Autonomous vehicles promise to radically change the way people and goods travel. However, AVs won’t reach their full potential through engineering alone. The greatest breakthroughs will come from interdisciplinary teams solving complex challenges together. Michigan State University serves as a hub where experts across fields collaborate to advance mobility innovation. “Interdisciplinary research isn’t just a catchphrase for us,” said Judd Herzer, MSU Mobility director. “Our approach is distinctive because our campuswide network of engineers, business leaders, legal scholars and social scientists work together from the outset. This ensures new technologies are technically possible as well as scalable, economically viable, safe, trusted and ready for deployment by stakeholders in the real world.” Meet five Michigan State University researchers — three in engineering, one in business and one in law — whose expertise in data collection, generative AI and policy analysis is delivering tangible mobility solutions. AVs already have sophisticated systems that collect data and perceive their environment. The next challenge is interpreting that information. Shaunak Bopardikar, associate professor in the Department of Electrical and Computer Engineering, uses simulated autonomous racing competitions to explore how self-driving vehicles make coordinated decisions based on what they know about each other. This work is notable because instead of combining multiple goals into a single score using weighted averages, Bopardikar developed a new method that keeps each goal separate during decision-making. This allows the algorithm to find solutions where each participant has a clear best choice and no one can improve their outcome at another’s expense. “Imagine a self-driving vehicle trying to reach its destination quickly while maintaining a safe distance from other vehicles,” Bopardikar said. “Existing methods combine these goals into a single score. Our approach treats safety as a requirement rather than a preference, allowing us to test AVs closer to their performance limits