HuMingithub

HuMingithub's Stars

• mh0797/interPlan

  Language:Python674

• MCZhi/DIPP

  [TNNLS] Differentiable Integrated Prediction and Planning Framework for Urban Autonomous Driving

  Language:Python22945

• souhaiel1/Longitudinal-and-Lateral-Control-of-an-automotive-vehicle

  This repository contains the Matlab code for the lateral and longitudinal control of vehicle modeled based on the Bicycle-Model. This work was carried out by me and my colleague Suleyman as part of the smart transportation course.

  Language:MATLAB5916

• mathworks/vehicle-model-predictive-control

  This submission contains a model to show the implementation of MPC on a vehicle moving in a US Highway scene.

  Language:MATLAB8519

• ZexuWang/MPC_based-nonlinear-trajectory-planning

  This project is related to Zexu's Master thesis regarding trajectory planning for 4 wheel steered vehicle

  Language:Matlab3517

• xuelang-wang/Paper-code-implementation

  Thesis retrieval

  Language:MATLAB8331

• euge2838/Autonomous_Guidance_MPC_and_LQR-LMI

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

  Language:Matlab13443

• MMehrez/MPC-and-MHE-implementation-in-MATLAB-using-Casadi

  This is a workshop on implementing model predictive control (MPC) and moving horizon estimation (MHE) on Matlab. The implementation is based on the Casadi Package which is used for numerical optimization. A non-holonomic mobile robot is used as a system for the implementation. The workshop video recording can be found here https://www.youtube.com/playlist?list=PLK8squHT_Uzej3UCUHjtOtm5X7pMFSgAL ... Casadi can be downloaded here https://web.casadi.org/

  Language:MATLAB345138

• TakaHoribe/EKF_based_Adaptive_control

  LQR controller design for nonlinear systems via KEF based parameter estimation

  Language:Matlab216

• autowarefoundation/autoware

  Autoware - the world's leading open-source software project for autonomous driving

  Language:Dockerfile9.2k3.1k

• ApolloAuto/apollo

  An open autonomous driving platform

  Language:C++25.3k9.7k

• Michelle-NYX/traffic-simulator-Q-learning

  We propose a driver modeling process and its evaluation results of an intelligent autonomous driving policy, which is obtained through reinforcement learning techniques. Assuming a MDP decision making model, Q-learning method is applied to simple but descriptive state and action spaces, so that a policy is developed within limited computational load. The driver could perform reasonable maneuvers, like acceleration, deceleration or lane-changes, under usual traffic conditions on a multi-lane highway. A traffic simulator is also construed to evaluate a given policy in terms of collision rate, average travelling speed, and lane change times. Results show the policy gets well trained under reasonable time periods, where the driver acts interactively in the stochastic traffic environment, demonstrating low collision rate and obtaining higher travelling speed than the average of the environment. Sample traffic simulation videos are postedsit on YouTube.

  Language:Jupyter Notebook266

• yanb514/car_following_online_est

  Language:MATLAB1

• fabianobie/luppar

  Sistema de Recuperação de Informação dotado de Análise de Contexto Local baseada em Modelo Semântico Distribucional

  Language:Python4