Improved Position and Attitude Determination Method for Monocular Vision in Vehicle Collision Warning System

2016 ◽  
Vol 30 (07) ◽  
pp. 1655019 ◽  
Author(s):  
Lijuan Qin ◽  
Ting Wang ◽  
Yulan Hu ◽  
Chen Yao
Keyword(s):  
Real Time ◽  
Solution Method ◽  
Warning System ◽  
Time Characteristic ◽  
Monocular Vision ◽  
Theoretical Research ◽  
Driving Safety ◽  
License Plate ◽  
Collision Warning ◽  
Vehicle Collision

Vehicle collision warning system can determine the relative distance and speed between target vehicle and the front vehicle by monocular vision positioning technique from automobile license plate image captured by camera so as to judge danger level and remind the driver to make appropriate action and avoid vehicle collision timely. Study on the positioning technology of this system aims to help the driver to judge and improve driving safety. Thus, the system has a broad application prospect. The research content of this paper could enrich and supply PNL visual locating method, endowing with significance of theoretical research. The paper proposes an improved vehicle measuring method based on monocular vision for vehicle license plate. This method combines the characteristics of fast speed for analytical solution method and high positioning accuracy for iterative solution method, therefore has a high robustness and overcomes the multi-solution problem of P3P iterative method. The simulation experiments show that localization precision of the improved positioning method has been improved greatly as compared with P4L method. At the same time, the real-time characteristic of collision avoidance warning system with improved visual locating method has been improved a lot, and the new location algorithm has good performance in real-time characteristic, which greatly improve the processing ability of the system for images.

Robustness Analysis of License Plate Visual Positioning Method in Vehicle Collision Warning System

2017 ◽  
Vol 31 (01) ◽  
pp. 1750001 ◽  
Author(s):  
Lijuan Qin ◽  
Ting Wang
Keyword(s):  
Robustness Analysis ◽  
Warning System ◽  
Driving Safety ◽  
License Plate ◽  
Positioning Accuracy ◽  
Robust Analysis ◽  
Collision Warning ◽  
Location Method ◽  
Visual Positioning ◽  
Positioning Method

Vehicle anti-collision warning system is a key research area of vehicle safety. It is also a necessary means to enhance driving safety and reduce traffic accidents. The visual location method is presented for this system based on license plate image. This method realizes visual positioning with the help of the mounting points of license plate. At the same time, the feasible robust analysis method is proposed for the visual positioning method with license plate frame image. Finally, the robust analysis determines good angles for commutating rotation parameter in the method of license plate image visual positioning. At the same time, it determines good angles for translation parameter and for rotation and translation transform. Therefore, robust analysis determines positions where the visual positioning method has high positioning accuracy. Robust analysis for license plate vision positioning method is useful in analysis of high positioning accuracy positions for the proposed license plate image visual location method combined with plate mounting points. The research content of this paper is beneficial to driver decision-making and improves the safety of collision warning system and intelligent vehicle.

A Novel Method for Tramcar Anti-Collision Warning System Based on Monocular Vision

2012 ◽  
Vol 430-432 ◽  
pp. 1871-1876
Author(s):  
Hui Bo Bi ◽  
Xiao Dong Xian ◽  
Li Juan Huang
Keyword(s):  
Software Design ◽  
Early Warning System ◽  
Warning System ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Monocular Vision ◽  
System Construction ◽  
Collision Warning ◽  
Lane Width ◽  
Novel Method

For the problem of tramcar collision accident in coal mine underground, a monocular vision-based tramcar anti-collision warning system based on ARM and FPGA was designed and implemented. In this paper, we present an improved fast lane detection algorithm based on Hough transform. Besides, a new distance measurement and early-warning system based on the invariance of the lane width is proposed. System construction, hardware architecture and software design are given in detail. The experiment results show that the precision and speed of the system can satisfy the application requirement.

Real-Time Associative Memory–Based Rear-End Collision Warning System

2017 ◽  
Vol 2621 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Donghoun Lee ◽  
Sehyun Tak ◽  
Sungjin Park ◽  
Hwasoo Yeo
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Associative Memory ◽  
Intelligent Transportation System ◽  
Warning System ◽  
Time Interval ◽  
Associative Memories ◽  
Collision Warning ◽  
Sampling Ratio ◽  
Artificial Neural Network Ann

In the intelligent transportation system field, there has been a growing interest in developing collision warning systems based on artificial neural network (ANN) techniques in an effort to address several issues associated with parametric approaches. Previous ANN-based collision warning algorithms were generally based on predetermined associative memories derived before driving. Because collision risk is highly related to the current traffic situation, such as traffic state transition from free flow to congestion, however, updating associative memory in real time should be considered. To improve further the performance of the warning system, a systemic architecture is proposed to implement the multilayer perceptron neural network–based rear-end collision warning system (MCWS), which updates the associative memory with the vehicle distance sensor and smartphone data in a cloud computing environment. For the practical use of the proposed MCWS, its collision warning accuracy is evaluated with respect to various time intervals for updating the associative memories and market penetration rates. Results show that the MCWS exhibits a steady improvement in its warning performance as the time interval decreases, whereas the MCWS works more efficiently as the sampling ratio increases overall. When the sampling ratio reaches 50%, the MCWS shows a particularly stable warning accuracy, regardless of the time interval. These findings suggest that the MCWS has great potential to provide an acceptable level of warning accuracy for practical use, as it can obtain the well-trained associative memories reflecting current traffic situations by using information from widespread smartphones.

Sign in / Sign up

Export Citation Format

Share Document