Construction of Driving Conditions Based on Multi-segment Clustering Algorithm
Authors: 
Yanxiong Sun, Yeli Li, Qingtao Zeng, Yuning Bian, Xinyu Sun, Linxuan YuAuthors Info & Claims
Pages 548 - 553
Abstract
In this paper, a new method of building k-means and DBSCAN multi-segment clustering algorithm slots on the driving conditions of driverless cars. First, data such as GPS speed obtained by driverless vehicles is used to pre-process the data using ff vehicle data pre-processing models. Then, the model is extracted using Kin-se kin-se kine kinesiology fragments and all kinematic fragments are extracted. Finally, using the Driv-D-means clustering model, the kinematics fragments are used to construct a driving curve for cars that can reflect the data collected by driverless cars. The experimental results show that the algorithm can more effectively express the characteristics contained in the original data than the k-means algorithm.
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- Construction of Driving Conditions Based on Multi-segment Clustering Algorithm
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Published In
December 2019
601 pages
ISBN:9781450376631
DOI:10.1145/3377170
Copyright © 2019 ACM.
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In-Cooperation
- Shanghai Jiao Tong University: Shanghai Jiao Tong University
- The Hong Kong Polytechnic: The Hong Kong Polytechnic University
- University of Malaya: University of Malaya
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 20 March 2020
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