# A Simple Framework Towards Vision-based Traffic Signal Control with Microscopic Simulation

arXiv: https://arxiv.org/abs/2403.06884 · HTML: https://mlsanity.com/paper/2403.06884/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 44 references that are in the map.
- First posted on arXiv 11 Mar 2024.

## Builds on (most cited first, 11 of 11)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning](https://mlsanity.com/paper/2109.12674/) (2021)
- [CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario](https://mlsanity.com/paper/1905.05217/) (2019)
- [Scenic: A Language for Scenario Specification and Scene Generation](https://mlsanity.com/paper/1809.09310/) (2018)
- [A Survey on Traffic Signal Control Methods](https://mlsanity.com/paper/1904.08117/) (2019)
- [Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning](https://mlsanity.com/paper/2308.14284/) (2023)
- [Deep Deterministic Policy Gradient for Urban Traffic Light Control](https://mlsanity.com/paper/1703.09035/) (2017)
- [CBLab: Supporting the Training of Large-scale Traffic Control Policies with Scalable Traffic Simulation](https://mlsanity.com/paper/2210.00896/) (2022)
- [Deep Reinforcement Q-Learning for Intelligent Traffic Signal Control with Partial Detection](https://mlsanity.com/paper/2109.14337/) (2021)

## Built on by (most cited first, 3 of 3)

- [SceneDiffuser++: City-Scale Traffic Simulation via a Generative World Model](https://mlsanity.com/paper/2506.21976/) (2025)
- [Traffic Co-Simulation Framework Empowered by Infrastructure Camera Sensing and Reinforcement Learning](https://mlsanity.com/paper/2412.03925/) (2024)
- [OracleTSC: Oracle-Informed Reward Hurdle and Uncertainty Regularization for Traffic Signal Control](https://mlsanity.com/paper/2605.08516/) (2026)

## Abstract (arXiv)

Traffic signal control (TSC) is crucial for reducing traffic congestion leading to smoother traffic flow, reduced idle time, and mitigated CO2 emissions. In this paper, we explore the computer vision approach for TSC that modulates on-road traffic flows through visual observation. Unlike traditional feature-based approaches, vision-based methods depend much less on heuristics and predefined features, bringing promising potentials for end-to-end learning and optimization of traffic signals. Thus, we introduce a simple traffic simulation framework called TrafficDojo towards vision-based TSC and its benchmark by integrating the microscopic traffic flow provided in SUMO into the 3D driving simulator MetaDrive. This proposed framework offers a versatile traffic environment for in-depth analysis and comprehensive evaluation of traffic signal controllers across diverse traffic conditions and scenarios. We establish and compare baseline algorithms including both traditional and Reinforcement Learning (RL) approaches. This work sheds light on the design and development of vision-based TSC approaches and opens up new research opportunities