Technical Papers
May 23, 2023

A Planning Control Strategy Based on Dynamic Safer Buffer to Avoid Traffic Collisions in an Emergency for CAVs at Nonsignalized Intersections

Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 8

Abstract

Intelligent traffic control at nonsignalized intersections under a connected and automated vehicle (CAV) environment can reduce traffic congestion, vehicle emissions [e.g., carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxide (NO)], but it is still not safe enough for an unpredictable emergency. For example, an accidental stop or slow down of a vehicle in the conflict area will cause secondary accidents of vehicles from other directions because they do not have enough distance to brake. Such risks have not been given sufficient attention in the literature. This paper proposed a safety guarantee mechanism of intersection control to avoid secondary accidents by adding a safe buffer area outside the conflict area of the intersection. To improve the traffic efficiency of intersections and reduce vehicle emissions under the mechanism, we proposed a heuristic vehicle trajectory planning control strategy based on vehicle kinematics. Three planning principles are applied to generate the microscopic trajectory for each vehicle: (1) the vehicle first decelerates and then accelerates before passing the intersection; (2) try not to change the running state of the vehicle; and (3) the vehicles do not collide with each other. Finally, to further improve efficiency, we proposed the concept of a dynamic safety area, which reduces the delay by self-adjusting the safety area to each vehicle’s trajectory. The paper selected three simulation scenarios of road intersections to validate our methodology. The simulation results show that the proposed control method with the safe buffer significantly reduces average vehicle delay and emissions compared to traditional signal control systems. Compared with the fixed buffer, in the dynamic safe buffer scenario, the average vehicle delay can be reduced by 66.94%, the throughput is increased by 18.75%, and CO2 emissions are reduced by 9.9%. The dynamic safe buffer can not only relax the safety concern of a nonsignalized intersection, which is not well addressed in the literature, but also has much better performance than the traditional intersection.

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Data Availability Statement

Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

This research was supported by the National Science Foundation of China (Grant No. 52232011) and the Sichuan Science and Technology Program (No. 2023YFH0083). The remaining errors are ours alone.

References

AASHTO. 2010. Highway safety manual. Washington, DC: AASHTO.
Ahn, H., and D. Del Vecchio. 2017. “Safety verification and control for collision avoidance at road intersections.” IEEE Trans. Autom. Control 63 (3): 630–642. https://doi.org/10.1109/TAC.2017.2729661.
Ahn, K., H. Rakha, A. Trani, and M. Van Aerde. 2002. “Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels.” J. Transp. Eng. 128 (2): 182–190. https://doi.org/10.1061/(ASCE)0733-947X(2002)128:2(182).
Andert, E., M. Khayatian, and A. Shrivastava. 2017. “Crossroads: Time-sensitive autonomous intersection management technique.” In Proc., 54th Annual Design Automation Conf. 2017, 1–6. Tempe, AZ: Arizona State Univ.
Aoki, S., and R. Rajkumar. 2018. “Dynamic intersections and self-driving vehicles.” In Proc., 2018  ACM/IEEE 9th Int. Conf. on Cyber-Physical Systems (ICCPS), 320–330. New York: IEEE.
Azimi, R., G. Bhatia, R. Rajkumar, and P. Mudalige. 2015. “Ballroom intersection protocol: Synchronous autonomous driving at intersections.” In Proc., 2015 IEEE 21st Int. Conf. on Embedded and Real-Time Computing Systems and Applications, 167–175. New York: IEEE.
Azimi, R., G. Bhatia, R. R. Rajkumar, and P. Mudalige. 2014. “Stip: Spatio-temporal intersection protocols for autonomous vehicles.” In Proc., 2014  ACM/IEEE Int. Conf. on Cyber-Physical Systems (ICCPS), 1–12. New York: IEEE.
Bashiri, M., and C. H. Fleming. 2017. “A platoon-based intersection management system for autonomous vehicles.” In Proc., 2017 IEEE Intelligent Vehicles Symp. (IV), 667–672. New York: IEEE.
Belkhouche, F. 2018. “Collaboration and optimal conflict resolution at an unsignalized intersection.” IEEE Trans. Intell. Transp. Syst. 20 (6): 2301–2312. https://doi.org/10.1109/TITS.2018.2867256.
Castañeda, K., O. Sánchez, R. F. Herrera, E. Pellicer, and H. Porras. 2021. “BIM-based traffic analysis and simulation at road intersection design.” Autom. Constr. 131 (Nov): 103911. https://doi.org/10.1016/j.autcon.2021.103911.
Chandra, V., and K. V. Kumar. 1997. “Reliability and safety analysis of fault tolerant and fail safe node for use in a railway signalling system.” Reliab. Eng. Syst. Saf. 57 (2): 177–183. https://doi.org/10.1016/S0951-8320(97)00020-3.
Choi, M., A. Rubenecia, and H. H. Choi. 2019. “Reservation-based traffic management for autonomous intersection crossing.” Int. J. Distrib. Sens. Netw. 15 (12): 1550147719895956. https://doi.org/10.1177/1550147719895956.
Clarke, R., S. Giddey, and S. Badwal. 2010. “Stand-alone PEM water electrolysis system for fail safe operation with a renewable energy source.” Int. J. Hydrogen Energy 35 (3): 928–935. https://doi.org/10.1016/j.ijhydene.2009.11.100.
Dresner, K., and P. Stone. 2004. “Multiagent traffic management: A reservation-based intersection control mechanism.” In Proc., 3rd Int. Joint Conf. on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004, 530–537. Washington, DC: IEEE Computer Society.
Dresner, K., and P. Stone. 2008. “A multiagent approach to autonomous intersection management.” J. Artif. Intell. Res. 31 (Mar): 591–656. https://doi.org/10.1613/jair.2502.
Eisenman, S. M., J. Josselyn, G. List, B. Persaud, C. Lyon, B. Robinson, M. Blogg, E. Waltman, and R. Troutbeck. 2004. Operational and safety performance of modern roundabouts and other intersection types. New York: New York State Department of Transportation Albany.
Elhadef, M. 2015. “An adaptable invanets-based intersection traffic control algorithm.” In Proc., 2015 IEEE Int. Conf. on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, 2387–2392. New York: IEEE.
Fagnant, D. J., and K. Kockelman. 2015. “Preparing a nation for autonomous vehicles: Opportunities, barriers and policy recommendations.” Transp. Res. Part A Policy Pract. 77 (Jul): 167–181.
Fang, Z., and D. Jingming. 2007. “Study on the application of fail-safe design principles in mining engineering and security technology.” In Proc., 2007’ Int. Symp. on Safety, 1095–1099. Jiaozuo, China: HeNan Polytechnice Univ.
Jeon, K., H. Hwang, S. Choi, S. Hwang, S. B. Choi, and K. Yi. 2012. “Development of a fail-safe control strategy based on evaluation scenarios for an fcev electronic brake system.” Int. J. Autom. Technol. 13 (7): 1067–1075. https://doi.org/10.1007/s12239-012-0109-1.
Jin, Q., G. Wu, K. Boriboonsomsin, and M. Barth. 2012. “Advanced intersection management for connected vehicles using a multi-agent systems approach.” In Proc., 2012 IEEE Intelligent Vehicles Symp., 932–937. New York: IEEE.
Jin, Q., G. Wu, K. Boriboonsomsin, and M. Barth. 2013. “Platoon-based multi-agent intersection management for connected vehicle.” In Proc., 16th Int. IEEE Conf. on Intelligent Transportation Systems (ITSC 2013), 1462–1467. New York: IEEE.
Karlaftis, M. G., S. P. Latoski, N. J. Richards, and K. C. Sinha. 1999. “Its impacts on safety and traffic management: An investigation of secondary crash causes.” J. Intell. Transp. Syst. 5 (1): 39–52. https://doi.org/10.1080/10248079908903756.
Katriniok, A., S. Kojchev, E. Lefeber, and H. Nijmeijer. 2018. “Distributed scenario model predictive control for driver aided intersection crossing.” In Proc., 2018 European Control Conf. (ECC), 1746–1752. New York: IEEE.
Katriniok, A., P. Sopasakis, M. Schuurmans, and P. Patrinos. 2019. “Nonlinear model predictive control for distributed motion planning in road intersections using panoc.” In Proc., 2019 IEEE 58th Conf. on Decision and Control, 5272–5278. New York: IEEE.
Khayatian, M., M. Mehrabian, E. Andert, R. Dedinsky, S. Choudhary, Y. Lou, and A. Shirvastava. 2020. “A survey on intersection management of connected autonomous vehicles.” ACM Trans. Cyber-Phys. Syst. 4 (4): 1–27. https://doi.org/10.1145/3407903.
Khayatian, M., M. Mehrabian, and A. Shrivastava. 2018. “Rim: Robust intersection management for connected autonomous vehicles.” In Proc., 2018 IEEE Real-Time Systems Symp., 35–44. New York: IEEE.
Kim, G.-H., K. Smith, J. Ireland, and A. Pesaran. 2012. “Fail-safe design for large capacity lithium-ion battery systems.” J. Power Sources 210 (Jul): 243–253. https://doi.org/10.1016/j.jpowsour.2012.03.015.
Kudarauskas, N. 2007. “Analysis of emergency braking of a vehicle.” Transport 22 (3): 154–159. https://doi.org/10.3846/16484142.2007.9638118.
Li, L., and F.-Y. Wang. 2006. “Cooperative driving at blind crossings using intervehicle communication.” IEEE Trans. Veh. Technol. 55 (6): 1712–1724. https://doi.org/10.1109/TVT.2006.878730.
Liu, B., Q. Shi, Z. Song, and A. El Kamel. 2019. “Trajectory planning for autonomous intersection management of connected vehicles.” Simul. Modell. Pract. Theory 90 (Jan): 16–30. https://doi.org/10.1016/j.simpat.2018.10.002.
Lu, G., Z. Shen, X. Liu, Y. M. Nie, and Z. Xiong. 2022. “Are autonomous vehicles better off without signals at intersections? A comparative computational study.” Transp. Res. Part B Methodol. 155 (Jan): 26–46. https://doi.org/10.1016/j.trb.2021.10.012.
Meng, Y., L. Li, F.-Y. Wang, K. Li, and Z. Li. 2018. “Analysis of cooperative driving strategies for nonsignalized intersections.” IEEE Trans. Veh. Technol. 67 (4): 2900–2911. https://doi.org/10.1109/TVT.2017.2780269.
Muzahid, A. J. M., S. F. Kamarulzaman, M. A. Rahman, and A. H. Alenezi. 2022. “Deep reinforcement learning-based driving strategy for avoidance of chain collisions and its safety efficiency analysis in autonomous vehicles.” IEEE Access 10 (Apr): 43303–43319. https://doi.org/10.1109/ACCESS.2022.3167812.
NHTSA (National Highway Traffic Safety Administration). 2019. Traffic safety facts 2017—A compilation of motor vehicle crash data. Washington, DC: NHTSA.
Park, H., A. Haghani, S. Samuel, and M. A. Knodler. 2018. “Real-time prediction and avoidance of secondary crashes under unexpected traffic congestion.” Accid. Anal. Prev. 112 (Mar): 39–49. https://doi.org/10.1016/j.aap.2017.11.025.
Qian, B., H. Zhou, F. Lyu, J. Li, T. Ma, and F. Hou. 2019. “Toward collision-free and efficient coordination for automated vehicles at unsignalized intersection.” IEEE Internet Things J. 6 (6): 10408–10420. https://doi.org/10.1109/JIOT.2019.2939180.
Qin, Y., H. Wang, and B. Ran. 2019. “Impact of connected and automated vehicles on passenger comfort of traffic flow with vehicle-to-vehicle communications.” KSCE J. Civ. Eng. 23 (2): 821–832. https://doi.org/10.1007/s12205-018-1990-6.
Sakaguchi, T., A. Uno, S. Kato, and S. Tsugawa. 2000. “Cooperative driving of automated vehicles with inter-vehicle communications.” In Proc., IEEE Intelligent Vehicles Symp. 2000 (Cat. No. 00TH8511), 516–521. New York: IEEE.
Sayed, T., and S. Zein. 1999. “Traffic conflict standards for intersections.” Transp. Plann. Technol. 22 (4): 309–323. https://doi.org/10.1080/03081069908717634.
Sayin, M. O., C.-W. Lin, S. Shiraishi, J. Shen, and T. Başar. 2018. “Information-driven autonomous intersection control via incentive compatible mechanisms.” IEEE Trans. Intell. Transp. Syst. 20 (3): 912–924. https://doi.org/10.1109/TITS.2018.2838049.
Sheu, J.-B., T. Liu, and J.-J. Lee. 2012. “On the fail-safe design of tendon-driven manipulators with redundant tendons.” J. Mech. Sci. Technol. 26 (6): 1911–1920. https://doi.org/10.1007/s12206-012-0409-4.
Shrestha, R., S. Y. Nam, R. Bajracharya, and S. Kim. 2020. “Evolution of v2x communication and integration of blockchain for security enhancements.” Electronics 9 (9): 1338. https://doi.org/10.3390/electronics9091338.
Sun, L., J. Tao, C. Li, S. Wang, and Z. Tong. 2018. “Microscopic simulation and optimization of signal timing based on multi-agent: A case study of the intersection in Tianjin.” KSCE J. Civ. Eng. 22 (Sep): 3373–3382. https://doi.org/10.1007/s12205-018-0528-2.
Uno, A., T. Sakaguchi, and S. Tsugawa. 1999. “A merging control algorithm based on inter-vehicle communication.” In Proc., 199 IEEE/IEEJ/JSAI Int. Conf. on Intelligent Transportation Systems (Cat. No. 99TH8383), 783–787. New York: IEEE.
Vlahogianni, E. I., M. G. Karlaftis, and F. P. Orfanou. 2012. “Modeling the effects of weather and traffic on the risk of secondary incidents.” J. Intell. Transp. Syst. 16 (3): 109–117. https://doi.org/10.1080/15472450.2012.688384.
Wang, H., Y. Huang, A. Khajepour, Y. Zhang, Y. Rasekhipour, and D. Cao. 2019. “Crash mitigation in motion planning for autonomous vehicles.” IEEE Trans. Intell. Transp. Syst. 20 (9): 3313–3323. https://doi.org/10.1109/TITS.2018.2873921.
Xu, C., P. Liu, B. Yang, and W. Wang. 2016. “Real-time estimation of secondary crash likelihood on freeways using high-resolution loop detector data.” Transp. Res. Part C Emerging Technol. 71 (Oct): 406–418. https://doi.org/10.1016/j.trc.2016.08.015.
Yang, H., Z. Wang, and K. Xie. 2017. “Impact of connected vehicles on mitigating secondary crash risk.” Int. J. Transp. Sci. Technol. 6 (3): 196–207. https://doi.org/10.1016/j.ijtst.2017.07.007.
Yang, H., Z. Wang, K. Xie, K. Ozbay, and M. Imprialou. 2018. “Methodological evolution and frontiers of identifying, modeling and preventing secondary crashes on highways.” Accid. Anal. Prev. 117 (Aug): 40–54. https://doi.org/10.1016/j.aap.2018.04.001.
Yang, K., S. I. Guler, and M. Menendez. 2016. “Isolated intersection control for various levels of vehicle technology: Conventional, connected, and automated vehicles.” Transp. Res. Part C Emerging Technol. 72 (Nov): 109–129. https://doi.org/10.1016/j.trc.2016.08.009.

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Go to Journal of Transportation Engineering, Part A: Systems
Journal of Transportation Engineering, Part A: Systems
Volume 149Issue 8August 2023

History

Received: May 7, 2022
Accepted: Mar 8, 2023
Published online: May 23, 2023
Published in print: Aug 1, 2023
Discussion open until: Oct 23, 2023

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Authors

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Graduate Student, School of Transportation and Logistics, Southwest Jiaotong Univ., Chengdu 611756, China. ORCID: https://orcid.org/0000-0002-7564-1262. Email: [email protected]
Xiaobo Liu
Professor, School of Transportation and Logistics, Southwest Jiaotong Univ., Chengdu 611756, China.
Mulin Xiao
Graduate Student, School of Foreign Languages, Chengdu Univ. of Technology, Chengdu 610095, China.
Associate Professor, School of Transportation and Logistics, Southwest Jiaotong Univ., Chengdu 611756, China (corresponding author). ORCID: https://orcid.org/0000-0002-4224-5490. Email: [email protected]

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