A New Algorithm Based on Dijkstra for Vehicle Path Planning Considering Intersection Attribute

Vehicle path planning is a key issue for car navigation systems. When path planning, considering the time spent at intersections is more in line with the actual situation, so it is of practical significance to study the path planning problem take account intersection attributes. In this article, we...

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Bibliographic Details
Published in:IEEE access Vol. 9; pp. 19761 - 19775
Main Authors: Zhu, Dan-Dan, Sun, Jun-Qing
Format: Journal Article
Language:English
Published: Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Ant colony optimization
Convergence
deterministic network
Dijkstra's algorithm
Heuristic algorithms
intersection attribute
Intersections
Machine learning algorithms
Navigation systems
Optimization
Particle swarm optimization
Path planning
Polynomials
reverse labeling Dijkstra algorithm
Roads
Run time (computers)
Search algorithms
Transportation
Travel time
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:Vehicle path planning is a key issue for car navigation systems. When path planning, considering the time spent at intersections is more in line with the actual situation, so it is of practical significance to study the path planning problem take account intersection attributes. In this article, we study the problem in a deterministic network, taking the minimization of travel time from the origin to the destination as the optimization goal. For this purpose, we construct a mathematical model for the problem. This paper proposes a reverse labeling Dijkstra algorithm (RLDA) based on traditional Dijkstra algorithm to solve the problem, it is proved that the correctness of the RLDA algorithm theoretically, and analyze that the RLDA algorithm has a lower polynomial time complexity. Finally, we selected the actual road network as the simulation experiment object to verify the effectiveness of the algorithm searching for the optimal path. And select 10 groups of networks of different sizes and conduct extensive experiments to compare the convergence efficiency and calculation speed between RLDA and PSO, GA, ACO, NNA, OPABRL. The statistical results show that the convergence rate of the RLDA algorithm is better than that of ACO, NNA, and GA. When the number of network nodes is less than 350, the algorithm has the smallest running time.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3053169