Vehicle Platoon Control in High-Latency Wireless Communications Environment

Zhou, Hao; Saigal, Romesh; Dion, François; Yang, Li

doi:10.3141/2324-10

Cited by 35 publications

(19 citation statements)

References 16 publications

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“…According to a guideline published by the Federal Highway Administration [41], suggested maximum acceleration and deceleration are 3.04 m/s 2 and −4.5 m/s 2 , respectively. Several recent studies have employed some relaxed values considering the advancement of the vehicle technologies, in which the thresholds ranged from 4.0 m/s 2 to 8.0 m/s 2 [42], [43]. Since the vehicle considered here are automated, the maximum acceleration threshold was assigned to be 4.5 m/s 2 and the maximum deceleration was adopted as the guideline taking account the safety and comfortable driving behavior.…”

Section: B Experimental Set-upmentioning

confidence: 99%

Optimal Control for Speed Harmonization of Automated Vehicles

Malikopoulos

Hong

Park

et al. 2019

IEEE Trans. Intell. Transport. Syst.

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This article addresses the problem of controlling the speed of a number of automated vehicles before they enter a speed reduction zone on a freeway. We formulate the control problem and provide an analytical, closed-form solution that can be implemented in real time. The solution yields the optimal acceleration/deceleration of each vehicle under the hard safety constraint of rear-end collision avoidance. The effectiveness of the solution is evaluated through a microscopic simulation testbed and it is shown that the proposed approach significantly reduces both fuel consumption and travel time. In particular, for three different traffic volume levels, fuel consumption for each vehicle is reduced by 19-22% compared to the baseline scenario, in which human-driven vehicles are considered, by 12-17% compared to the variable speed limit algorithm, and by 18-34% compared to the vehicular-based speed harmonization (SPD-HARM) algorithm. Similarly, travel time is improved by 26-30% compared to the baseline scenario, by 3-19% compared to the VSL algorithm, and by 31-39% compared to the vehicularbased SPD-HARM algorithm. His research spans several fields, including analysis, optimization, and control of cyber-physical systems; decentralized systems; and stochastic scheduling and resource allocation problems. The emphasis is on applications related to sociotechnical systems, energy efficient mobility systems, and sustainable systems. He is currently an Associate Editor of the IEEE Transactions on Intelligent Vehicles and IEEE Transactions on Intelligent Transportation Systems. He is a member of SIAM and a Fellow of the ASME. Seongah Hong received the B.S. degree in Urban Planning and Engineering from Yonsei University, Seoul, Korea in 2012 and the M.S. from the Civil Engineering in the University of Minnesota, Duluth, MN in 2014, and a Ph.D. degree in the Civil and Environmental Engineering Department at the University of Virginia, Charlottesville, VA, USA. She has participated in various research project about traffic operations, intelligent transportation systems, connected automated vehicles applications, cybersecurity and monitoring system, and sustainable traffic control devices. She has conducted extensive work of modeling and analyzing the effectiveness of the traffic operations applications with the customized control algorithms under diverse scenarios.

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Section: B Experimental Set-upmentioning

confidence: 99%

Optimal Control for Speed Harmonization of Automated Vehicles

Malikopoulos

Hong

Park

et al. 2019

IEEE Trans. Intell. Transport. Syst.

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“…For generalpurpose vehicles, 3.0 m/s 2 and -4.5 m/s 2 are the recommended maximum acceleration and minimum deceleration settings. However, considering the presence of automated vehicle technologies, the maximum acceleration threshold here is relaxed to 4.5 m/s 2 ; see Dowling et al (2004); Lee et al (2013); Zhou et al (2012).…”

Section: Simulation Frameworkmentioning

confidence: 99%

Optimal Control of Connected and Automated Vehicles at Roundabouts: An Investigation in a Mixed-Traffic Environment

2018

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Connectivity and automation in vehicles provide the most intriguing opportunity for enabling users to better monitor transportation network conditions and make better operating decisions to improve safety and reduce pollution, energy consumption, and travel delays. This paper investigates the implications of optimally coordinating vehicles that are wirelessly connected to each other in roundabouts to achieve a smooth traffic flow. We apply an optimization framework and an analytical solution that allows optimal coordination of vehicles for merging in such traffic scenario. The effectiveness of the proposed approach is validated through simulation and it is shown that fully coordinated vehicles reduce total travel time by 51% and fuel consumption by 35%. Furthermore, we study the influence of vehicle coordination in a mixed-traffic environment and compare the network performance under different market penetration rates of connected and automated vehicles (CAVs). For this particular study with near-capacity demand, due to extremely unstable traffic, the results show that even with high penetration of CAVs (e.g., 80%), travel time and fuel savings are much less than a network of CAVs.

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“…In [9], a robust non‐linear platoon control system with the disturbance observer is proposed, and this proposed non‐linear platoon control system can effectively handle with the effect of the lumped disturbance and overcome the sensing range limitation. In order to solve the problems of high latency in the wireless communications of the platoon control system, a decentralised model predictive platoon control scheme is proposed in [10].…”

Section: Introductionmentioning

confidence: 99%