Intelligent physical systems (IPSs) architecture must be cognizant, taskable, adaptive, and ethical, particularly when users need to reliably address uncertain, dynamically changing situations. Despite the collection and availability of massive data on IPSs, a significant challenge, faced by the autonomous vehicle (AV) industry is the capability to efficiently anticipate and avoid rare but catastrophic failure events. Developing an information structure that enables users to make the best use of massive data and to model the complexity and rarity of critical situations would contribute to addressing this challenge. This project investigates a novel, unsupervised rare-event learning framework for AV research that utilizes the collected data to assess and alleviate the risks, limitations, and failure modes of IPSs. The framework combines the flexibility in modeling high-dimensional time-series driving data via unsupervised learning algorithms, with the statistical robustness and computational efficiency from rare-event analysis.
The specific goals of the research include: 1) formulate an effective framework to extract safety-critical information from driving database; 2) develop a solid probabilistic measurement scheme for rare but adverse events in AV driving contexts; 3) disseminate the framework and research outcomes to industry and government units in urgent need of reliable evaluation and development methods; and 4) cross-fertilize research thrusts between the academic communities of rare-event estimation and the study of IPSs. The research will merge high-fidelity driving representations learned from data and statistically rigorous rare event analysis so that AV design evaluation in simulated complex driving situations will become highly efficient. It will synthesize machine learning with simulation methodologies, including rare-event analysis, stochastic optimization, and particle methods to boost the performances of state-of-the-art algorithms. The project will foster transformative academic cross-fertilization between researchers in rare-event estimation (operations research) and IPSs (machine learning/robotics), by disseminating the outcomes in top-tier journals, conferences, tutorials and invited seminars. The research will train under-represented minority students at the PIs' institutions, provide opportunities to meet key AV academic and industry players, and show them how to apply mathematical and engineering skills to improve transportation mobility, safety, and roadway efficiency.
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.
