scholarly journals Robust Lane Detection and Tracking Algorithm for Steering Assist Systems

Machines ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 10
Author(s):  
Mihail-Alexandru Andrei ◽  
Costin-Anton Boiangiu ◽  
Nicolae Tarbă ◽  
Mihai-Lucian Voncilă
Keyword(s):  
Warning System ◽  
Region Of Interest ◽  
Original Method ◽  
Lane Detection ◽  
Light Sources ◽  
Detection And Tracking ◽  
Lane Departure ◽  
Increase In Accuracy ◽  
Safety Systems ◽  
Distinct Features

Modern vehicles rely on a multitude of sensors and cameras to both understand the environment around them and assist the driver in different situations. Lane detection is an overall process as it can be used in safety systems such as the lane departure warning system (LDWS). Lane detection may be used in steering assist systems, especially useful at night in the absence of light sources. Although developing such a system can be done simply by using global positioning system (GPS) maps, it is dependent on an internet connection or GPS signal, elements that may be absent in some locations. Because of this, such systems should also rely on computer vision algorithms. In this paper, we improve upon an existing lane detection method, by changing two distinct features, which in turn leads to better optimization and false lane marker rejection. We propose using a probabilistic Hough transform, instead of a regular one, as well as using a parallelogram region of interest (ROI), instead of a trapezoidal one. By using these two methods we obtain an increase in overall runtime of approximately 30%, as well as an increase in accuracy of up to 3%, compared to the original method.

Research on Lane Departure Warning System

2014 ◽  
Vol 598 ◽  
pp. 731-735 ◽  
Author(s):  
Hui Bao Yang
Keyword(s):  
Hough Transform ◽  
Warning System ◽  
Region Of Interest ◽  
Lane Departure Warning ◽  
Linear Mode ◽  
Detection And Tracking ◽  
Lane Departure ◽  
Calculation Parameter ◽  
Lane Line ◽  
Lane Position

Lane departure warning system includes lane identification and lane departure determination. Lane identification is crucial for lane departure warning system. In this paper, A linear mode which can only bring out small distance error and angle error is used to detect lane boundaries. A region of interest (ROI) appropriate is set to reduce nonessential cost of computation. According to the characteristics of lane position, we improve the Hough transform, reduce the detection and tracking transform angle and raise the speed of calculation. Parameter angle form Hough transform is used to lane departure determination, this method does not calibrate the camera and can get the lane departure rate. Experiments show that the system is well to detect and track the lane line and give alarm correctly.

scholarly journals Lane Detection and Lane Departure Warning System

2021 ◽  
Vol 10 (3) ◽  
pp. 2212-2218
Keyword(s):  
Autonomous Vehicles ◽  
Warning System ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Road Accidents ◽  
Lane Departure ◽  
Clustering And Classification ◽  
Canny Edge ◽  
Boundary Information

Lane detection is important for autonomous vehicles. For this reason, many approaches use lane boundary information to locate the vehicle inside the street, or to integrate GPS-based localization. Advanced driverassistance systems are developed to assist drivers in the driving process reducing road accidents. In this work, we present an end-to-end system for lane identification, clustering and classification, based on two cascaded neural networks, that runs in real-time. The first step is camera calibration which is used to remove the effect of lens distortion. Then a canny edge detection algorithm finds the edges of the images. Then the region of interest (ROI) is selected. The ROI is actually based on the rectangular shape appearing at the bottom of the image. ROI removes the unwanted region in the image. The potential lane markers are then determined using the Hough transform to analyze lane boundaries. Once the lane pixels are found, these pixels are continuously scanned to obtain the best linear regression analysis.It is qualified to be applied on highways and urban roadways. It also has been successfully verified in sunny, and rainy conditions for both day and night.

A Novel Real-Time Lighting-Invariant Lane Departure Warning System

2018 ◽  
pp. 231-252
Author(s):  
Yassin Kortli ◽  
Mehrez Marzougui ◽  
Mohamed Atri
Keyword(s):  
Real Time ◽  
Traffic Accidents ◽  
Essential Element ◽  
Warning System ◽  
Lane Detection ◽  
Accuracy Evaluation ◽  
Lane Departure Warning ◽  
Lighting Conditions ◽  
Lane Departure ◽  
Driving Assistance

In recent years, in order to minimize traffic accidents, developing driving assistance systems for security has attracted much attention. Lane detection is an essential element of avoiding accidents and enhancing driving security. In this chapter, the authors implement a novel real-time lighting-invariant lane departure warning system. The proposed methodology works well in different lighting conditions, such as in poor conditions. The experimental results and accuracy evaluation indicates the efficiency of the system proposed for lane detection. The correct detection rate averages 97% and exceeds 95.6% in poor conditions. Furthermore, the entire process has only 29 ms per frame.

Real-time illumination invariant lane detection for lane departure warning system

2015 ◽  
Vol 42 (4) ◽  
pp. 1816-1824 ◽  
Author(s):  
Jongin Son ◽  
Hunjae Yoo ◽  
Sanghoon Kim ◽  
Kwanghoon Sohn
Keyword(s):  
Real Time ◽  
Warning System ◽  
Lane Detection ◽  
Lane Departure Warning ◽  
Illumination Invariant ◽  
Lane Departure

scholarly journals Vision-Based Lane Departure Detection Using a Stacked Sparse Autoencoder

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Zengcai Wang ◽  
Xiaojin Wang ◽  
Lei Zhao ◽  
Guoxin Zhang
Keyword(s):  
Polar Angle ◽  
Region Of Interest ◽  
Lane Detection ◽  
Fine Tuning ◽  
High Detection Rate ◽  
Softmax Classifier ◽  
Lane Departure ◽  
Detection Approach ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder

This paper presents a lane departure detection approach that utilizes a stacked sparse autoencoder (SSAE) for vehicles driving on motorways or similar roads. Image preprocessing techniques are successfully executed in the initialization procedure to obtain robust region-of-interest extraction parts. Lane detection operations based on Hough transform with a polar angle constraint and a matching algorithm are then implemented for two-lane boundary extraction. The slopes and intercepts of lines are obtained by converting the two lanes from polar to Cartesian space. Lateral offsets are also computed as an important step of feature extraction in the image pixel coordinate without any intrinsic or extrinsic camera parameter. Subsequently, a softmax classifier is designed with the proposed SSAE. The slopes and intercepts of lines and lateral offsets are the feature inputs. A greedy, layer-wise method is employed based on the inputs to pretrain the weights of the entire deep network. Fine-tuning is conducted to determine the global optimal parameters by simultaneously altering all layer parameters. The outputs are three detection labels. Experimental results indicate that the proposed approach can detect lane departure robustly with a high detection rate. The efficiency of the proposed method is demonstrated on several real images.

Design and Development of In-Vehicle Lane Departure Warning System using Standard Global Positioning System Receiver

2019 ◽  
Vol 2673 (8) ◽  
pp. 648-656 ◽  
Author(s):  
Muhammad Faizan ◽  
Shah Hussain ◽  
M. I. Hayee
Keyword(s):  
Image Processing ◽  
Global Positioning System ◽  
Warning System ◽  
Lane Detection ◽  
Positioning System ◽  
Gps Receiver ◽  
Lane Departure Warning ◽  
Significant Potential ◽  
Lane Departure ◽  
Global Positioning

A lane departure warning system is a critical element among advanced driver-assistance systems functions, which has significant potential to reduce crashes. Generally, lane departure warning systems use image processing or optical scanning techniques to detect a lane departure. These systems have some limitations, however, such as harsh weather or irregular lane markings having a negative influence on their performance. Integrating global positioning system (GPS) and digital maps of lane-level resolution with an image processing based lane detection system can improve its efficiency but make the overall system more complex and expensive. In this paper, a lane detection method is proposed which uses a standard GPS receiver without any lane-level resolution maps. The proposed algorithm determines the lateral shift of a vehicle by comparing the vehicle’s trajectory acquired by standard GPS receiver to the reference road direction. The reference road direction is extracted from a standard digital mapping database commonly available in any navigational device containing maps with only road-level information. Extensive field tests were performed to evaluate the efficiency of the proposed system. The field test results show that the proposed system can detect a true lane departure with an accuracy of almost 100%. Although no true lane departure was left undetected, occasional false lane departures were detected when the vehicle did not actually depart its lane. Furthermore, a modification in the proposed algorithm was also tested which has significant potential to reduce the frequency of false alarms.

scholarly journals Robust lane detection algorithm based on triangular lane model

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1639-1647
Author(s):  
Young Park ◽  
Sung Na ◽  
Qun Wei ◽  
Ki Seong ◽  
Jyung Lee ◽  
...  
Keyword(s):  
Extraction Method ◽  
Industrial Revolution ◽  
Near Field ◽  
Warning System ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Feature Points ◽  
Lane Departure ◽  
Cloudy Weather ◽  
Forward Collision Warning

Recently, various technologies and sciences are developed for fourth industrial revolution which includes artificial intelligence, robotics, internet of things, 3-D printing, and autonomous transportation. The autonomous transportation is one of the fourth industrial technology, and has been studying for safety driving. Accurate lane detection and lane departure warning system is very important for autonomous transportation. However, conventional methods have some problems of applying real-driving such as extracting miss lanes under cloudy weather and vehicle disturbance situations. In order to solve the real driving problems, we propose a new lane detection technique for lane departure and forward collision warning system using a single in-vehicle camera. The proposed method consists of triangular lane model, feature points extraction method. In the near field, a triangular lane model is used to approximate a pair of lane boundaries. Subsequently, feature points extraction method based on hyperbola curve model is applied to obtain lane curvature in the far field. Mathematical B-spline applied to feature points for curved lane fitting. Simulation results show that the proposed lane detection and tracking method has good performance.

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