SynTraC: A Synthetic Dataset for Traffic Signal Control from Traffic Monitoring Cameras

TL;DR

SynTraC is a pioneering public dataset of real-style traffic images from the CARLA simulator, designed to advance image-based traffic signal control research by providing diverse scenarios and annotations.

Contribution

It introduces the first comprehensive image-based traffic signal control dataset with diverse scenarios, annotations, and reward annotations, bridging the gap between simulation and real-world applications.

Findings

Image-based control remains challenging compared to feature-based methods.

SynTraC enables development and benchmarking of traffic signal control algorithms.

The dataset supports diverse weather and lighting conditions for robust algorithm training.

Abstract

This paper introduces SynTraC, the first public image-based traffic signal control dataset, aimed at bridging the gap between simulated environments and real-world traffic management challenges. Unlike traditional datasets for traffic signal control which aim to provide simplified feature vectors like vehicle counts from traffic simulators, SynTraC provides real-style images from the CARLA simulator with annotated features, along with traffic signal states. This image-based dataset comes with diverse real-world scenarios, including varying weather and times of day. Additionally, SynTraC also provides different reward values for advanced traffic signal control algorithms like reinforcement learning. Experiments with SynTraC demonstrate that it is still an open challenge to image-based traffic signal control methods compared with feature-based control methods, indicating our dataset can further guide the development of future algorithms. The code for this paper can be found in \url{https://github.com/DaRL-LibSignal/SynTraC}.SynTraC