CurveLanes is a new benchmark lane detection dataset with 150K lanes images for difficult scenarios such as curves and multi-lanes in traffic lane detection. It is collected in real urban and highway scenarios in multiple cities in China.

An old experimental implementation of curved lane detection using OpenCV, RANSAC, DBSCAN etc

Exploring the use of bezier curves for lane detection —— A baseline model.

Curved Lane Detection by computer vision techniques such as perspective transform or image thresholding.

Lane line detection technique is used in many self-driving autonomous vehicles as well as line-following robots,We developed a software to Detect lane lines in a variety of conditions, including changing road surfaces, curved roads, and variable lighting. An image processing based detection and a deep learning AI detection are to be implemented, evaluated and compared in the scope of this project and the final is an easy to use sandbox for lane detection in the proposed methods.

Advanced Lane Detection Project which includes advanced image processing to detect lanes irrespective of the road texture, brightness, contrast, curves etc. Used Image warping and sliding window approach to find and plot the lane lines. Also determined the real curvature of the lane and vehicle position with respect to center.

A Flask Application deployed over Google Cloud Platfrom to perform Advanced Lane Detection on Road Images which includes advanced image processing to detect lanes irrespective of the road texture, brightness, contrast, curves etc. Used Image warping and sliding window approach to find and plot the lane lines.

This is the advanced lane detection project which includes advanced image processing to detect lanes irrespective of the road texture, brightness, contrast etc. I used Image warping and sliding window approach to find and plot the lanes. This makes it better with the lane curves.

This is continued project for lane detection on highway driving using curve fitting on to lane lines.

The development of the self-driving car is needed for the safety of driver and passenger on the vehicle. The two most important parts of advanced driver assistance systems are a collision avoidance system and a Lane keeping assist system, which could help to reduce the number of traffic accidents. A fundamental technique for effective collision avoidance and lane-keeping is a robust lane detection method

Lane detection using perspective transform and 2nd order curve fitting

Advanced Lane Detection Project which includes advanced image processing to detect lanes irrespective of the road texture, brightness, contrast, curves etc. Used Image warping and sliding window approach to find and plot the lane lines. Also determined the real curvature of the lane and vehicle position with respect to center

Project 1 of Udacity Self-Driving Car Engineer Nanodegree. Able to find curved lane lines regardless by using color&gradient threshold on hsl color space. The camera is calibrated and the image undistorted to increase the accuracy of detection.

Lane detection (curved line)

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In any driving scenario, lane lines are an essential component of indicating traffic flow and where a vehicle should drive. It's also a good starting point when developing a self-driving car! Building on my previous lane detection project, I've implemented a curved lane detection system that works much better, and is more robust to challenging environments. The lane detection system was written in Python using the OpenCV library.

This project was done to study camera calibration, lane detection at straight lanes and curved lanes and broaden my knowledge on perception by cameras for autonomous vehicles.

the project is for detecting the lane so that the autonomous vehicles can align accordingly and on encountering any curves the steering angle predicts how much the steering should be rotated in which direction. this project is developed using OpenCV. its a simple program without deep learning. DL maybe used in this to improve the object detection and manoeuvre the vehicle accordingly

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Curve lane detection

LaneNet: Enhanced Curved Lane Detection

Advanced Lane Detection (for straight or curved lane)

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Using Computer vision techniques to implement curved lane detection

Implementation of a curved lane auto-detection pipeline for self-driving car applications.

An advanced Lane detection pipeline that can detects lane boundaries, predicts upcoming curves, and measures lane curvature.

Self driving car, with lane detection and curve detection, using Deep Learning and OpenCV