Trajectory Optimization for a Connected Automated Traffic Stream: Comparison Between an Exact Model and Fast Heuristics

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Xu, ZhigangChangan University, Peoples R China (author)

Trajectory Optimization for a Connected Automated Traffic Stream: Comparison Between an Exact Model and Fast Heuristics

Article/chapterEnglish2021

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2021

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LIBRIS-ID:oai:research.chalmers.se:dd3d810f-94ce-48f2-ac55-1be7403ec793
https://research.chalmers.se/publication/524228URI
https://doi.org/10.1109/TITS.2020.2978382DOI

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Language:English
Summary in:English

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Numerous fast heuristic algorithms, including shooting heuristics (SH), have been developed for real-time trajectory optimization, although their optimality has not yet been quantified. This paper compares the performance between fast heuristics and exact optimization models. We investigate a core trajectory optimization problem as a building block for numerous trajectory optimization problems, i.e., guiding movements of connected automated vehicles on a one-lane highway when the arrival and departure times and velocity are given. To apply the SH algorithm to this problem, we adapt it to a fast-simplified shooting heuristic (FSSH) model to solve the trajectory smoothing problems with different arrival and departure velocities. An exact trajectory optimization (ETO) model is formulated that takes the vehicle position and velocity as the decision variables, and the fuel consumption and driving comfort as the objective function. The constraints of the model are based on the limits and safety of the vehicle dynamics between consecutive vehicles. We demonstrate the convexity of the ETO objective function, ensuring the solvability of the ETO model at the true optimum using gradient descent algorithms supplied by the MATLAB optimization toolbox. Six groups of numerical experiments using different input parameters and one experiment using real Next Generation Simulation (NGSIM) data are conducted. ETO can improve the objective values by a few to tens of percentage points. However, FSSH achieves a greater solution efficiency with an average solution time of less than 0.1 s compared to similar to 450 s for ETO.

Subject headings and genre

NATURVETENSKAP Matematik Beräkningsmatematik hsv//swe
NATURAL SCIENCES Mathematics Computational Mathematics hsv//eng
TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik Reglerteknik hsv//swe
ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering Control Engineering hsv//eng
TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik Signalbehandling hsv//swe
ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering Signal Processing hsv//eng
Connected automated vehicle
speed control
nonlinear programming
shooting heuristics
fuel economy
trajectory optimization

Added entries (persons, corporate bodies, meetings, titles ...)

Wang, YuUniversity of South Florida (author)
Wang, GuanqunChangan University, Peoples R China (author)
Li, XiaopengUniversity of South Florida (author)
Bertini, Robert L.University of South Florida (author)
Qu, Xiaobo,1983Chalmers tekniska högskola,Chalmers University of Technology(Swepub:cth)xiaobo (author)
Zhao, XiangmoChangan University, Peoples R China (author)
Changan University, Peoples R ChinaUniversity of South Florida (creator_code:org_t)

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In:IEEE Transactions on Intelligent Transportation Systems22:5, s. 2969-29781524-90501558-0016

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By the author/editor: Xu, Zhigang; Wang, Yu; Wang, Guanqun; Li, Xiaopeng; Bertini, Robert ...; Qu, Xiaobo, 1983; show more...; Zhao, Xiangmo; show less...

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NATURAL SCIENCES: NATURAL SCIENCES; and Mathematics; and Computational Ma ...

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Electrical Engin ...; and Control Engineer ...

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Electrical Engin ...; and Signal Processin ...

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