A Novel Method of Mining Driving States via Latent Dirichlet Allocation Model

Automatic driving technology has attracted significant attention in artificial intelligence and data mining in the recent years. Mining driving states for accurately understanding driving behaviors is of significant importance for automatic vehicle. Although some methods for mining individualization driving have been developed, the application of these methods is still limited in practice because the latent driving states and structure driving behavior cannot be obtained. In this study, the objective is to mine the latent driving states and structure driving behavior for deeply understanding the individualization driving. The vehicle motion data are collected using on-vehicle detection sensors, and the driving behavior is successfully extracted by the proposed encode method. Latent Dirichlet Allocation (LDA) model is employed to discover driving states (topics) from individualization driving (documents) using driving behaviors (words). With the LDA model, the latent driving states and quantified structure of the driving behavior pattern can be discovered. In order to validate the performance and effectiveness of the proposed method, a typical unsupervised method k-means and the random method are selected as compared methods in the experiments. Experimental results show that the proposed method is effective and can achieve better performance.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ABJ70 Standing Committee on Artificial Intelligence and Advanced Computing Applications.
• Authors:
  • Chen, Zhijun
  • Cai, Hao
  • Zhang, Yi-Shi
  • Wu, Chaozhong
  • Ran, Bin
• Conference:
  • Transportation Research Board 97th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2018-1-7 to 2018-1-11
• Date: 2018

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; Maps; Photos; Tables;
• Pagination: 15p

Subject/Index Terms

• TRT Terms: Automation; Behavior; Data mining; Drivers; Driving; In vehicle sensors; Intelligent vehicles
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01658934
• Record Type: Publication
• Report/Paper Numbers: 18-04589
• Files: TRIS, TRB, ATRI
• Created Date: Feb 5 2018 11:24AM