Robust Real-Time Lane Marking Detection for Intelligent Vehicles in Urban Environment

Author(s):  
Wentao Yao ◽  
Zhidong Deng
Author(s):  
Alexander Filonenko ◽  
Danilo Caceres Hernandez ◽  
Laksono Kurnianggoro ◽  
Dongwook Seo ◽  
Kang-Hyun Jo
Keyword(s):  

2020 ◽  
Vol 10 (20) ◽  
pp. 7054 ◽  
Author(s):  
Muzaffar Rao ◽  
Liam Lynch ◽  
James Coady ◽  
Daniel Toal ◽  
Thomas Newe

Industry 4.0 uses the analysis of real-time data, artificial intelligence, automation, and the interconnection of components of the production lines to improve manufacturing efficiency and quality. Manufacturing Execution Systems (MESs) and Autonomous Intelligent Vehicles (AIVs) are key elements of Industry 4.0 implementations. An MES connects, monitors, and controls data flows on the factory floor, while automation is achieved by using AIVs. The Robot Operating System (ROS) built AIVs are targeted here. To facilitate MES and AIV interactions, there is a need to integrate the MES and the AIVs to help in building an automated and interconnected manufacturing environment. This integration needs middleware, which understands both MES and AIVs. To address this issue, a LabVIEW-based scheduler is proposed here as the middleware. LabVIEW communicates with the MES through webservices and has support for ROS. The main task of the scheduler is to control the AIV based on MES requests. The scheduler developed was tested in a real factory environment using the SAP MES and a Robotnik ‘RB-1′ robot. The scheduler interface provides real-time information about the current status of the MES, AIV, and the current stage of scheduler processing. The proposed scheduler provides an efficient automated product delivery system that transports the product from process cell to process cell using the AIV, based on the production sequences defined by the MES. In addition, using the proposed scheduler, integration of an MES is possible with any low-cost ROS-built AIV.


Author(s):  
Yeun Sub Byun ◽  
Young Chol Kim

This paper presents a new real-time heading estimation method for an all-wheel steered single-articulated autonomous vehicle guided by a magnetic marker system. To achieve good guidance control for the vehicle, precise estimation of the position and heading angle during travel is necessary. The main concept of this study is to estimate the heading angle from the relative orientations of the magnetic markers and the vehicle motion. To achieve this, a kinematic model of the all-wheel steered vehicle is derived and combined with the motion of a magnetic ruler mounted near each axle underneath the vehicle. The position coordinates and polarities of the magnetic markers, which are provided a priori, are used to determine the vehicle position at every detection instance. A gyroscope is employed to assist real-time heading estimation at sample times when there are no marker detection data. The proposed method was tested on a real vehicle and evaluated by comparing the experimental results with those of the differential global positioning system (DGPS) in real-time kinematics (RTK) mode. Experimental results show that the proposed method exhibits good performance for heading estimation.


2007 ◽  
Vol 31 (4) ◽  
pp. 625-635 ◽  
Author(s):  
R.G. Laycock ◽  
G.D.G. Ryder ◽  
A.M. Day

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