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

TL;DR

This paper introduces TrafficDojo, a versatile simulation framework combining SUMO and MetaDrive for developing and benchmarking vision-based traffic signal control methods, emphasizing the potential of end-to-end learning approaches.

Contribution

It presents a simple, integrated simulation framework for vision-based TSC, enabling comprehensive evaluation and comparison of traditional and RL algorithms.

Findings

Baseline algorithms established and compared

Framework supports diverse traffic scenarios

Highlights potential of vision-based TSC approaches

Abstract

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