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3d-machine-learning

A resource repository for 3D machine learning

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Library implementing the Responsibility Sensitive Safety model (RSS) for Autonomous Vehicles

apollo

An open autonomous driving platform

apollo-note

Note for Apollo 3.0 perception, prediction and planning modules

autoware

Open-source software for self-driving vehicles

awesome-decision-tree-papers

A collection of research papers on decision, classification and regression trees with implementations.

baidu-netdisk-downloaderx

⚡️ 一款图形界面的百度网盘不限速下载器，支持 Windows、Linux 和 Mac。 https://hacpai.com/article/1563154719934

behavioral_cloning

carnd-advanced-lane-lines

carnd-capstone

carnd-extended-kalman-filter-project

Self-Driving Car Nanodegree Program Starter Code for the Extended Kalman Filter Project

carnd-functional-safety-project

carnd-kidnapped-vehicle-project

carnd-mpc-project

CarND Term 2 Model Predictive Control (MPC) Project

carnd-object-detection-lab

carnd-path-planning-project

Create a path planner that is able to navigate a car safely around a virtual highway

carnd-pid-control-project

cpprobotics

cpp implementation of robotics algorithms including localization, mapping, SLAM, path planning and control

decisiontree.jl

Julia implementation of Decision Tree (CART) and Random Forest algorithms

decomputil

A header only c++ package for convex decomposition using ellipsoids

dgcnn

dig-into-apollo

Apollo learning notes

driving-lane-departure-warning

Built a real-time lane departure warning system with a monocular camera, using OpenCV.

efficient-decision-tree-notes

高效决策树算法系列笔记

epsilon

fucking-algorithm

刷算法全靠套路，认准 labuladong 就够了！English version supported! Crack LeetCode, not only how, but also why.

img_training

interview-notebook

https://github.com/CyC2018/Interview-Notebook

jps3d

A C++ implementation of Jump Point Search on both 2D and 3D maps

lane-detection

Lane departure warning system is a mechanism designed to warn a driver when the vehicle begins to move out of its lane (unless a turn signal is on in that direction) on freeways. The system is designed to minimize accidents by addressing the main causes of collisions: driver error, distractions and drowsiness. The aim of this project is to implement the core algorithm of this feature which is the lane detection. The aim is to detect road lane markers in a video stream and to highlight the lane in which the vehicle is driven by detecting boundaries of the lane markers in the video.