5 papers
Embodied4C: Measuring What Matters for Embodied Vision-Language Navigation
Tin Stribor Sohn, Maximilian Dillitzer, Jason J. Corso +1
Vision-language navigation requires agents to reason and act under constraints of embodiment. While vision-language models (VLMs) demonstrate strong generalization, current benchma…
SNOW: Spatio-Temporal Scene Understanding with World Knowledge for Open-World Embodied Reasoning
Tin Stribor Sohn, Maximilian Dillitzer, Jason J. Corso +1
Autonomous robotic systems require spatio-temporal understanding of dynamic environments to ensure reliable navigation and interaction. While Vision-Language Models (VLMs) provide…
R4: Retrieval-Augmented Reasoning for Vision-Language Models in 4D Spatio-Temporal Space
Tin Stribor Sohn, Maximilian Dillitzer, Jason J. Corso +1
Humans perceive and reason about their surroundings in four dimensions by building persistent, structured internal representations that encode semantic meaning, spatial layout, and…
SAFERad: A Framework to Enable Radar Data for Safety-Relevant Perception Tasks
Tim Brühl, Jenny Glönkler, Robin Schwager +3
Radar sensors play a crucial role for perception systems in automated driving but suffer from a high level of noise. In the past, this could be solved by strict filters, which remo…
A Framework for a Capability-driven Evaluation of Scenario Understanding for Multimodal Large Language Models in Autonomous Driving
Tin Stribor Sohn, Philipp Reis, Maximilian Dillitzer +3
Multimodal large language models (MLLMs) hold the potential to enhance autonomous driving by combining domain-independent world knowledge with context-specific language guidance. T…