A Hybrid Classification of Driver’s Style and Skill Using Fully-Connected Deep Neural Networks

Driving style and skill classification are of great significance in human-oriented advanced driver-assistance system (ADAS) development. In this paper, the authors propose Fully-Connected Deep Neural Networks (FC-DNN) to classify drivers’ styles and skills with naturalistic driving data. Followed by the data collection and pre-processing, FC-DNN with a series of deep learning optimization algorithms are applied. In the experimental part, the proposed model is validated and compared with other commonly used supervised learning methods including the k-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), and multilayer perceptron (MLP). The results show that the proposed model has a higher Macro F1 score than other methods. In addition, the authors discussed the effect of different time window sizes on experimental results. The results show that the driving information of 1s can improve the final evaluation score of the model. In order to get a relatively low computation cost, the authors use principal component analysis (PCA) to reduce input data dimensions, which also made the model achieve a good performance.

• Record URL:
  https://doi.org/10.4271/2020-01-5107
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/01487191
• Supplemental Notes:
  • Abstract reprinted with permission of SAE International.
• Authors:
  • Liu, Jiarui
  • Hu, Ruofei
  • Hu, Hongyu
• Conference:
  • Automotive Technical Papers
  • Location: Warrendale Pennsylvania, United States
  • Date: 2017-12-28
• Publication Date: 2021-2-3

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Serial:
  • SAE Technical Paper
  • Publisher: Society of Automotive Engineers (SAE)
  • ISSN: 0148-7191
  • EISSN: 2688-3627
  • Serial URL: http://papers.sae.org/

Subject/Index Terms

• TRT Terms: Algorithms; Decision trees; Driver support systems; Machine learning; Mathematical models; Neural networks
• Identifier Terms: K-Nearest Neighbor Models; Principal Component Analysis
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01774486
• Record Type: Publication
• Source Agency: SAE International
• Report/Paper Numbers: 2020-01-5107
• Files: TRIS, SAE
• Created Date: Jun 20 2021 4:49PM