. 11, no. 3, p. e0149607, 2016.
DOI Google Scholar
[36]
S. Li, K. Shu, Y. Zhou, D. Cao, and B. Ran, Cooperative critical turning point-based decision-making and planning for CAVH intersection management system, IEEE Trans. Intell. Transport. Syst., vol. 23, no. 8, pp. 11062–11072, 2022.
DOI Google Scholar
[37]
L. Chai, B. Cai, W. Shangguan, J. Wang, and H. Wang, Connected and autonomous vehicles coordinating approach at intersection based on space-time slot, Transportmetrica A: Transport Science, vol. 14, no. 10, pp. 929–951, 2018.
DOI Google Scholar
[38]
X. Chen, M. Li, X. Lin, Y. Yin, and F. He, Rhythmic control of automated traffic-part I: Concept and properties at isolated intersections, Transp. Sci., vol. 55, no. 5, pp. 969–987, 2021.
DOI Google Scholar
[39]
W. Zhao, D. Ngoduy, S. Shepherd, R. Liu, and M. Papageorgiou, A platoon based cooperative eco-driving model for mixed automated and human-driven vehicles at a signalised intersection, Transp. Res. Part C: Emerg. Technol., vol. 95, pp. 802–821, 2018.
DOI Google Scholar
[40]
C. Chen, J. Wang, Q. Xu, J. Wang, and K. Li, Mixed platoon control of automated and human-driven vehicles at a signalized intersection: Dynamical analysis and optimal control, Transp. Res. Part C: Emerg. Technol., vol. 127, p. 103138, 2021.
DOI Google Scholar
[41]
Y. Yin, Robust optimal traffic signal timing, Transp. Res. Part B: Methodol., vol. 42, no. 10, pp. 911–924, 2008.
DOI Google Scholar
[42]
Z. Li, Q. Wu, H. Yu, C. Chen, G. Zhang, Z. Z. Tian, and P. D. Prevedouros, Temporal-spatial dimension extension-based intersection control formulation for connected and autonomous vehicle systems, Transp. Res. Part C: Emerg. Technol., vol. 104, pp. 234–248, 2019.
DOI Google Scholar
[43]
C. Yu, W. Ma, and X. Yang, A time-slot based signal scheme model for fixed-time control at isolated intersections, Transp. Res. Part B: Methodol., vol. 140, pp. 176–192, 2020.
DOI Google Scholar
[44]
S. E. Shladover, D. Su, and X. Y. Lu, Impacts of cooperative adaptive cruise control on freeway traffic flow, Transp. Res. Rec., vol. 2324, no. 1, pp. 63–70, 2012.
DOI Google Scholar
[45]
H. Yu, R. Jiang, Z. He, Z. Zheng, L. Li, R. Liu, and X. Chen, Automated vehicle-involved traffic flow studies: A survey of assumptions, models, speculations, and perspectives, Transp. Res. Part C: Emerg. Technol., vol. 127, p. 103101, 2021.
DOI Google Scholar
[46]
V. Milanés, S. E. Shladover, J. Spring, C. Nowakowski, H. Kawazoe, and M. Nakamura, Cooperative adaptive cruise control in real traffic situations, IEEE Trans. Intell. Transport. Syst., vol. 15, no. 1, pp. 296–305, 2014.
DOI Google Scholar
[47]
A. Uno, T. Sakaguchi, and S. Tsugawa, A merging control algorithm based on inter-vehicle communication, in Proc. 199 IEEE/IEEJ/JSAI Int. Conf. Intelligent Transportation Systems, Tokyo, Japan, 1999, pp. 783–787.
Google Scholar
[48]
T. Sakaguchi, A. Uno, S. Kato, and S. Tsugawa, Cooperative driving of automated vehicles with inter-vehicle communications, in Proc. IEEE Intelligent Vehicles Symp. 2000, Dearborn, MI, USA, 2000, pp. 516–521.
Google Scholar
[49]
L. Zhang and D. Levinson, Balancing efficiency and equity of ramp meters, J. Transp. Eng., vol. 131, no. 6, pp. 477–481, 2005.
DOI Google Scholar
[50]
L. Zhang and D. Levinson, Ramp metering and freeway bottleneck capacity, Transp. Res. Part A: Policy Pract., vol. 44, no. 4, pp. 218–235, 2010.
DOI Google Scholar
[51]
Q. Tian, H. J. Huang, H. Yang, and Z. Gao, Efficiency and equity of ramp control and capacity allocation mechanisms in a freeway corridor, Procedia Soc. Behav. Sci., vol. 17, pp. 509–531, 2011.
DOI Google Scholar
[52]
X. Liang, S. I. Guler, and V. V. Gayah, An equitable traffic signal control scheme at isolated signalized intersections using connected vehicle technology, Transp. Res. Part C: Emerg. Technol., vol. 110, pp. 81–97, 2020.
DOI Google Scholar
[53]
H. Pei, S. Feng, Y. Zhang, and D. Yao, A cooperative driving strategy for merging at on-ramps based on dynamic programming, IEEE Trans. Veh. Technol., vol. 68, no. 12, pp. 11646–11656, 2019.
DOI Google Scholar
[54]
J. Ding, H. Peng, Y. Zhang, and L. Li, Penetration effect of connected and automated vehicles on cooperative on-ramp merging, IET Intell. Transp. Syst., vol. 14, no. 1, pp. 56–64, 2020.
DOI Google Scholar
[55]
S. Feng, Z. Song, Z. Li, Y. Zhang, and L. Li, Robust platoon control in mixed traffic flow based on tube model predictive control, IEEE Trans. Intell. Veh., vol. 6, no. 4, pp. 711–722, 2021.
DOI Google Scholar
[56]
J. Ge, H. Xu, J. Zhang, Y. Zhang, D. Yao, and L. Li, Heterogeneous driver modeling and corner scenarios sampling for automated vehicles testing, J. Adv. Transp., vol. 2022, p. 8655514, 2022.
DOI Google Scholar
[57]
J. Zhang, S. Li, and L. Li, Coordinating CAV swarms at intersections with a deep learning model, IEEE Trans. Intell. Transport. Syst., .
DOI Google Scholar
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 08 September 2022
Revised: 09 November 2022
Accepted: 29 December 2022
Published: 21 August 2023
Issue date: February 2024

Copyright

© The author(s) 2024.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 52272420), the Science and Technology Innovation Committee of Shenzhen (No. CJGJZD20200617102801005), and the Tsinghua-Toyota Joint Research Institution.

Rights and permissions

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

Return