A Holistic Framework Towards Vision-based Traffic Signal Control with Microscopic Simulation
This work addresses traffic congestion for urban planners and researchers by providing a versatile simulation environment, though it is incremental as it builds on existing simulators and methods.
The authors tackled traffic signal control by developing a holistic simulation framework called TrafficDojo, which integrates SUMO and MetaDrive to enable vision-based approaches, and they established baseline algorithms including traditional and reinforcement learning methods for evaluation.
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.