rui201 Goto Github PK

ad-rss-lib

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

adp-helicopter-problem

Resource files for application of Approximate Dynamic Programming applied to the Elevator Problem

apollo

An open autonomous driving platform

autonomous_driving_project

SLAM, navigation for ackermann steering vehicle

autonomous_guidance_mpc_and_lqr-lmi

Kinematic MPC and dynamic LPV-LQR state feedback control for an autonomous vehicle

autonomous_vehicle_paper_reading_list

A collection of papers focus on self-driving car. Many topics are covered including computer vison, sensor fusion, SLAM, decision and planning. The paper list will be timely updated.

autonomousvehiclepaper

无人驾驶相关论文速递

autoware2020-course

Self-Driving Cars with ROS 2 and Autoware

awesome-autonomous-vehicle

无人驾驶的资源列表中文版

awesome-autonomous-vehicles

Curated List of Self-Driving Cars and Autonomous Vehicles Resources

awesome-cs

计算机优质书籍搜罗+学习路线推荐！

awesome-cs-books

经典编程书籍大全，涵盖：计算机系统与网络、系统架构、算法与数据结构、前端开发、后端开发、移动开发、数据库、测试、项目与团队、程序员职业修炼、求职面试等

c-c-

程序员资料免费分享，欢迎关注个人微信公众号：程序员编程指南

c-plus-plus

All Algorithms implemented in C++

carnd-path-planning-bosch

Self-Driving Car Engineer Program: Path Planning Project

cooperative-lane-changes-of-cavs

Codes for "Balancing Computation Speed and Quality: A Decentralized Motion Planning Method for Cooperative Lane Changes of Connected and Automated Vehicles"

crc

CRC原理及实现

d_star_pathplanning

Simple Matlab implementation of D*Lite, Focussed D*, A*, for dynamic path planning for mobile robots

docker-ros-gui

An image of ROS with Nvidia cudagl to enable GUI

drl-autonomousvehicles

Autonomous vehicle navigation based on Deep Reinforcement Learning

drl-energy-management

Deep reinforcement learning based energy management strategy for hybrid electric vehicle

dynamo

A modular MATLAB toolkit for Dynamic programming (DP) and Approximate Dynamic Programming (ADP) for Adaptive Modeling and Optimization

efficient-motion-planning

To guarantee safe and efficient driving for automated vehicles in complicated traffic conditions, the motion planning module of automated vehicles are expected to generate collision-free driving policies as soon as possible in varying traffic environment. However, there always exist a tradeoff between efficiency and accuracy for the motion planning algorithms. Besides, most motion planning methods cannot find the desired trajectory under extreme scenarios (e.g., lane change in crowded traffic scenarios). This study proposed an efficient motion planning strategy for automated lane change based on Mixed-Integer Quadratic Optimization (MIQP) and Neural Networks. We modeled the lane change task as a mixed-integer quadratic optimization problem with logical constraints, which allows the planning module to generate feasible, safe and comfortable driving actions for lane changing process. Then, a hierarchical machine learning structure that consists of SVM-based classification layer and NN-based action learning layer is established to generate desired driving policies that can make online, fast and generalized motion planning. Our model is validated in crowded lane change scenarios through numerical simulations and results indicate that our model can provide optimal and efficient motion planning for automated vehicles

ga-pso-optimized-lqr-controller-for-active-suspension-system-for-quarter-car-model

AI optimized Eigenstructure Assignment based objective function for LQR controller for Active Suspension System for Quarter Car model

h-obca

Hierarchical Optimization-Based Collision Avoidance - a path planner for autonomous parking

how-to-learn-robotics

Open-source study guide of Robotics

improved-path-planning-for-ant-colony-algorithm

learngit

libpku

贵校课程资料民间整理

localplanner