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Multiple-Vehicle Longitudinal Collision Mitigation by Coordinated Brake Control

Author

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  • Xiao-Yun Lu
  • Jianqiang Wang
  • Shengbo Eben Li
  • Yang Zheng

Abstract

Rear-end collision often leads to serious casualties and traffic congestion. The consequences are even worse for multiple-vehicle collision. Many previous works focused on collision warning and avoidance strategies of two consecutive vehicles based on onboard sensor detection only. This paper proposes a centralized control strategy for multiple vehicles to minimize the impact of multiple-vehicle collision based on vehicle-to-vehicle communication technique. The system is defined as a coupled group of vehicles with wireless communication capability and short following distances. The safety relationship can be represented as lower bound limit on deceleration of the first vehicle and upper bound on maximum deceleration of the last vehicle. The objective is to determine the desired deceleration for each vehicle such that the total impact energy is minimized at each time step. The impact energy is defined as the relative kinetic energy between a consecutive pair of vehicles (approaching only). Model predictive control (MPC) framework is used to formulate the problem to be constrained quadratic programming. Simulations show its effectiveness on collision mitigation. The developed algorithm has the potential to be used for progressive market penetration of connected vehicles in practice.

Suggested Citation

  • Xiao-Yun Lu & Jianqiang Wang & Shengbo Eben Li & Yang Zheng, 2014. "Multiple-Vehicle Longitudinal Collision Mitigation by Coordinated Brake Control," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-13, September.
  • Handle: RePEc:hin:jnlmpe:192175
    DOI: 10.1155/2014/192175
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    Cited by:

    1. Guo, Yinjia & Chen, Yanyan & Gu, Xin & Guo, Jifu & Zheng, Shuyan & Zhou, Yuntong, 2024. "Dynamic traffic graph based risk assessment of multivehicle lane change interaction scenarios," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 643(C).

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