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- W2371532535 abstract "In this paper,based on the reverse path planning,the possible collision points in the process of parallel parking are analyzed and the minimum parking space needed is calculated.To get a better planned parking path applied to actual parking process and avoid the analysis of a variety of constraint relations such as safe distance,the initial point of the traditional path planning is replaced with the initial region,the data during the whole process of parking is collected by real vehicle tests,the different data samples are used for training radial basis function(RBF) neural network by particle swarm optimization to generate the parking path.Simulation results and real vehicle tests show that the proposed method achieves a higher parking success rate according to the path generated with the above method." @default.
- W2371532535 created "2016-06-24" @default.
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- W2371532535 date "2012-01-01" @default.
- W2371532535 modified "2023-09-24" @default.
- W2371532535 title "Path planning of automatic parallel parking based on RBF neural network" @default.
- W2371532535 hasPublicationYear "2012" @default.
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