scholarly journals Lane Detection for Autonomous Vehicles using Open CV Library

2020 ◽  
Vol 6 (12) ◽  
pp. 176-180
Author(s):  
Aarushi Mittal and Narinder Kaur
Keyword(s):  
Hough Transform ◽  
Autonomous Vehicles ◽  
Region Of Interest ◽  
Traffic Signals ◽  
Lane Detection ◽  
Polar Coordinates ◽  
Cartesian Coordinates ◽  
Path Discovery

For vehicles to have the option to drive without anyone else, they have to comprehend their encompassing world like human drivers, so they can explore their way in roads, pause at stop signs and traffic signals, and try not to hit impediments, for example, different vehicles and pedestrians. In view of the issues experienced in identifying objects via self-governing vehicles an exertion has been made to show path discovery utilizing OpenCV library. The explanation and method for picking grayscale rather than shading, distinguishing and detecting edges in an image, selecting region of interest, applying Hough Transform and choosing polar coordinates over Cartesian coordinates has been discussed.

scholarly journals Lane Detection for Self-Driven Vehicles

2020 ◽  
Vol 6 (12) ◽  
pp. 98-102
Author(s):  
Prince Chugh and Ajay Kaushik
Keyword(s):  
Hough Transform ◽  
Traffic Signals ◽  
Lane Detection ◽  
Area Of Interest ◽  
Path Discovery

For vehicles to have the option to drive without help from anyone else, they have to comprehend their encompassing world like human drivers, so they can explore their way in roads, delay at stop signs and traffic signals, and try not to hit impediments, for example, different vehicles and people on foot. In light of the issues experienced in identifying objects via self-sufficient vehicles an exertion has been made to exhibit path discovery utilizing OpenCV library. The explanation and methodology for picking grayscale rather than coloring, identifying edges in a picture, choosing area of interest, applying Hough Transform and picking polar directions over Cartesian directions has been talked about.

Algorithm of Lane Detection on an Appropriate Limited Region

2013 ◽  
Vol 433-435 ◽  
pp. 267-272
Author(s):  
Xing Ma ◽  
Chun Yang Mu ◽  
Chun Tao Zhang ◽  
Lu Ming Zhang
Keyword(s):  
Urban Environment ◽  
Hough Transform ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Limited Region ◽  
Feature Based

This paper proposed a lane detection algorithm for urban environment. The algorithm was concerned on selecting an appropriate limited region of interest (ROI) by OTSU segmentation. Then candidates of lane markers were extracted by Canny, finally the lane boundaries were detected by Hough transform. The limited ROI helps to identification lane in an appropriate region. This process have the effect of enhancement in the speed of operation. The proposed algorithm was simulated in MATLAB. The test databases were shared by Fondazione Bruno Kessler (FBK). The experiments show that lane boundaries can be detected correctly although they are fade. Feature-based method is usually affected by intension of image. Several characteristics of roads need to be considered further for detection more precisely.

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.

scholarly journals Robust and real-time multi-lane and single lane detection in Indian highway scenarios

2021 ◽  
Vol 309 ◽  
pp. 01016
Author(s):  
A. Sai Hanuman ◽  
G. Prasanna Kumar
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Region Of Interest ◽  
Weather Conditions ◽  
Vital Role ◽  
Lane Detection ◽  
Fundamental Aspect ◽  
Detection Approach ◽  
Lane Line ◽  
Canny Edge

In the Advanced Driver Assistance System (ADAS), lane detection plays a vital role to avoid road accidents of an Autonomous vehicle. Also, autonomous vehicles should be able to navigate by themselves, in-order to do, it needs to understand its surrounding conditions like a human. So that vehicle can determine its path in streets and highways it can maintain lane manoeuvre. Also, It has become the most fundamental aspect to consider in current ADAS research. One of the major hurdles in self-driving vehicle research is identifying the curved lanes, multiple lanes with challenging light, and weather conditions, especially in Indian highway scenarios. As it is a vision-based lane detection approach we are using OpenCV library which consists of multiple algorithms like the optimization of canny edge detection to find out the edges, features of the lane and Hough Transform for lane line generation and apply on the particular region of interest.

scholarly journals Practical limitations of lane detection algorithm based on Hough transform in challenging scenarios

2021 ◽  
Vol 18 (2) ◽  
pp. 172988142110087
Author(s):  
Qiao Huang ◽  
Jinlong Liu
Keyword(s):  
Hough Transform ◽  
Detection Rate ◽  
Real Life ◽  
Weather Conditions ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Search Range ◽  
Range Restriction ◽  
Lighting Conditions

The vision-based road lane detection technique plays a key role in driver assistance system. While existing lane recognition algorithms demonstrated over 90% detection rate, the validation test was usually conducted on limited scenarios. Significant gaps still exist when applied in real-life autonomous driving. The goal of this article was to identify these gaps and to suggest research directions that can bridge them. The straight lane detection algorithm based on linear Hough transform (HT) was used in this study as an example to evaluate the possible perception issues under challenging scenarios, including various road types, different weather conditions and shades, changed lighting conditions, and so on. The study found that the HT-based algorithm presented an acceptable detection rate in simple backgrounds, such as driving on a highway or conditions showing distinguishable contrast between lane boundaries and their surroundings. However, it failed to recognize road dividing lines under varied lighting conditions. The failure was attributed to the binarization process failing to extract lane features before detections. In addition, the existing HT-based algorithm would be interfered by lane-like interferences, such as guardrails, railways, bikeways, utility poles, pedestrian sidewalks, buildings and so on. Overall, all these findings support the need for further improvements of current road lane detection algorithms to be robust against interference and illumination variations. Moreover, the widely used algorithm has the potential to raise the lane boundary detection rate if an appropriate search range restriction and illumination classification process is added.

Some Comments on Sherratt's Principle

1977 ◽  
Vol 9 (12) ◽  
pp. 1417-1419
Author(s):  
R Vaughan
Keyword(s):  
Probability Density ◽  
Polar Coordinates ◽  
Cartesian Coordinates

The term ‘density of dwellings' used by geographers is conceptually different to the term ‘probability density of dwellings' used by statisticians. In Cartesian coordinates the numerical values only differ by a scaling constant, but in polar coordinates this is not the case. To help clear up the resulting confusion, this paper attempts to show the relation between the two concepts.

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