gdjunnan Goto Github PK

-lateral-lqr-autonomous-driving-carsim-simulink-

the lateral linear quadratic regulator algorithm for autonomous driving in carsim/simulink

aicar-lanefollowing

aionr6-mpc-ros

ROS implementation of an mpc controller for Aion R6 Rover- Final Year Project: Optimal Path Planning and Following for Race Cars

automatic-parking

Python implementation of an automatic parallel parking system in a virtual environment, including path planning, path tracking, and parallel parking

automatic-parking-by-reinforcement-learning-ppo

The summer project of SDU by Hu Shurui, Wang Yunping, He Lijie, Zhu Junyi, Hou Wei

autonomous-racecar-project

autonomous_driving_mpc

Model Predictive Controller for Autonomous Driving implemented using ROS and C++

autonomous_guidance_mpc_and_lqr-lmi

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

autoware

Open-source software for self-driving vehicles

autowarearchitectureproposal.proj

This is the source code of the feasibility study for Autoware architecture proposal.

barc

Main branch for BARC related code

cherry-autonomous-racecar

Implementation of the CNN from End to End Learning for Self-Driving Cars on a Nvidia Jetson TX1 using Tensorflow and ROS

data-driven-control

A reliable controller is critical for execution of safe and smooth maneuvers of an autonomous vehicle. The controller must be robust to external disturbances, such as road surface, weather, wind conditions, and so on. It also needs to deal with internal variations of vehicle sub-systems, including powertrain inefficiency, measurement errors, time delay, etc. These factors introduce issues in controller performance. In this paper, a feed-forward compensator is designed via a data-driven method to model and optimize the controller’s performance. Principal Component Analysis (PCA) is applied for extracting influential features, after which a Time Delay Neural Network is adopted to predict control errors over a future time horizon. Based on the predicted error, a feedforward compensator is then designed to improve control performance. Simulation results in different scenarios show that, with the help of with the proposed feedforward compensator, the maximum path tracking error and the steering wheel angle oscillation are improved by 44.4% and 26.7%, respectively.

droneprogrammingcourse

Source code for drone programming course

dsctutorials

A few simple tutorials for dynamical systems and control. Most require Matlab.

dynamic-cil-driving

dynamic-trajectory-predictor

Repo for the paper 'Forecasting Pedestrian Trajectory with Machine-Annotated Training Data'. IV 2019

dyno

effectiveness-of-a-simplified-mpc-under-different-parking-circumstances

MPC control for vehicle

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

enragedrabbitproject

env_build

The repo develops a general and extensible RL environment for large-scale autonomous driving tasks.

gdutthesis

广东工业大学 LaTeX 论文模板

grapebot_simulation

husky

Common packages for the Clearpath Husky

hybrid_a_star

Hybrid A Star algorithm C++ implementation

hybrid_astar_planner

This is a global planner plugin of ROS move_base package

hypharos_minicar

1/20 MiniCar: An ackermann based rover for MPC and Pure-Pursuit controller

intersection-planning-with-mpc-approach-for-autonomous-vehicles

Implement an MPC approach to coordinate automated vehicles with fixed priorities

lane-change

ME231A Project / MPC for lane change