The Robot Brains Podcast
Cathy Wu of MIT on the future of our highways and roads
Feb 9, 2022
Guest Cathy Wu, Professor focusing on building machine-learning for future roadways and infrastructure, discusses the potential of using machine learning to predict ideal infrastructure, eliminate traffic congestion, and ensure smooth travel and safe roadways as transportation evolves. They explore the potential of self-driving cars to improve traffic situations, using existing cars and infrastructure for traffic control, and the future of transportation infrastructure and algorithmic solutions. Valuable advice for PhD students and new faculty members is also provided.
34:11
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Quick takeaways
- Machine learning can be used to predict the ideal infrastructure for future mobility and eliminate traffic congestion.
- Reinforcement learning in self-driving cars can significantly reduce congestion and enhance overall transportation system efficiency.
Deep dives
Using machine learning to predict the ideal infrastructure for future mobility
MIT professor Kathy Wu is utilizing machine learning to predict the ideal infrastructure for future mobility. Her research focuses on determining the cost of building this infrastructure and finding ways to eliminate traffic congestion. By incorporating machine learning, Wu aims to ensure self-driving cars never have to experience bumper-to-bumper gridlock, ultimately improving the efficiency of our transportation system.
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