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
ETNA – time series forecasting framework

ETNA Time Series Library Predict your time series the easiest way Homepage | Documentation | Tutorials | Contribution Guide | Release Notes ETNA is an

Tinkoff.AI 675 Jan 08, 2023
Python module for machine learning time series:

seglearn Seglearn is a python package for machine learning time series or sequences. It provides an integrated pipeline for segmentation, feature extr

David Burns 536 Dec 29, 2022
Firebase + Cloudrun + Machine learning

A simple end to end consumer lending decision engine powered by Google Cloud Platform (firebase hosting and cloudrun)

Emmanuel Ogunwede 8 Aug 16, 2022
Code Repository for Machine Learning with PyTorch and Scikit-Learn

Code Repository for Machine Learning with PyTorch and Scikit-Learn

Sebastian Raschka 1.4k Jan 03, 2023
Predicting diabetes over a five year period using logistic regression and the Pima First-Nation dataset

Diabetes This script uses the Pima First Nations dataset to create a model to predict whether or not an individual will develop Diabetes Mellitus Type

1 Mar 28, 2022
Winning solution for the Galaxy Challenge on Kaggle

Winning solution for the Galaxy Challenge on Kaggle

Sander Dieleman 483 Jan 02, 2023
LiuAlgoTrader is a scalable, multi-process ML-ready framework for effective algorithmic trading

LiuAlgoTrader is a scalable, multi-process ML-ready framework for effective algorithmic trading. The framework simplify development, testing, deployment, analysis and training algo trading strategies

Amichay Oren 458 Dec 24, 2022
flexible time-series processing & feature extraction

A corona statistics and information telegram bot.

PreDiCT.IDLab 206 Dec 28, 2022
The easy way to combine mlflow, hydra and optuna into one machine learning pipeline.

mlflow_hydra_optuna_the_easy_way The easy way to combine mlflow, hydra and optuna into one machine learning pipeline. Objective TODO Usage 1. build do

shibuiwilliam 9 Sep 09, 2022
Falken provides developers with a service that allows them to train AI that can play their games

Falken provides developers with a service that allows them to train AI that can play their games. Unlike traditional RL frameworks that learn through rewards or batches of offline training, Falken is

Google Research 223 Jan 03, 2023
Uber Open Source 1.6k Dec 31, 2022
Practical Time-Series Analysis, published by Packt

Practical Time-Series Analysis This is the code repository for Practical Time-Series Analysis, published by Packt. It contains all the supporting proj

Packt 325 Dec 23, 2022
A model to predict steering torque fully end-to-end

torque_model The torque model is a spiritual successor to op-smart-torque, which was a project to train a neural network to control a car's steering f

Shane Smiskol 4 Jun 03, 2022
A linear equation solver using gaussian elimination. Implemented for fun and learning/teaching.

A linear equation solver using gaussian elimination. Implemented for fun and learning/teaching. The solver will solve equations of the type: A can be

Sanjeet N. Dasharath 3 Feb 15, 2022
TensorFlow Decision Forests (TF-DF) is a collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models.

TensorFlow Decision Forests (TF-DF) is a collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models. The library is a collection of Keras models

538 Jan 01, 2023
pywFM is a Python wrapper for Steffen Rendle's factorization machines library libFM

pywFM pywFM is a Python wrapper for Steffen Rendle's libFM. libFM is a Factorization Machine library: Factorization machines (FM) are a generic approa

João Ferreira Loff 251 Sep 23, 2022
A logistic regression model for health insurance purchasing prediction

Logistic_Regression_Model A logistic regression model for health insurance purchasing prediction This code is using these packages, so please make sur

ShawnWang 1 Nov 29, 2021
Given the names and grades for each student in a class N of students, store them in a nested list and print the name(s) of any student(s) having the second lowest grade.

Hackerank-Nested-List Given the names and grades for each student in a class N of students, store them in a nested list and print the name(s) of any s

Sangeeth Mathew John 2 Dec 14, 2021
A webpage that utilizes machine learning to extract sentiments from tweets.

Tweets_Classification_Webpage The goal of this project is to be able to predict what rating customers on social media platforms would give to products

Ayaz Nakhuda 1 Dec 30, 2021
Extreme Learning Machine implementation in Python

Python-ELM v0.3 --- ARCHIVED March 2021 --- This is an implementation of the Extreme Learning Machine [1][2] in Python, based on scikit-learn. From

David C. Lambert 511 Dec 20, 2022