12 citations · 21 across the 16 of their papers we have counts for
17 papers
2.5D Object Detection for Intelligent Roadside Infrastructure
Nikolai Polley, Yacin Boualili, Ferdinand Mütsch +3
On-board sensors of autonomous vehicles can be obstructed, occluded, or limited by restricted fields of view, complicating downstream driving decisions. Intelligent roadside infras…
Exploring Semantic Clustering and Similarity Search for Heterogeneous Traffic Scenario Graph
Ferdinand Mütsch, Maximilian Zipfl, Nikolai Polley +1
Scenario-based testing is an indispensable instrument for the comprehensive validation and verification of automated vehicles (AVs). However, finding a manageable and finite, yet r…
DigiT4TAF -- Bridging Physical and Digital Worlds for Future Transportation Systems
Maximilian Zipfl, Pascal Zwick, Patrick Schulz +22
In the future, mobility will be strongly shaped by the increasing use of digitalization. Not only will individual road users be highly interconnected, but also the road and associa…
Empowering Autonomous Shuttles with Next-Generation Infrastructure
Sven Ochs, Melih Yazgan, Rupert Polley +9
As cities strive to address urban mobility challenges, combining autonomous transportation technologies with intelligent infrastructure presents an opportunity to transform how peo…
CoCar NextGen: a Multi-Purpose Platform for Connected Autonomous Driving Research
Marc Heinrich, Maximilian Zipfl, Marc Uecker +9
Real world testing is of vital importance to the success of automated driving. While many players in the business design purpose build testing vehicles, we designed and build a mod…
Scene-Extrapolation: Generating Interactive Traffic Scenarios
Maximilian Zipfl, Barbara Schütt, J. Marius Zöllner
Verifying highly automated driving functions can be challenging, requiring identifying relevant test scenarios. Scenario-based testing will likely play a significant role in verify…
One Stack to Rule them All: To Drive Automated Vehicles, and Reach for the 4th level
Sven Ochs, Jens Doll, Daniel Grimm +15
Most automated driving functions are designed for a specific task or vehicle. Most often, the underlying architecture is fixed to specific algorithms to increase performance. There…
Heterogeneous Graph-based Trajectory Prediction using Local Map Context and Social Interactions
Daniel Grimm, Maximilian Zipfl, Felix Hertlein +7
Precisely predicting the future trajectories of surrounding traffic participants is a crucial but challenging problem in autonomous driving, due to complex interactions between tra…
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…
Utilizing Hybrid Trajectory Prediction Models to Recognize Highly Interactive Traffic Scenarios
Maximilian Zipfl, Sven Spickermann, J. Marius Zöllner
Autonomous vehicles hold great promise in improving the future of transportation. The driving models used in these vehicles are based on neural networks, which can be difficult to…
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…
Relation-based Motion Prediction using Traffic Scene Graphs
Maximilian Zipfl, Felix Hertlein, Achim Rettinger +5
Representing relevant information of a traffic scene and understanding its environment is crucial for the success of autonomous driving. Modeling the surrounding of an autonomous c…
Self Supervised Clustering of Traffic Scenes using Graph Representations
Maximilian Zipfl, Moritz Jarosch, J. Marius Zöllner
Examining graphs for similarity is a well-known challenge, but one that is mandatory for grouping graphs together. We present a data-driven method to cluster traffic scenes that is…
Fingerprint of a Traffic Scene: an Approach for a Generic and Independent Scene Assessment
Maximilian Zipfl, Barbara Schütt, J. Marius Zöllner +1
A major challenge in the safety assessment of automated vehicles is to ensure that risk for all traffic participants is as low as possible. A concept that is becoming increasingly…
Towards Traffic Scene Description: The Semantic Scene Graph
Maximilian Zipfl, J. Marius Zöllner
For the classification of traffic scenes, a description model is necessary that can describe the scene in a uniform way, independent of its domain. A model to describe a traffic sc…
Reliving the Dataset: Combining the Visualization of Road Users' Interactions with Scenario Reconstruction in Virtual Reality
Lars Töttel, Maximilian Zipfl, Daniel Bogdoll +2
One core challenge in the development of automated vehicles is their capability to deal with a multitude of complex trafficscenarios with many, hard to predict traffic participants…