Dynamic traffic shockwave speed estimation in connected vehicle environment

Li, Jiangchen; Wang, Chenhao; He, Shuxian; Qiu, Tony Z.

doi:10.1109/ictis.2017.8047742

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…Luo et al [22] proved that vehicles could perform real-time calculations and update the lane-changing track before completing lane changing. Li et al [15] studied the problem of simultaneous lane changing of multiple vehicles through the cooperation of multiple vehicles. Lu et al [26] subsequently developed a mathematical model with safety and car-following constraints.…”

Section: Review Of Vehicle Trajectory Optimization Modelsmentioning

confidence: 99%

“…Microsimulation models (cellular automata model and following model) have been used to develop the mixed traffic flow basic diagram and the influence of automatic driving on traffic flow [13]. Due to the difficulty in carrying out real vehicle experiments, the research on the traffic flow theory of connected and automated vehicles (CAV) is mainly focused on the longitudinal control technology of assisted driving, such as adaptive cruise and coadaptive cruise systems [14], as well as the road intersection management of AVs [15]. Studies have also been conducted to smooth highway traffic by controlling individual vehicles [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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Automatic Vehicles’ Trajectories Optimization on Highway Exclusive Lanes

Chen

Ruan

Gou

2022

Journal of Advanced Transportation

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The rapid development of V2X communication has made it possible to optimize and control the trajectories of vehicles from the whole traffic flow’s perspective and improve traffic performance. Therefore, this paper discusses the trajectories management problem on highway facilitated with lanes exclusively for autonomous vehicles (AVs). The paper proposes a model that aims to search for optimal trajectories and minimize total travel time for AVs with multiple initial and target states while averting crashes and conforming to vehicles’ kinetic. Dividing the time zone into discrete pieces, the model is analyzed as a large-scale discrete problem influenced by the randomness of the sequence of vehicles. A two-phase algorithm combined with upper evolution strategies and lower dynamic programming is developed to diminish stochastics and reduce computation step by step and solve the trajectories optimization model. Numerical experiments validate that the proposed method is capable of generating optimal trajectories for multiple AVs and approaching to system optimum by simultaneously solving all the spatial and temporal values of the trajectories. The two-phase algorithm can be applied efficiently in practice to obtain a feasible approximate solution for trajectories optimization by presetting appropriate algorithm parameters.

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Section: Review Of Vehicle Trajectory Optimization Modelsmentioning

confidence: 99%