Predictive Autonomy

For safe navigation and planning, it is important for autonomous systems to accurately predict how other agents in their environments are going to move over time. Our research uses various machine-learning techniques ranging from Gaussian Processes to deep neural networks to enable systems to predict the future states of their environment so that they can proactively plan safe trajectories. These techniques have been applied in various application areas such as aerial robotics, autonomous driving, and underwater robotics.

Relevant Publications

• M. Toyungyernsub, E. Yel, J.Li, M. Kochenderfer, “Dynamics-Aware Spatiotemporal Occupancy Prediction in Urban Environments”, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2022 PDF
• M. Cleaveland, E. Yel, Y. Kantaros, I. Lee, N. Bezzo, “Learning Enabled Fast Planning and Control in Dynamic Environments with Intermittent Information”, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2022 PDF
• L. Kruse, E. Yel, R. Senanayake, M. Kochenderfer, “Uncertainty-Aware Online Merge Planning with Learned Driver Behavior”, IEEE International Conference on Intelligent Transportation Systems (ITSC), 2022 PDF
• E. Yel and N. Bezzo, “Fast Run-time Monitoring, Replanning, and Recovery for Safe Autonomous System Operations” 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, pp. 1661-1667. PDF