Have We Ever Encountered This Before? Retrieving Out-of-Distribution Road Obstacles from Driving Scenes

no code implementations • 8 Sep 2023 • Youssef Shoeb, Robin Chan, Gesina Schwalbe, Azarm Nowzard, Fatma Güney, Hanno Gottschalk

In this work, we extend beyond identifying OoD road obstacles in video streams and offer a comprehensive approach to extract sequences of OoD road obstacles using text queries, thereby proposing a way of curating a collection of OoD data for subsequent analysis.

Retrieval

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

no code implementations • 10 May 2022 • Julian Wörmann, Daniel Bogdoll, Christian Brunner, Etienne Bührle, Han Chen, Evaristus Fuh Chuo, Kostadin Cvejoski, Ludger van Elst, Philip Gottschall, Stefan Griesche, Christian Hellert, Christian Hesels, Sebastian Houben, Tim Joseph, Niklas Keil, Johann Kelsch, Mert Keser, Hendrik Königshof, Erwin Kraft, Leonie Kreuser, Kevin Krone, Tobias Latka, Denny Mattern, Stefan Matthes, Franz Motzkus, Mohsin Munir, Moritz Nekolla, Adrian Paschke, Stefan Pilar von Pilchau, Maximilian Alexander Pintz, Tianming Qiu, Faraz Qureishi, Syed Tahseen Raza Rizvi, Jörg Reichardt, Laura von Rueden, Alexander Sagel, Diogo Sasdelli, Tobias Scholl, Gerhard Schunk, Gesina Schwalbe, Hao Shen, Youssef Shoeb, Hendrik Stapelbroek, Vera Stehr, Gurucharan Srinivas, Anh Tuan Tran, Abhishek Vivekanandan, Ya Wang, Florian Wasserrab, Tino Werner, Christian Wirth, Stefan Zwicklbauer

The availability of representative datasets is an essential prerequisite for many successful artificial intelligence and machine learning models.

Autonomous Driving BIG-bench Machine Learning