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Updates about our researchers and their work will be featured here, from media coverage to conference appearances and journal publications.

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Mechanical engineering PhD student Ted Sender configures the physical MRZR vehicle platform, interfaced with a synthetic off-road environment in the Digital Engineering (DE) lab of the Automotive Research Center. A wraparound screen creates an immersive experience. This DE setup supports integrated autonomy and mobility testing in mission-relevant scenarios. Photo: Levi Hutmacher/Michigan Engineering
Off-road autonomy and digital twins: a Q&A with Bogdan Epureanu

Explore how the Automotive Research Center (ARC) leverages digital twins—real-time, data-driven virtual models—to design, test, and operate autonomous off-road vehicles, especially for military and hazardous applications. ARC Director Bogdan Epureanu explains that digital twins enable immersive, cost-effective simulations involving human-machine teams, improving decision-making and system capabilities. The technology is critical for developing safer, more adaptable, and efficient autonomous vehicles in complex, dangerous settings such as disaster relief, mining, and military missions, and is central to ARC’s innovative research supported by the U.S. Army.

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Announcements

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Oct 7, 2024

Dr. Bogdan I. Epureanu, ARC Director: “I’m genuinely excited about the University of Michigan’s ‘Campus Plan 2050: Blueprint for Our Future,’ which sets a clear vision for the growth and development of North Campus, home to ARC and the core of U-M’s mobility research. This forward-looking plan will significantly strengthen our infrastructure, supporting key research areas like autonomous vehicles, electric vehicles, and mobility technologies. By enhancing these capabilities, we are well-positioned to maintain our leadership in mobility research and continue making transformative contributions both nationally and globally.”

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Publications

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Accounting for Test Variability through Sizing Local Domains in Sequential Design Optimization with Concurrent Calibration-based Model Validation