Lane Detection And Lane Departure-The Next Gen Technology
By: Nikhil Saste
Tags: Artificial Intelligence, Image Processing, Computer Vision, Machine Learning

Lane Detection And Lane Departure-The Next Gen Technology

Introduction

Lane detection is one significant method in the visualization based driver support structure and capable to be used for vehicle routing, cross power, crash avoidance, or lane departure warning system. Different road condition that create this difficulty more complex include dissimilar variety of lanes (straight or rounded), occlusions cause by obstacle, fog, darkness, illumination change (like nighttime), and so on. Therefore it is the method to locate lane in the picture and is a significant enable or attractive skill in different automobile application, include lane departure recognition and warning, travel control, cross-control, and self-directed driving. A lane departure warning system(LDWS) is a technology designed for warning a driver when the vehicle begins to depart from its lane. An effective lane detection system will navigate autonomously or assist driver in all types of lanes like straight and curved, white and yellow, single and double, solid and broken and pavement or highway lane boundaries. The system should be able to detect lane even under noisy conditions such as fog, shadow, and stain.

Lane Detection

According to wikipedia,

“A lane is part of a roadway that is designated to be used by a single line of vehicles, to control and guide drivers and reduce traffic conflicts.”

As I mentioned earlier, lane detection is a critical component of self-driving cars and autonomous vehicles. It is one of the most important research topics for driving scene understanding. Once lane positions are obtained, the vehicle will know where to go and avoid the risk of running into other lanes or getting off the road. This can prevent the driver/car system from drifting off the driving lane.

LANE DETECTION TECHNIQUES

Hough Line Transformation

Hough Transform is a technique used for extracting features that can be used in image analysis and digital image processing. Traditional Hough Transform is basically used for identifying lines in the images. There was a difficulty in detecting straight lines, circles etc. in automated analysis of digital images. The edge detector has been used in pre-processing stage for obtaining points on image that lie on desired curve but due to some problem in image, some of the pixels were missing on desired curve.

Hough Transform

Edge Detection

This method is based on the idea of identifying points in an image at which image brightness changes sharply. Edge is defined as organized set of curved line segments. This set consists of points at which brightness of image changes sharply. Edge Detection is a tool used in image processing for feature detection and extraction.

However it is not always possible that ideal edges can be obtained from real life images of modern complexity. An edge detection algorithm called canny edge detector is used to detect edges in an image. This method uses multiple stage algorithm and aims in discovering the optimal edge detection.

Canny Edge Detector

Code for Canny Edge Detection

img = cv.imread(‘image.jpg’,0)

edges = cv.Canny(img,100,200)

images = cv2.imshow(“result”,edges)

Types of Lanes 

1. Traffic Lane : Lane for the vehicles moving from one destination to another. 2.Express Lane : Used by faster moving traffic and has less access to exits/off ramps

3.Reversible Lane: To match the peak flow direction of vehicles is changed. Periods of high traffic flow are accommodated by this lane.

4. Auxiliary lane : Used for separating entering, exiting or turning traffic from the through traffic.

In some areas, for non-moving vehicles lane adjacent to curb is reserved

Lane Departure Warning System consists of

1. Systems which warn the driver if the vehicle is leaving its lane with visual, audible, and/or vibration warnings (lane departure warning, LDW)

2. Systems which warn the driver and, with no response, automatically take steps to ensure the vehicle stays in its lane (lane keeping assist)

3. Systems which assist in oversteering, keeping the car centered in the lane, and asking the driver to take over in challenging situations.

Benefits

1. Gives assistance and details to pedestrians and drivers

2. Uniformity of the markings is an important factor in minimizing confusion and uncertainty about their meaning

3. Allows vehicular drivers to drive safely

Limitations

1. Lane departure warning systems and lane keeping systems rely on visible lane markings. They typically cannot decipher faded, missing, or incorrect lane markings. Markings covered in snow or old lane markings left visible can hinder the ability of the system.

2. UNECE regulation 130 does not require LDWS of heavy vehicles to work under 60 km/h or to work in a curve with a radius lower than 250 meters.

3. Lane departure warning systems also face many legal limitations regarding autonomous driving.

4. As stated previously, this system requires constant driver input. Vehicles with this technology are limited to assisting the driver, not driving the vehicle. Lane departure warning systems biggest limitation is that it is not in complete control of the vehicle. The system does not take into account other vehicles on the road and cannot replace good driving habits.