A Hybrid Path Planning Algorithm Based on Simulated Annealing Particle Swarm for The Self-driving Car

In order to improve the safety of the self-driving cars for the purpose of using the particle swarm algorithm alone in path planning of the self-driving cars, a hybrid path planning algorithm based on simulated annealing algorithm and particle swarm optimization is proposed. The hybrid optimization...

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Bibliographic Details
Published in2018 International Computers, Signals and Systems Conference (ICOMSSC) pp. 696 - 700
Main Authors Yin, Jinkai, Fu, Weiping
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2018
Subjects
Autonomous automobiles
Convergence
Hybrid algorithm
Optimization
Particle swarm
Particle swarm optimization
Path planning
Safety
Simulated annealing
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DOI10.1109/ICOMSSC45026.2018.8941726

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Summary:In order to improve the safety of the self-driving cars for the purpose of using the particle swarm algorithm alone in path planning of the self-driving cars, a hybrid path planning algorithm based on simulated annealing algorithm and particle swarm optimization is proposed. The hybrid optimization algorithm keeps the PSO algorithm simple and easy to implement, improves the global optimization ability of the algorithm, and improves the convergence speed and calculation accuracy of the algorithm. Simulation experimental results show that the algorithm has better global optimization ability and can provide guarantee for solving the global path planning problem of the self-driving cars.
DOI:10.1109/ICOMSSC45026.2018.8941726