A selection of state-of-the-art research materials on decision making and motion planning.

[ICDE'2023] When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks

a Map-Matching-based Python Toolbox for Vehicle Trajectory Reconstruction

This repository contains the code for the paper "LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban Environments".

[WWW'2023] "AutoST: Automated Spatio-Temporal Graph Contrastive Learning"

The vehicle orientation dataset is a large-scale dataset containing more than one million annotations for vehicle detection with simultaneous orientation classification using a standard object detection network.

IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning

FedML for Autonomous Driving (AD), Intelligent Transportation Systems (ITS), Connected and Automated Vehicles (CAV)

FindVehicle: A NER dataset in transportation to extract keywords describing vehicles on the road

[IEEE TIM] Let You See in Haze and Sandstorm: Two-in-One Low-visibility Enhancement Network

Intelligent Vehicle Perception Based on Inertial Sensing and Artificial Intelligence

This repository contains the code for the paper "UniTSA: A Universal Reinforcement Learning Framework for V2X Traffic Signal Control".

Traffic Control Test Bed

Open data and performance hub for the City of Austin Transportation Department

Welcome to quote our published papers, and the codes have been uploaded.

Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmap

An automatic vehicle speed measurement and speeding violation detection approach

A Novel Spatio-Temporal Generative Inference Network for Predicting the Long-Term Highway Traffic Speed

Python scripts for Austin Transportation's ETL tasks

A curated list of Vehicle to X (V2X) resources (continually updated)