2 citations · 4 across the 13 of their papers we have counts for
15 papers · 1 filter
BodyBridge: Grounded Body Layers for Sim-to-Real Translation of Vulnerable Road Users
Mark Azer, Fabian B. Flohr
Simulation-to-real driving video diffusion models can translate photorealistic scenes, but they typically represent pedestrians and cyclists, vulnerable road users (VRUs), as gener…
From Laboratory to Road: Evaluating Wearable Gaze Accuracy for Driving
William Engel, Fabian Flohr
Bird's-eye-view (BEV) representations have become a widely used interface between perception and planning in autonomous driving, but they encode what is in a scene, not what is beh…
Speed in the Blind Spot: An Interpretability Analysis of Dynamic Perception in VLMs for Autonomous Driving
Katharina Winter, Stefan Englmeier, Fabian B. Flohr
Vision-Language Models are increasingly used in autonomous-driving systems, yet their ability to recover dynamic physical state from visual input remains insufficiently characteriz…
CommandLM: Data driven behavior level descriptor for ego vehicles
Boris Tokic, Constantin Selzer, Fabian B. Flohr
As autonomous driving systems move toward real-world deployment, interpretable, behavior-level decision-making is essential for safety, trust, and regulation. We introduce CommandL…
Auto-Labelling-Based Domain Transfer for 3D Object Detection on a Bicycle-Mounted LiDAR Platform
Mario Finkbeiner, Max A. Buettner, Kanak Mazumder +1
Reliable 3D perception of vulnerable road users (VRUs) such as cyclists and pedestrians is essential for their safety in urban traffic and a core requirement for autonomous driving…
LIE: LiDAR-only HD Map Construction with Intensity Enhancement via Online Knowledge Distillation
Kanak Mazumder, Fabian B. Flohr
Online High-Definition (HD) map construction is a key component of autonomous driving. Recent methods rely on multi-view camera images for cost-effective HD map segmentation, but c…
WorldVLM: Combining World Model Forecasting and Vision-Language Reasoning
Stefan Englmeier, Katharina Winter, Fabian B. Flohr
Autonomous driving systems depend on on models that can reason about high-level scene contexts and accurately predict the dynamics of their surrounding environment. Vision- Languag…
MASAR: Motion-Appearance Synergy Refinement for Joint Detection and Trajectory Forecasting
Mohammed Amine Bencheikh Lehocine, Julian Schmidt, Frank Moosmann +2
Classical autonomous driving systems connect perception and prediction modules via hand-crafted bounding-box interfaces, limiting information flow and propagating errors to downstr…
BikeActions: An Open Platform and Benchmark for Cyclist-Centric VRU Action Recognition
Max A. Buettner, Kanak Mazumder, Luca Koecher +3
Anticipating the intentions of Vulnerable Road Users (VRUs) is a critical challenge for safe autonomous driving (AD) and mobile robotics. While current research predominantly focus…
SatMap: Revisiting Satellite Maps as Prior for Online HD Map Construction
Kanak Mazumder, Fabian B. Flohr
Online high-definition (HD) map construction is an essential part of a safe and robust end-to-end autonomous driving (AD) pipeline. Onboard camera-based approaches suffer from limi…
DeepUrban: Interaction-Aware Trajectory Prediction and Planning for Automated Driving by Aerial Imagery
Constantin Selzer, Fabian B. Flohr
The efficacy of autonomous driving systems hinges critically on robust prediction and planning capabilities. However, current benchmarks are impeded by a notable scarcity of scenar…
HABIT: Human Action Benchmark for Interactive Traffic in CARLA
Mohan Ramesh, Mark Azer, Fabian B. Flohr
Current autonomous driving (AD) simulations are critically limited by their inadequate representation of realistic and diverse human behavior, which is essential for ensuring safet…
Context-based Motion Retrieval using Open Vocabulary Methods for Autonomous Driving
Stefan Englmeier, Max A. Büttner, Katharina Winter +1
Autonomous driving systems must operate reliably in safety-critical scenarios, particularly those involving unusual or complex behavior by Vulnerable Road Users (VRUs). Identifying…
BEV-LLM: Leveraging Multimodal BEV Maps for Scene Captioning in Autonomous Driving
Felix Brandstaetter, Erik Schuetz, Katharina Winter +1
Autonomous driving technology has the potential to transform transportation, but its wide adoption depends on the development of interpretable and transparent decision-making syste…
Generative AI for Autonomous Driving: A Review
Katharina Winter, Abhishek Vivekanandan, Rupert Polley +17
Generative AI (GenAI) is rapidly advancing the field of Autonomous Driving (AD), extending beyond traditional applications in text, image, and video generation. We explore how gene…