CAREER: Interactive and Explainable AI for Next-Gen Transportation Systems Management

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This Faculty Early Career Development (CAREER) grant establishes an integrated research and education plan to re-design existing artificial intelligence (AI) algorithms to facilitate multi-task learning on holistic transportation problems, while removing bottlenecks to model interpretability. The research plan will result in an open-source platform that hosts models that are fast, scalable and able to multi-task on different transportation problems. The platform developed has the potential to create fundamentally new sources of data to speed up the safe introduction of autonomous and connective vehicle technologies across different transportation modes. In addition, educational strategies such as inquiry-based learning are used to address gaps in workforce development and systemic imbalances that continue to perpetuate long-held deficit views of communities of color. Unique opportunities for outreach and education will also be created through open-source software development, interactive workshops, and curriculum development. The main thrusts of the project include the following: i) investigate novel edge and distributed cloud computing methods to develop a massive, parallel data platform purposefully built for deploying AI algorithms on big data; ii) investigate new architectures for deploying multipurpose machine learning algorithms, which will enable the development of a decision support system that is interactive and collaborative - with abilities to respond to questions via voice, text, or web interactions and continuously learn from a human expert to enable it to improve its performance over time; and iii) develop a framework for infusing explainability at every stage of the AI lifecycle (training, testing, deployment) by integrating rule-based learning into the building blocks of the multipurpose model – this will provide insight and visibility into black-box AI algorithms, allowing humans to play an active part of the AI process and course-correct when needed. In pursuing these objectives, this CAREER project draws inspiration from theories of deep learning and adaptive computing to design and deploy an end-to-end transportation systems management solution.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.