Developing an Autonomous Vehicle Control System for Intersections Using Obstacle/Blind Spot Detection Frames

2016 ◽  
Author(s):  
Masanori Yoshihira ◽  
Seigo Watanabe ◽  
Hikaru Nishira ◽  
Norimasa Kishi
Keyword(s):  
Control System ◽  
Autonomous Vehicle ◽  
Blind Spot ◽  
Vehicle Control ◽  
Spot Detection

Development of unmanned autonomous vehicle control system based on 1D image sensor

1999 ◽  
Vol 21 (2) ◽  
pp. 126
Author(s):  
Kazuyuki Ito ◽  
Masayuki Ohara ◽  
Yuichioro Takahashi ◽  
Ryuji Mizukami ◽  
Hiroyuki Obinata ◽  
...  
Keyword(s):  
Control System ◽  
Autonomous Vehicle ◽  
Image Sensor ◽  
Vehicle Control

Development of Unmanned Autonomous Vehicle Control System Based on 1D Image Sensor

1998 ◽  
Author(s):  
Kazuyuki Ito ◽  
Masayuki Ohara ◽  
Yuichioro Takahashi ◽  
Ryuji Mizukami ◽  
Hiroyuki Obinata ◽  
...  
Keyword(s):  
Control System ◽  
Autonomous Vehicle ◽  
Image Sensor ◽  
Vehicle Control

scholarly journals A Study on Development of the Camera-Based Blind Spot Detection System Using the Deep Learning Methodology

2019 ◽  
Vol 9 (14) ◽  
pp. 2941 ◽  
Author(s):  
Donghwoon Kwon ◽  
Ritesh Malaiya ◽  
Geumchae Yoon ◽  
Jeong-Tak Ryu ◽  
Su-Young Pi
Keyword(s):  
Autonomous Vehicles ◽  
Detection System ◽  
Autonomous Vehicle ◽  
Blind Spot ◽  
Detection Accuracy ◽  
Source Codes ◽  
Spot Detection ◽  
Research Goal ◽  
Safety Features ◽  
Fully Connected

One of the recent news headlines is that a pedestrian was killed by an autonomous vehicle because safety features in this vehicle did not detect an object on a road correctly. Due to this accident, some global automobile companies announced plans to postpone development of an autonomous vehicle. Furthermore, there is no doubt about the importance of safety features for autonomous vehicles. For this reason, our research goal is the development of a very safe and lightweight camera-based blind spot detection system, which can be applied to future autonomous vehicles. The blind spot detection system was implemented in open source software. Approximately 2000 vehicle images and 9000 non-vehicle images were adopted for training the Fully Connected Network (FCN) model. Other data processing concepts such as the Histogram of Oriented Gradients (HOG), heat map, and thresholding were also employed. We achieved 99.43% training accuracy and 98.99% testing accuracy of the FCN model, respectively. Source codes with respect to all the methodologies were then deployed to an off-the-shelf embedded board for actual testing on a road. Actual testing was conducted with consideration of various factors, and we confirmed 93.75% average detection accuracy with three false positives.

Autonomous Vehicle Control System as a Mobile Robot by Artificial Neural Network

2017 ◽  
Vol 6 (3) ◽  
pp. 200
Author(s):  
Nazmul Haque ◽  
Md Hasnat Riaz
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control System ◽  
Motion Control ◽  
Neural Control ◽  
Mobile Robotics ◽  
Autonomous Vehicle ◽  
Vehicle Control ◽  
Network Methods ◽  
Artificial Neural

<p><span>In this paper we have presented the artificial neural network controlled car in mobile robotics and intelligent car systems. The motion control architecture of the robot is presented with an importance on the support and directing units. This uses neural network methods and the values fundamental its design is drawn. A robust neural control system using a model of the process is also developed.</span></p>

Designing a cost-effective Autonomous Vehicle Control System Kit (AVCS Kit)

2017 ◽  
Author(s):  
Li Dang ◽  
Nishanth Sriramoju ◽  
Girma Tewolde ◽  
Jaerock Kwon ◽  
Xiaoyuan Zhang
Keyword(s):  
Control System ◽  
Autonomous Vehicle ◽  
Cost Effective ◽  
Vehicle Control
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