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

2020-01-5107

02/03/2021

Features
Event
Automotive Technical Papers
Authors Abstract
Content
Driving style and skill classification are of great significance in human-oriented advanced driver-assistance system (ADAS) development. In this paper, we 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, we 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, we use principal component analysis (PCA) to reduce input data dimensions, which also made the model achieve a good performance.
Meta TagsDetails
DOI
https://doi.org/10.4271/2020-01-5107
Pages
10
Citation
Liu, J., Hu, R., and Hu, H., "A Hybrid Classification of Driver’s Style and Skill Using Fully-Connected Deep Neural Networks," SAE Technical Paper 2020-01-5107, 2021, https://doi.org/10.4271/2020-01-5107.
Additional Details
Publisher
Published
Feb 3, 2021
Product Code
2020-01-5107
Content Type
Technical Paper
Language
English