Udacity Self-Driving Car Engineer Nanodegree projects.

Unofficial implemention of lanenet model for real time lane detection

Ultra Fast Structure-aware Deep Lane Detection (ECCV 2020)

Advanced lane detection using computer vision

Lane and obstacle detection for active assistance during driving. Uses windowed sweep for lane detection. Combination of object tracking and YOLO for obstacles. Determines lane change, relative velocity and time to collision

Lane detection and classification in an end-to-end Deep Learning fashion

TuSimple lane detection dataset addon with class information.

An advanced lane-finding algorithm using distortion correction, image rectification, color transforms, and gradient thresholding.

Lane identification system for camera based systems.

Apply computer vision to label the lanes in a driving video

Advanced lane finding

we present the plans of a driver-assistance system, which will be capable of road lane and traffic sign detection by using an OPEN-CV.

Fourth Project of the Udacity Self-Driving Car Nanodegree Program

Lane detection MATLAB code for Kalman Filter book chapter: Lane Detection

Project: Advanced Lane Finding || Udacity: Self-Driving Car Engineer Nanodegree

In this project, I used OpenCV to write a software pipeline to identify the lane boundaries in a video from a front-facing camera on a car.

Lane depertaure and Yolo objection detection C++ Linux

Lane Finding Project for Self-Driving Car Nano Degree Term 1. Road Lane Lines are detected by using various image processing techniques like Grayscale, Blurring, Canny Edge Detection, Hough Transform and Masking.

Combined lane and vehicle detection pipeline comparing YOLOv2 and LeNet-5

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