GitHub topics: automated-vehicles

Repositories

usdot-fhwa-stol/carma-platform

CARMA Platform is built on robot operating system (ROS) and utilizes open source software (OSS) that enables Cooperative Driving Automation (CDA) features to allow Automated Driving Systems to interact and cooperate with infrastructure and other vehicles through communication. Doxygen Source Code Documentation :

Language: C++ - Size: 63.8 MB - Last synced at: 7 days ago - Pushed at: 7 days ago - Stars: 413 - Forks: 125

Yiru-Jiao/SSMsOnPlane

This repository shares python scripts for calculating various surrogate safety measures (SSMs), or in another way called, surrogate measures of safety (SMoS) for pairs of road users on an abstracted plane of road, i.e., in a two-dimensional space.

Language: Python - Size: 0 Bytes - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 0 - Forks: 0

M-Colley/pedsumo

A Python library extending SUMO for the simulation of interaction between automated vehicles and pedestrians.

Language: HTML - Size: 29.2 MB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 21 - Forks: 2

djamelbenr/Improving-Efficiency-and-Generalisability-of-MP

(IEEE-ITS 2023) Improving Efficiency and Generalisability of Motion Prediction with Deep Multi-Agent Learning and Multi-Head Attention

Language: Python - Size: 849 KB - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 7 - Forks: 0

shanto268/NaSch_CA_Traffic_Flow_Analysis_Software

This repository contains software for multi-agent simulation model of mixed traﬃc ﬂow of connected (HVs) and automated vehicles (AVs) in Python using pygame, matplotlib, numpy, scipy and seaborn libraries. The software is capable of simulating many different cases of traffic flow and creates data files and figures for the purpose of analysis. Currently I am working on making the front end of the software more user friendly for potential commercialization.

Language: Python - Size: 71.3 MB - Last synced at: 25 days ago - Pushed at: almost 5 years ago - Stars: 18 - Forks: 1

Arjunprajapat732/VLPR_Project

VLPR System: Open-source Python project for automatic license plate recognition. Uses advanced image processing and OCR to extract alphanumeric characters. GitHub repo for collaboration.

Language: Python - Size: 61.5 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 0

utkarshgupta27/AutomatedVehicleSystemLevelSimulation

A comprehensive simulation platform integrating vehicle dynamics, environment emulation, body controls, and battery management for holistic testing and validation of automated vehicles.

Language: Python - Size: 2.18 MB - Last synced at: about 1 year ago - Pushed at: over 1 year ago - Stars: 4 - Forks: 0

Ossairy/Thouth_Automated-Guided-Vehicle

the project objective is to design, manufacture and control Hospital and hotels cleaning automated guided vehicle.

Language: C++ - Size: 34.5 MB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 2 - Forks: 0

toruseo/SOSAV

Multi-objective dynamic traffic assignment for social optimal shared autonomous vehicles

Language: Python - Size: 2.4 MB - Last synced at: about 1 year ago - Pushed at: almost 4 years ago - Stars: 3 - Forks: 0

Raja-mishra1/traffic-signs_image_classifier

An image classifier that can classify various traffic symbols present on the road , built by keeping in mind how automated vehicles can use these for more effortless user experience .

Language: Jupyter Notebook - Size: 3.31 MB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 2 - Forks: 0

SHITIANYU-hue/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

Language: MATLAB - Size: 27.6 MB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 34 - Forks: 11