29 citations · 121 across the 63 of their papers we have counts for
15 papers · 1 filter
On The Impact of Replacing Private Cars with Autonomous Shuttles: An Agent-Based Approach
Daniel Bogdoll, Louis Karsch, Jennifer Amritzer +1
The European Green Deal aims to achieve climate neutrality by 2050, which demands improved emissions efficiency from the transportation industry. This study uses an agent-based sim…
MUVO: A Multimodal Generative World Model for Autonomous Driving with Geometric Representations
Daniel Bogdoll, Yitian Yang, Tim Joseph +2
World models for autonomous driving have the potential to dramatically improve the reasoning capabilities of today's systems. However, most works focus on camera data, with only a…
Conditional Unscented Autoencoders for Trajectory Prediction
Faris Janjoš, Marcel Hallgarten, Anthony Knittel +3
The CVAE is one of the most widely-used models in trajectory prediction for AD. It captures the interplay between a driving context and its ground-truth future into a probabilistic…
KI-PMF: Knowledge Integrated Plausible Motion Forecasting
Abhishek Vivekanandan, Ahmed Abouelazm, Philip Schörner +1
Accurately forecasting the motion of traffic actors is crucial for the deployment of autonomous vehicles at a large scale. Current trajectory forecasting approaches primarily conce…
Traffic Scene Similarity: a Graph-based Contrastive Learning Approach
Maximilian Zipfl, Moritz Jarosch, J. Marius Zöllner
Ensuring validation for highly automated driving poses significant obstacles to the widespread adoption of highly automated vehicles. Scenario-based testing offers a potential solu…
Exploring the Potential of World Models for Anomaly Detection in Autonomous Driving
Daniel Bogdoll, Lukas Bosch, Tim Joseph +3
In recent years there have been remarkable advancements in autonomous driving. While autonomous vehicles demonstrate high performance in closed-set conditions, they encounter diffi…
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,…
Unscented Autoencoder
Faris Janjoš, Lars Rosenbaum, Maxim Dolgov +1
The Variational Autoencoder (VAE) is a seminal approach in deep generative modeling with latent variables. Interpreting its reconstruction process as a nonlinear transformation of…
Bridging the Gap Between Multi-Step and One-Shot Trajectory Prediction via Self-Supervision
Faris Janjoš, Max Keller, Maxim Dolgov +1
Accurate vehicle trajectory prediction is an unsolved problem in autonomous driving with various open research questions. State-of-the-art approaches regress trajectories either in…
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…
Inverse Universal Traffic Quality -- a Criticality Metric for Crowded Urban Traffic Scenes
Barbara Schütt, Maximilian Zipfl, J. Marius Zöllner +1
An essential requirement for scenario-based testing the identification of critical scenes and their associated scenarios. However, critical scenes, such as collisions, occur compar…
A Comprehensive Review on Ontologies for Scenario-based Testing in the Context of Autonomous Driving
Maximilian Zipfl, Nina Koch, J. Marius Zöllner
The verification and validation of autonomous driving vehicles remains a major challenge due to the high complexity of autonomous driving functions. Scenario-based testing is a pro…
Perception Datasets for Anomaly Detection in Autonomous Driving: A Survey
Daniel Bogdoll, Svenja Uhlemeyer, Kamil Kowol +1
Deep neural networks (DNN) which are employed in perception systems for autonomous driving require a huge amount of data to train on, as they must reliably achieve high performance…
Impact, Attention, Influence: Early Assessment of Autonomous Driving Datasets
Daniel Bogdoll, Jonas Hendl, Felix Schreyer +3
Autonomous Driving (AD), the area of robotics with the greatest potential impact on society, has gained a lot of momentum in the last decade. As a result of this, the number of dat…