Code base of KU AIRS: SPARK Autonomous Vehicle Team

Related tags

Machine Learningspark
Overview

KU AIRS: SPARK Autonomous Vehicle Project

Check this link for the blog post describing this project and the video of SPARK in simulation and on parkour.

Project Structure

SPARK code base was written on ROS Noetic using Python and C++. The code is structured into packages with specific responsibilities. The repository also includes driver packages for the sensor suite.

Packages

  • controllers: Contains PID and Stanley controller for waypoint following

  • hardware_interface: Interface to Arduino microcontrollers in the car. Accepts messages controlling steering, throttle, brake and gearshift commands.

  • joy_controller: Contains the code for controlling SPARK with a joystick

  • lane_detector: Implementation of LaneNet instance segmentation model for lane detection. Modified for the use case and includes trained models on TUSimple dataset augmented with our custom real and synthetic data.

  • lidar_cam_calibration: Sensor fusion package for fusing LIDAR and camera data to localize traffic signs in 3D.

  • lidar_following: Contains the navigation stack using LIDAR mapping.

  • localization: Contains the localization stack using RTK-GPS data.

  • planners: Contains finite state machine based behavioural planner and cubic spiral lattice planner for parking and waypoint navigation

  • spark: Contains launch files for system startup

  • spark_description: Contains environment and robot descriptions for use with Rviz and Gazebo

  • spark_msgs: Contains all custom message and service definitions.

  • visualization: Rviz visualization scripts

  • yolov5: YoloV5 implementation and ROS node trained on custom Turkish traffic signs dataset for real time traffic sign and light detection.

Owner
Mehmet Enes Erciyes
Mehmet Enes Erciyes
SIMD-accelerated bitwise hamming distance Python module for hexidecimal strings

hexhamming What does it do? This module performs a fast bitwise hamming distance of two hexadecimal strings. This looks like: DEADBEEF = 1101111010101

Michael Recachinas 12 Oct 14, 2022
This project impelemented for midterm of the Machine Learning #Zoomcamp #Alexey Grigorev

MLProject_01 This project impelemented for midterm of the Machine Learning #Zoomcamp #Alexey Grigorev Context Dataset English question data set file F

Hadi Nakhi 1 Dec 18, 2021
A benchmark of data-centric tasks from across the machine learning lifecycle.

A benchmark of data-centric tasks from across the machine learning lifecycle.

61 Dec 28, 2022
Adversarial Framework for (non-) Parametric Image Stylisation Mosaics

Fully Adversarial Mosaics (FAMOS) Pytorch implementation of the paper "Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Imag

Zalando Research 120 Dec 24, 2022
Can a machine learning project be implemented to estimate the salaries of baseball players whose salary information and career statistics for 1986 are shared?

END TO END MACHINE LEARNING PROJECT ON HITTERS DATASET Can a machine learning project be implemented to estimate the salaries of baseball players whos

Pinar Oner 7 Dec 18, 2021
🌊 River is a Python library for online machine learning.

River is a Python library for online machine learning. It is the result of a merger between creme and scikit-multiflow. River's ambition is to be the go-to library for doing machine learning on strea

OnlineML 4k Jan 03, 2023
Educational python for Neural Networks, written in pure Python/NumPy.

Educational python for Neural Networks, written in pure Python/NumPy.

127 Oct 27, 2022
Bottleneck a collection of fast, NaN-aware NumPy array functions written in C.

Bottleneck Bottleneck is a collection of fast, NaN-aware NumPy array functions written in C. As one example, to check if a np.array has any NaNs using

Python for Data 835 Dec 27, 2022
Evidently helps analyze machine learning models during validation or production monitoring

Evidently helps analyze machine learning models during validation or production monitoring. The tool generates interactive visual reports and JSON profiles from pandas DataFrame or csv files. Current

Evidently AI 3.1k Jan 07, 2023
A python fast implementation of the famous SVD algorithm popularized by Simon Funk during Netflix Prize

âš¡ funk-svd funk-svd is a Python 3 library implementing a fast version of the famous SVD algorithm popularized by Simon Funk during the Neflix Prize co

Geoffrey Bolmier 171 Dec 19, 2022
This repository contains the code to predict house price using Linear Regression Method

House-Price-Prediction-Using-Linear-Regression The dataset I used for this personal project is from Kaggle uploaded by aariyan panchal. Link of Datase

0 Jan 28, 2022
MCML is a toolkit for semi-supervised dimensionality reduction and quantitative analysis of Multi-Class, Multi-Label data

MCML is a toolkit for semi-supervised dimensionality reduction and quantitative analysis of Multi-Class, Multi-Label data. We demonstrate its use

Pachter Lab 26 Nov 29, 2022
Factorization machines in python

Factorization Machines in Python This is a python implementation of Factorization Machines [1]. This uses stochastic gradient descent with adaptive re

Corey Lynch 892 Jan 03, 2023
Mortality risk prediction for COVID-19 patients using XGBoost models

Mortality risk prediction for COVID-19 patients using XGBoost models Using demographic and lab test data received from the HM Hospitales in Spain, I b

1 Jan 19, 2022
Python bindings for MPI

MPI for Python Overview Welcome to MPI for Python. This package provides Python bindings for the Message Passing Interface (MPI) standard. It is imple

MPI for Python 604 Dec 29, 2022
AutoTabular automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications.

AutoTabular AutoTabular automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just

wenqi 2 Jun 26, 2022
Python based GBDT implementation

Py-boost: a research tool for exploring GBDTs Modern gradient boosting toolkits are very complex and are written in low-level programming languages. A

Sberbank AI Lab 20 Sep 21, 2022
Skforecast is a python library that eases using scikit-learn regressors as multi-step forecasters

Skforecast is a python library that eases using scikit-learn regressors as multi-step forecasters. It also works with any regressor compatible with the scikit-learn API (pipelines, CatBoost, LightGBM

Joaquín Amat Rodrigo 297 Jan 09, 2023
Confidence intervals for scikit-learn forest algorithms

forest-confidence-interval: Confidence intervals for Forest algorithms Forest algorithms are powerful ensemble methods for classification and regressi

272 Dec 01, 2022
pandas, scikit-learn, xgboost and seaborn integration

pandas, scikit-learn and xgboost integration.

299 Dec 30, 2022