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Time-Dependent Vehicle Speed Variation Based Global Path Planning for Intelligent Connected Vehicles
  • Sihao Chen 1, 2,   
  • Zhenfeng Wang 1, 2,   
  • Zhengbai Liu 3,   
  • Xianyi Yang 1, 2,   
  • Heng Wang 1, 2, *

Received: 08 May 2023 | Accepted: 30 May 2023 | Published: 21 Jun 2023

Abstract

When an intelligent connected vehicle (ICV) autonomously completes an intelligent driving assignment, the decision planning layer needs to plan an optimal path from the starting location to the target location for the vehicle, which is referred to as global path planning (GPP) for the ICV. For the GPP of ICVs undertaking long-distance and multi-location driving assignments, a fixed open travelling salesman problem (TSP) was constructed in conjunction with travel time analysis. To better address this issue, a genetic annealing algorithm (GAA) was proposed, and corresponding simulations were conducted using genetic algorithm, ant colony algorithm, and GAA respectively. Based on the optimization processes and results, the GAA outperformed the traditional genetic algorithm and ant colony algorithm in tackling this issue. Therefore, the method proposed in this paper can be applied to the global path planning platform for intelligent networked vehicles.

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DOI
10.53941/ijamm.2023.100005
Issue
Volume 2, Issue 2
How to Cite
Chen, S.; Wang, Z.; Liu, Z.; Yang, X.; Wang, H. Time-Dependent Vehicle Speed Variation Based Global Path Planning for Intelligent Connected Vehicles. International Journal of Automotive Manufacturing and Materials 2023, 2 (2), 5. https://doi.org/10.53941/ijamm.2023.100005.
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