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LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00004077naa a2200457 4500
001oai:research.chalmers.se:dd3d810f-94ce-48f2-ac55-1be7403ec793
003SwePub
008210603s2021 | |||||||||||000 ||eng|
0247 a https://research.chalmers.se/publication/5242282 URI
0247 a https://doi.org/10.1109/TITS.2020.29783822 DOI
040 a (SwePub)cth
041 a engb eng
042 9 SwePub
072 7a art2 swepub-publicationtype
072 7a ref2 swepub-contenttype
1001 a Xu, Zhigangu Changan University, Peoples R China4 aut
2451 0a Trajectory Optimization for a Connected Automated Traffic Stream: Comparison Between an Exact Model and Fast Heuristics
264 1c 2021
520 a 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.
650 7a NATURVETENSKAPx Matematikx Beräkningsmatematik0 (SwePub)101052 hsv//swe
650 7a NATURAL SCIENCESx Mathematicsx Computational Mathematics0 (SwePub)101052 hsv//eng
650 7a TEKNIK OCH TEKNOLOGIERx Elektroteknik och elektronikx Reglerteknik0 (SwePub)202022 hsv//swe
650 7a ENGINEERING AND TECHNOLOGYx Electrical Engineering, Electronic Engineering, Information Engineeringx Control Engineering0 (SwePub)202022 hsv//eng
650 7a TEKNIK OCH TEKNOLOGIERx Elektroteknik och elektronikx Signalbehandling0 (SwePub)202052 hsv//swe
650 7a ENGINEERING AND TECHNOLOGYx Electrical Engineering, Electronic Engineering, Information Engineeringx Signal Processing0 (SwePub)202052 hsv//eng
653 a Connected automated vehicle
653 a speed control
653 a nonlinear programming
653 a shooting heuristics
653 a fuel economy
653 a trajectory optimization
7001 a Wang, Yuu University of South Florida4 aut
7001 a Wang, Guanqunu Changan University, Peoples R China4 aut
7001 a Li, Xiaopengu University of South Florida4 aut
7001 a Bertini, Robert L.u University of South Florida4 aut
7001 a Qu, Xiaobo,d 1983u Chalmers tekniska högskola,Chalmers University of Technology4 aut0 (Swepub:cth)xiaobo
7001 a Zhao, Xiangmou Changan University, Peoples R China4 aut
7102 a Changan University, Peoples R Chinab University of South Florida4 org
7730 t IEEE Transactions on Intelligent Transportation Systemsg 22:5, s. 2969-2978q 22:5<2969-2978x 1524-9050x 1558-0016
8564 8u https://research.chalmers.se/publication/524228
8564 8u https://doi.org/10.1109/TITS.2020.2978382

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