3 citations · 4 across the 6 of their papers we have counts for
6 papers · 1 filter
TARS: Traffic-Aware Radar Scene Flow Estimation
Jialong Wu, Marco Braun, Dominic Spata +1
Scene flow provides crucial motion information for autonomous driving. Recent LiDAR scene flow models utilize the rigid-motion assumption at the instance level, assuming objects ar…
Benchmarking Vision Foundation Models for Input Monitoring in Autonomous Driving
Mert Keser, Halil Ibrahim Orhan, Niki Amini-Naieni +3
Deep neural networks (DNNs) remain challenged by distribution shifts in complex open-world domains like automated driving (AD): Robustness against yet unknown novel objects (semant…
Unveiling Ontological Commitment in Multi-Modal Foundation Models
Mert Keser, Gesina Schwalbe, Niki Amini-Naieni +2
Ontological commitment, i.e., used concepts, relations, and assumptions, are a corner stone of qualitative reasoning (QR) models. The state-of-the-art for processing raw inputs, th…
On the Potential of Open-Vocabulary Models for Object Detection in Unusual Street Scenes
Sadia Ilyas, Ido Freeman, Matthias Rottmann
Out-of-distribution (OOD) object detection is a critical task focused on detecting objects that originate from a data distribution different from that of the training data. In this…
SparseRadNet: Sparse Perception Neural Network on Subsampled Radar Data
Jialong Wu, Mirko Meuter, Markus Schoeler +1
Radar-based perception has gained increasing attention in autonomous driving, yet the inherent sparsity of radars poses challenges. Radar raw data often contains excessive noise, w…
OoDIS: Anomaly Instance Segmentation and Detection Benchmark
Alexey Nekrasov, Rui Zhou, Miriam Ackermann +3
Safe navigation of self-driving cars and robots requires a precise understanding of their environment. Training data for perception systems cannot cover the wide variety of objects…