Deep learning for vehicle safety

Raiyan Talkhani; Tao Huang; Shushi Gu; Zhaoxia Guo; Guanglin Zhang; Wei Xiang

doi:10.1201/9781003190691-2

Deep learning for vehicle safety

Raiyan Talkhani
, Tao Huang
, Shushi Gu
, Zhaoxia Guo
, Guanglin Zhang
, Wei Xiang

James Cook University Queensland
Harbin Institute of Technology Shenzhen
Sichuan University
Donghua University
La Trobe University

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

2 Scopus citations

Abstract

Deep learning has demonstrated its capability in intelligent system design. In addition, deep learning has shown that it can perform real-time object detection, object recognition, and optimizing communication networks. This chapter will introduce some recent developments relevant to vehicle safety improvement, such as internal vehicle monitoring, on-road environment monitoring, traffic management, etc. Various deep learning algorithms will be explored in these tasks, such as LSTM, CNN, reinforcement learning, and variations of them.

Original language	English
Title of host publication	Deep Learning and Its Applications for Vehicle Networks
Publisher	CRC Press
Pages	3-16
Number of pages	14
ISBN (Electronic)	9781000877236
ISBN (Print)	9781032041377
DOIs	https://doi.org/10.1201/9781003190691-2
State	Published - 1 Jan 2023
Externally published	Yes

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10.1201/9781003190691-2

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Deep learning for vehicle safety

Abstract

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