  Traffic signal control (TSC) is crucial for reducing traffic congestion that
leads to smoother traffic flow, reduced idling time, and mitigated CO2
emissions. In this study, 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 holistic traffic
simulation framework called TrafficDojo towards vision-based TSC and its
benchmarking by integrating the microscopic traffic flow provided in SUMO into
the 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
Reinforecment Learning (RL) approaches. This work sheds insights into the
design and development of vision-based TSC approaches and open up new research
opportunities. All the code and baselines will be made publicly available.
