Collision avoidance in Intelligent Transport Systems: towards an application of control theory

Visible Light Communication (VLC) technology is one of the main candidate solutions for Intelligent Transportation Systems (ITS). This paper presents a comprehensive study on VLC based anti-rear-end collision scheme on the highway. A detailed VLC based rear-end collision avoidance model is proposed with theoretical analysis and numerical simulations.

Download Full-text

Visible light communication based collision avoidance scheme for vehicle lane-changing in Intelligent Transport Systems

2015 14th International Conference on Optical Communications and Networks (ICOCN) ◽

10.1109/icocn.2015.7203772 ◽

2015 ◽

Cited By ~ 1

Author(s):

Yao Deng ◽

Luxi Li ◽

Haoyu Wang ◽

Zhida Liu ◽

Shihao Wang ◽

...

Keyword(s):

Visible Light ◽

Collision Avoidance ◽

Visible Light Communication ◽

Transport Systems ◽

Intelligent Transport Systems ◽

Lane Changing ◽

Intelligent Transport

Download Full-text

Cooperative Intelligent Transport Systems: Towards high-level automated driving

10.1049/pbtr025e ◽

2019 ◽

Keyword(s):

Transport Systems ◽

Automated Driving ◽

Intelligent Transport Systems ◽

Intelligent Transport ◽

High Level

Download Full-text

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

Transport and Communication Science Journal ◽

10.25073/tcsj.70.3.7 ◽

2019 ◽

Vol 70 (3) ◽

pp. 214-224

Author(s):

Bui Ngoc Dung ◽

Manh Dzung Lai ◽

Tran Vu Hieu ◽

Nguyen Binh T. H.

Keyword(s):

Machine Learning ◽

Gaussian Mixture ◽

Research Field ◽

Transport Systems ◽

Learning Approaches ◽

Subtraction Method ◽

Intelligent Transport Systems ◽

Intelligent Transport ◽

Detection And Tracking ◽

Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

Download Full-text