PlanT: Explainable Planning Transformers via Object-Level Representations Computer Vision News 10 Best Presentation ICVSS PlanT, Katrin’s paper from last year’s Conference on Robot Learning (CoRL), proposed a state-of-the-art learning-based planner for autonomous driving. Autonomous driving can involve a modular pipeline comprising sensor data, a perception module for perceiving the environment around you, and a planner. The planner considers the perception output and the 3D detection of surrounding vehicles and determines the optimal trajectory for the ego vehicle. Katrin Renz is a PhD student at the University of Tübingen in Andreas Geiger’s lab, working on the combination of autonomous driving and language. Last month, she won the Best Presentation prize at Sicily’s International Computer Vision Summer School (ICVSS). She speaks to us about her awardwinning work.
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