@inproceedings{c95c238a4f5d489180665d00bb90b23c,
title = "Unsafe Driving Behavior Prediction for Electric Vehicles",
abstract = "There is an increasing availability of electric vehicles in recent years. With the revolutionary motors and electric modules within the electric vehicles, the instant reactions bring up not only improved driving experience but also the unexpected unsafe driving accidents. Unsafe driving behavior prediction is a challenging tasks, due to the complex spatial and temporal scenarios. However, the rich sensor data collected in the electric vehicles shed light on the possible driving behavior profiling. In this paper, based on a recent electric vehicle dataset, we analyze and categorize the unsafe driving behaviors into several classes. We then design a deep learning based multi-feature fusion approach for the unsafe driving behavior prediction framework. The proposed approach is able to distinguish the unsafe behaviors from normal ones. Improved performance is also demonstrated in the different feature analysis of unsafe behaviors.",
keywords = "Electric vehicles, Feature fusion, Unsafe behavior prediction",
author = "Jiaxiang Huang and Hao Lin and Junjie Yao",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 5th International Joint Conference on Asia-Pacific Web and Web-Age Information Management, APWeb-WAIM 2021 ; Conference date: 23-08-2021 Through 25-08-2021",
year = "2021",
doi = "10.1007/978-3-030-85896-4\_7",
language = "英语",
isbn = "9783030858957",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "80--93",
editor = "U, \{Leong Hou\} and Marc Spaniol and Yasushi Sakurai and Junying Chen",
booktitle = "Web and Big Data - 5th International Joint Conference, APWeb-WAIM 2021, Proceedings",
address = "德国",
}