1 citations · 1 across the 12 of their papers we have counts for
12 papers
Time-Series Foundation Model Embeddings for Remaining Useful Life Estimation
Amir El-Ghoussani, Michele De Vita, Ronald Naumann +1
Remaining Useful Life (RUL) prediction is essential for industrial predictive maintenance, yet many learning-based approaches rely on extensive feature engineering or large labeled…
Forecasting the Past: Gradient-Based Distribution Shift Detection in Trajectory Prediction
Michele De Vita, Julian Wiederer, Vasileios Belagiannis
Trajectory prediction models often fail in real-world automated driving due to distributional shifts between training and test conditions. Such distributional shifts, whether behav…
GroupEnsemble: Efficient Uncertainty Estimation for DETR-based Object Detection
Yutong Yang, Katarina Popović, Julian Wiederer +3
Detection Transformer (DETR) and its variants show strong performance on object detection, a key task for autonomous systems. However, a critical limitation of these models is that…
LSA: Localized Semantic Alignment for Enhancing Temporal Consistency in Traffic Video Generation
Mirlan Karimov, Teodora Spasojevic, Markus Braun +3
Controllable video generation has emerged as a versatile tool for autonomous driving, enabling realistic synthesis of traffic scenarios. However, existing methods depend on control…
AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework
Yu Yao, Salil Bhatnagar, Markus Mazzola +5
Rare, yet critical, scenarios pose a significant challenge in testing and evaluating autonomous driving planners. Relying solely on real-world driving scenes requires collecting ma…
Diffusion Model Guided Sampling with Pixel-Wise Aleatoric Uncertainty Estimation
Michele De Vita, Vasileios Belagiannis
Despite the remarkable progress in generative modelling, current diffusion models lack a quantitative approach to assess image quality. To address this limitation, we propose to es…
Joint Out-of-Distribution Detection and Uncertainty Estimation for Trajectory Prediction
Julian Wiederer, Julian Schmidt, Ulrich Kressel +2
Despite the significant research efforts on trajectory prediction for automated driving, limited work exists on assessing the prediction reliability. To address this limitation we…
Data-Free Backbone Fine-Tuning for Pruned Neural Networks
Adrian Holzbock, Achyut Hegde, Klaus Dietmayer +1
Model compression techniques reduce the computational load and memory consumption of deep neural networks. After the compression operation, e.g. parameter pruning, the model is nor…
RESET: Revisiting Trajectory Sets for Conditional Behavior Prediction
Julian Schmidt, Pascal Huissel, Julian Wiederer +3
It is desirable to predict the behavior of traffic participants conditioned on different planned trajectories of the autonomous vehicle. This allows the downstream planner to estim…
Multi-Task Edge Prediction in Temporally-Dynamic Video Graphs
Osman Ülger, Julian Wiederer, Mohsen Ghafoorian +2
Graph neural networks have shown to learn effective node representations, enabling node-, link-, and graph-level inference. Conventional graph networks assume static relations betw…
A Benchmark for Unsupervised Anomaly Detection in Multi-Agent Trajectories
Julian Wiederer, Julian Schmidt, Ulrich Kressel +2
Human intuition allows to detect abnormal driving scenarios in situations they never experienced before. Like humans detect those abnormal situations and take countermeasures to pr…
A Multi-Task Recurrent Neural Network for End-to-End Dynamic Occupancy Grid Mapping
Marcel Schreiber, Vasileios Belagiannis, Claudius Gläser +1
A common approach for modeling the environment of an autonomous vehicle are dynamic occupancy grid maps, in which the surrounding is divided into cells, each containing the occupan…