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Katrin Renz
Katrin Renz
University of Tübingen, Tübingen AI Center & IMPRS-IS
Bestätigte E-Mail-Adresse bei uni-tuebingen.de - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Transfuser: Imitation with transformer-based sensor fusion for autonomous driving
K Chitta, A Prakash, B Jaeger, Z Yu, K Renz, A Geiger
IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (11 …, 2022
2222022
Drivelm: Driving with graph visual question answering
C Sima, K Renz, K Chitta, L Chen, H Zhang, C Xie, P Luo, A Geiger, H Li
arXiv preprint arXiv:2312.14150, 2023
82*2023
Plant: Explainable planning transformers via object-level representations
K Renz, K Chitta, OB Mercea, A Koepke, Z Akata, A Geiger
arXiv preprint arXiv:2210.14222, 2022
732022
King: Generating safety-critical driving scenarios for robust imitation via kinematics gradients
N Hanselmann, K Renz, K Chitta, A Bhattacharyya, A Geiger
European Conference on Computer Vision, 335-352, 2022
672022
Sign language segmentation with temporal convolutional networks
K Renz, NC Stache, S Albanie, G Varol
ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021
292021
Sign segmentation with changepoint-modulated pseudo-labelling
K Renz, NC Stache, N Fox, G Varol, S Albanie
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021
202021
On offline evaluation of 3d object detection for autonomous driving
T Schreier, K Renz, A Geiger, K Chitta
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023
72023
CarLLaVA: Vision language models for camera-only closed-loop driving
K Renz, L Chen, AM Marcu, J Hünermann, B Hanotte, A Karnsund, ...
arXiv preprint arXiv:2406.10165, 2024
32024
Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?
M Hallgarten, J Zapata, M Stoll, K Renz, A Zell
arXiv preprint arXiv:2404.07569, 2024
22024
Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?
A Zell, K Renz, M Stoll, J Zapata, M Hallgarten
arXiv, 2024
2024
Supplementary Material for PlanT: Explainable Planning Transformers via Object-Level Representations
K Renz, K Chitta, OB Mercea, AS Koepke, Z Akata, A Geiger
Supplementary Material for KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradients
N Hanselmann, K Renz, K Chitta, A Bhattacharyya, A Geiger
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