3 papers
cs.RO2025
PlanT 2.0: Exposing Biases and Structural Flaws in Closed-Loop Driving
Simon Gerstenecker, Andreas Geiger, Katrin Renz
Most recent work in autonomous driving has prioritized benchmark performance and methodological innovation over in-depth analysis of model failures, biases, and shortcut learning.…
cs.LG2025
MDPO: Overcoming the Training-Inference Divide of Masked Diffusion Language Models
Haoyu He, Katrin Renz, Yong Cao +1
Diffusion language models, as a promising alternative to traditional autoregressive (AR) models, enable faster generation and richer conditioning on bidirectional context. However,…
cs.CV2025
DriveLM: Driving with Graph Visual Question Answering
Chonghao Sima, Katrin Renz, Kashyap Chitta +7
We study how vision-language models (VLMs) trained on web-scale data can be integrated into end-to-end driving systems to boost generalization and enable interactivity with human u…