I will present several recent results of my group on learning robust driving policies that have advanced the state-of-the-art in the CARLA self-driving simulation environment. To generalize across diverse conditions, humans leverage multiple types of situation-specific reasoning and learning strategies. Motivated by this observation, I will first present a framework for learning situational driving policies that effectively captures reasoning under varying types of scenarios and leads to 98% success rate on the CARLA self-driving benchmark as well as state-of-the-art performance on a novel generalization benchmark. Next, I will discuss the problem of covariate shift in imitation learning. I will demonstrate that existing data aggregation techniques for addressing this problem have poor generalization performance, and present a novel approach with empirically better generalization performance. Finally, I will talk about the importance of intermediate representations and attention for learning robust self-driving models.
Andreas Geiger is professor at the University of Tübingen and group leader at the Max Planck Institute for Intelligent Systems. Prior to this, he was a visiting professor at ETH Zürich and a research scientist at MPI-IS. He studied at KIT, EPFL and MIT and received his PhD degree in 2013 from KIT. His research interests are at the intersection of 3D reconstruction, motion estimation, scene understanding and sensory-motor control. He maintains the KITTI vision benchmark and coordinates the ELLIS PhD and PostDoc program. Website: http://www.cvlibs.net/