Chen, Tiejin (Arizona State University), SHIRKE, PRITHVI PARAG (ARIZONA STATE UNIVERSITY), Chakravarthi, Bharatesh (Arizona State University), Vaghela, Arpitsinh Rohitkumar (Arizona State University), Da, Longchao (Arizona State University), Lu, Duo (Rider University), Yang, Yezhou (Arizona State University), Wei, Hua (Arizona State University)

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

Scheduled for presentation during the Regular Session "Synthetic datasets in perception" (ThBT4), Thursday, September 26, 2024, 15:50−16:10, Salon 7

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 14, 2024

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 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.