Bogdoll, Daniel (FZI Research Center for Information Technology), Ananta, Rajanikant Patnaik (University of Michigan), Giridharan, Abeyankar (University of Michigan), Moore, Isabel (Texas A&M University), Stevens, Gregory (University of Michigan), Liu, Henry X. (University of Michigan)

Mcity Data Engine: Iterative Model Improvement through Open-Vocabulary Data Selection

Scheduled for presentation during the Regular Session "S04b-Intelligent Perception and Detection Technologies for Connected Mobility" (WE-EA-T4), Wednesday, November 19, 2025, 14:10−14:30, Surfers Paradise 1

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 19, 2025

Abstract

With an ever-increasing availability of data, it has become more and more challenging to select and label appropriate samples for the training of machine learning models. It is especially difficult to detect long-tail classes of interest in large amounts of unlabeled data. This holds especially true for Intelligent Transportation Systems (ITS), where vehicle fleets and roadside perception systems generate an abundance of raw data. While industrial, proprietary data engines for such iterative data selection and model training processes exist, researchers and the open-source community suffer from a lack of an openly available system. We present the Mcity Data Engine, which provides modules for the complete data-based development cycle, beginning at the data acquisition phase and ending at the model deployment stage. The Mcity Data Engine focuses on rare and novel classes through an open-vocabulary data selection process. All code is publicly available on GitHub under an MIT license: https://github.com/mcity/mcity_data_engine