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Name: Rui201
Type: User
Name: Rui201
Type: User
Library implementing the Responsibility Sensitive Safety model (RSS) for Autonomous Vehicles
Resource files for application of Approximate Dynamic Programming applied to the Elevator Problem
An open autonomous driving platform
SLAM, navigation for ackermann steering vehicle
Kinematic MPC and dynamic LPV-LQR state feedback control for an autonomous vehicle
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.
无人驾驶相关论文速递
Self-Driving Cars with ROS 2 and Autoware
无人驾驶的资源列表中文版
Curated List of Self-Driving Cars and Autonomous Vehicles Resources
计算机优质书籍搜罗+学习路线推荐!
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
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
All Algorithms implemented in C++
Self-Driving Car Engineer Program: Path Planning Project
Codes for "Balancing Computation Speed and Quality: A Decentralized Motion Planning Method for Cooperative Lane Changes of Connected and Automated Vehicles"
CRC原理及实现
Simple Matlab implementation of D*Lite, Focussed D*, A*, for dynamic path planning for mobile robots
An image of ROS with Nvidia cudagl to enable GUI
Autonomous vehicle navigation based on Deep Reinforcement Learning
Deep reinforcement learning based energy management strategy for hybrid electric vehicle
A modular MATLAB toolkit for Dynamic programming (DP) and Approximate Dynamic Programming (ADP) for Adaptive Modeling and Optimization
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
AI optimized Eigenstructure Assignment based objective function for LQR controller for Active Suspension System for Quarter Car model
Hierarchical Optimization-Based Collision Avoidance - a path planner for autonomous parking
Open-source study guide of Robotics
贵校课程资料民间整理
A declarative, efficient, and flexible JavaScript library for building user interfaces.
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Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
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Open source projects and samples from Microsoft.
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Data-Driven Documents codes.
China tencent open source team.