8 papers · 1 filter
VOLA: Improving Open-World Driving by VLM-Based Semantic Attribute Prediction
Yuchen Zhang, Yuan Gao, Sebastian Schmidt +1
Driving in the real world is open-world: a car may encounter a fallen mattress, a deer, or other objects outside its training data. Naming them is not enough. The system must know…
EgoDyn-Bench: Evaluating Ego-Motion Understanding in Vision-Centric Foundation Models for Autonomous Driving
Finn Rasmus Schäfer, Yuan Gao, Dingrui Wang +5
While Vision-Language Models (VLMs) have advanced high-level reasoning in autonomous driving, their ability to ground this reasoning in the underlying physics of ego-motion remains…
Scalable Object Detection in the Car Interior With Vision Foundation Models
Sebastian Schmidt, Bálint Mészáros, Ahmet Firintepe +1
AI tasks in the car interior like identifying and localizing externally introduced objects is crucial for response quality of personal assistants. However, computational resources…
Unexplored flaws in multiple-choice VQA evaluations
Fabio Rosenthal, Sebastian Schmidt, Thorsten Graf +3
Multimodal Large Language Models (MLLMs) demonstrate strong capabilities in handling image-text inputs. A common way to assess this ability is through multiple-choice Visual Questi…
A Machine Learning Perspective on Automated Driving Corner Cases
Sebastian Schmidt, Julius Körner, Stephan Günnemann
For high-stakes applications, like autonomous driving, a safe operation is necessary to prevent harm, accidents, and failures. Traditionally, difficult scenarios have been categori…
A Unified Approach Towards Active Learning and Out-of-Distribution Detection
Sebastian Schmidt, Leonard Schenk, Leo Schwinn +1
When applying deep learning models in open-world scenarios, active learning (AL) strategies are crucial for identifying label candidates from a nearly infinite amount of unlabeled…