29 citations · 121 across the 63 of their papers we have counts for
27 papers · 1 filter
FutureLift: Lifting Trajectory Proposals into Bidirectional Future Graphs for Motion Forecasting
Daniel Grimm, J. Marius Zöllner
Accurate motion prediction is critical for safe autonomous driving, yet even strong forecasters leave residual errors in their multimodal proposals. Existing refiners compress comp…
Temporal Residual Bottleneck for Robust Asynchronous Collaborative Perception
Melih Yazgan, Ahmed Abouelazm, J. Marius Zöllner
Collaborative perception extends the sensing range of autonomous vehicles, but its performance degrades when shared features arrive stale or incomplete. Most latency-robust methods…
Real-World On-Vehicle Evaluation of Embedding-Based Anomaly Detection
Albert Schotschneider, Daniel Bogdoll, Svetlana Pavlitska +2
Detecting anomalies in traffic scenes is crucial for ensuring safety in autonomous driving, yet collecting representative anomalous data remains challenging. Existing anomaly detec…
Design and Behavior of Sparse Mixture-of-Experts Layers in CNN-based Semantic Segmentation
Svetlana Pavlitska, Haixi Fan, Konstantin Ditschuneit +1
Sparse mixture-of-experts (MoE) layers have been shown to substantially increase model capacity without a proportional increase in computational cost and are widely used in transfo…
Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers
Svetlana Pavlitska, Haixi Fan, Konstantin Ditschuneit +1
Robustifying convolutional neural networks (CNNs) against adversarial attacks remains challenging and often requires resource-intensive countermeasures. We explore the use of spars…
Extracting Uncertainty Estimates from Mixtures of Experts for Semantic Segmentation
Svetlana Pavlitska, Beyza Keskin, Alwin Faßbender +2
Estimating accurate and well-calibrated predictive uncertainty is important for enhancing the reliability of computer vision models, especially in safety-critical applications like…
EffiComm: Bandwidth Efficient Multi Agent Communication
Melih Yazgan, Allen Xavier Arasan, J. Marius Zöllner
Collaborative perception allows connected vehicles to exchange sensor information and overcome each vehicle's blind spots. Yet transmitting raw point clouds or full feature maps ov…
LanePerf: a Performance Estimation Framework for Lane Detection
Yin Wu, Daniel Slieter, Ahmed Abouelazm +2
Lane detection is a critical component of Advanced Driver-Assistance Systems (ADAS) and Automated Driving System (ADS), providing essential spatial information for lateral control.…
Contrast & Compress: Learning Lightweight Embeddings for Short Trajectories
Abhishek Vivekanandan, Christian Hubschneider, J. Marius Zöllner
The ability to retrieve semantically and directionally similar short-range trajectories with both accuracy and efficiency is foundational for downstream applications such as motion…
The ATLAS of Traffic Lights: A Reliable Perception Framework for Autonomous Driving
Rupert Polley, Nikolai Polley, Dominik Heid +3
Traffic light perception is an essential component of the camera-based perception system for autonomous vehicles, enabling accurate detection and interpretation of traffic lights t…
Self-Supervised Pretraining for Aerial Road Extraction
Rupert Polley, Sai Vignesh Abishek Deenadayalan, J. Marius Zöllner
Deep neural networks for aerial image segmentation require large amounts of labeled data, but high-quality aerial datasets with precise annotations are scarce and costly to produce…
From One to the Power of Many: Invariance to Multi-LiDAR Perception from Single-Sensor Datasets
Marc Uecker, J. Marius Zöllner
Recently, LiDAR segmentation methods for autonomous vehicles, powered by deep neural networks, have experienced steep growth in performance on classic benchmarks, such as nuScenes…
Efficient Data Representation for Motion Forecasting: A Scene-Specific Trajectory Set Approach
Abhishek Vivekanandan, J. Marius Zöllner
Representing diverse and plausible future trajectories is critical for motion forecasting in autonomous driving. However, efficiently capturing these trajectories in a compact set…
Iterative Filter Pruning for Concatenation-based CNN Architectures
Svetlana Pavlitska, Oliver Bagge, Federico Peccia +2
Model compression and hardware acceleration are essential for the resource-efficient deployment of deep neural networks. Modern object detectors have highly interconnected convolut…
A Survey on Intermediate Fusion Methods for Collaborative Perception Categorized by Real World Challenges
Melih Yazgan, Thomas Graf, Min Liu +2
This survey analyzes intermediate fusion methods in collaborative perception for autonomous driving, categorized by real-world challenges. We examine various methods, detailing the…
Traffic Light Recognition using Convolutional Neural Networks: A Survey
Svetlana Pavlitska, Nico Lambing, Ashok Kumar Bangaru +1
Real-time traffic light recognition is essential for autonomous driving. Yet, a cohesive overview of the underlying model architectures for this task is currently missing. In this…
Adversarial Attacks on Traffic Sign Recognition: A Survey
Svetlana Pavlitska, Nico Lambing, J. Marius Zöllner
Traffic sign recognition is an essential component of perception in autonomous vehicles, which is currently performed almost exclusively with deep neural networks (DNNs). However,…
From Model-Based to Data-Driven Simulation: Challenges and Trends in Autonomous Driving
Ferdinand Mütsch, Helen Gremmelmaier, Nicolas Becker +3
Simulation is an integral part in the process of developing autonomous vehicles and advantageous for training, validation, and verification of driving functions. Even though simula…
Analyzing Deep Learning Representations of Point Clouds for Real-Time In-Vehicle LiDAR Perception
Marc Uecker, Tobias Fleck, Marcel Pflugfelder +1
LiDAR sensors are an integral part of modern autonomous vehicles as they provide an accurate, high-resolution 3D representation of the vehicle's surroundings. However, it is comput…
Suppress with a Patch: Revisiting Universal Adversarial Patch Attacks against Object Detection
Svetlana Pavlitskaya, Jonas Hendl, Sebastian Kleim +3
Adversarial patch-based attacks aim to fool a neural network with an intentionally generated noise, which is concentrated in a particular region of an input image. In this work, we…
Experiments on Anomaly Detection in Autonomous Driving by Forward-Backward Style Transfers
Daniel Bogdoll, Meng Zhang, Maximilian Nitsche +1
Great progress has been achieved in the community of autonomous driving in the past few years. As a safety-critical problem, however, anomaly detection is a huge hurdle towards a l…
Adversarial Vulnerability of Temporal Feature Networks for Object Detection
Svetlana Pavlitskaya, Nikolai Polley, Michael Weber +1
Taking into account information across the temporal domain helps to improve environment perception in autonomous driving. However, it has not been studied so far whether temporally…
Feasibility of Inconspicuous GAN-generated Adversarial Patches against Object Detection
Svetlana Pavlitskaya, Bianca-Marina Codău, J. Marius Zöllner
Standard approaches for adversarial patch generation lead to noisy conspicuous patterns, which are easily recognizable by humans. Recent research has proposed several approaches to…
Sparsely-gated Mixture-of-Expert Layers for CNN Interpretability
Svetlana Pavlitska, Christian Hubschneider, Lukas Struppek +1
Sparsely-gated Mixture of Expert (MoE) layers have been recently successfully applied for scaling large transformers, especially for language modeling tasks. An intriguing side eff…