[NeurIPS 2025 spotlight] Official implementation for "FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving"

[AAAI 2026] OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action Model

A curated list of awesome LLM/VLM/VLA for Autonomous Driving(LLM4AD) resources (continually updated)

Recommend new arxiv papers of your interest daily according to your Zotero libarary.

Code of paper: MacLight: Multi-scene Aggregation Convolutional Learning for Traffic Signal Control

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".

CoLLMLight: Cooperative Large Language Model Agents for Network-Wide Traffic Signal Control

This repository contains the code for the paper“iLLM-TSC: Integration reinforcement learning and large language model for traffic signal control policy improvement”

This is a multi agent reinforcement learning system using SUMO for large scale traffic light control

Deep Reinforcement Learning for Traffic Lights Control using SUMO

Adaptive real-time traffic light signal control system using Deep Multi-Agent Reinforcement Learning

multi agent RL for traffic light control in Sumo using distributed PPO

Traffic simulation project for dissertation

Implementation of a reinforcement learning agent able to do autonomous changing lane using Sumo

deep reinforcement learing SUMO

Reinforcement Learning environments for Traffic Signal Control with SUMO. Compatible with Gymnasium, PettingZoo, and popular RL libraries.

Path planning for autonomous vehicles using constrained iLQR.

Using reinforcement learning and genetic algorithms to improve traffic flow and reduce vehicle waiting times in a single-lane two-way junction simulator by coordinating traffic signal schedules.

Intelligent traffic control on Vissim by dqn

SUMO adaptive traffic signal control - DQN, DDPG, Webster's, Max-pressure, Self-Organizing Traffic Lights

multi-agent deep reinforcement learning for large-scale traffic signal control.

An implementation of the traffic simulation optimisation with reinforcement learning, with FLOW and SUMO.

Project repository for Intelligent Transportation Systems course.

The SUMO simulation platform is used to realize the traditional traffic lights, intelligent traffic lights and intelligent traffic intersections.

A framework where a deep Q-Learning Reinforcement Learning agent tries to choose the correct traffic light phase at an intersection to maximize traffic efficiency.

Demos of reinforcement learning on Simulation of Urban MObility