24 citations · 41 across the 19 of their papers we have counts for
19 papers
Chance-Constrained MPPI under State and Dynamic Object Prediction Uncertainty and the Evaluation of Collision Risk Calibration
Benjamin Serfling, Konrad Doll, Kati Radkhah-Lens
Chance-constrained Model Predictive Path Integral (MPPI) control is increasingly adopted for navigation in dynamic environments to explicitly bound collision risk. However, these p…
DD-MDN: Human Trajectory Forecasting with Diffusion-Based Dual Mixture Density Networks and Uncertainty Self-Calibration
Manuel Hetzel, Kerim Turacan, Hannes Reichert +2
Human Trajectory Forecasting (HTF) predicts future human movements from past trajectories and environmental context, with applications in Autonomous Driving, Smart Surveillance, an…
LiDAR Based Semantic Perception for Forklifts in Outdoor Environments
Benjamin Serfling, Hannes Reichert, Lorenzo Bayerlein +2
In this study, we present a novel LiDAR-based semantic segmentation framework tailored for autonomous forklifts operating in complex outdoor environments. Central to our approach i…
Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans
Hannes Reichert, Benjamin Serfling, Elijah Schüssler +3
In recent studies, numerous previous works emphasize the importance of semantic segmentation of LiDAR data as a critical component to the development of driver-assistance systems a…
Reliable Probabilistic Human Trajectory Prediction for Autonomous Applications
Manuel Hetzel, Hannes Reichert, Konrad Doll +1
Autonomous systems, like vehicles or robots, require reliable, accurate, fast, resource-efficient, scalable, and low-latency trajectory predictions to get initial knowledge about f…
Height Change Feature Based Free Space Detection
Steven Schreck, Hannes Reichert, Manuel Hetzel +2
In the context of autonomous forklifts, ensuring non-collision during travel, pick, and place operations is crucial. To accomplish this, the forklift must be able to detect and loc…
Smart Infrastructure: A Research Junction
Manuel Hetzel, Hannes Reichert, Konrad Doll +1
Complex inner-city junctions are among the most critical traffic areas for injury and fatal accidents. The development of highly automated driving (HAD) systems struggles with the…
The IMPTC Dataset: An Infrastructural Multi-Person Trajectory and Context Dataset
Manuel Hetzel, Hannes Reichert, Günther Reitberger +3
Inner-city intersections are among the most critical traffic areas for injury and fatal accidents. Automated vehicles struggle with the complex and hectic everyday life within thos…
Sensor Equivariance by LiDAR Projection Images
Hannes Reichert, Manuel Hetzel, Steven Schreck +2
In this work, we propose an extension of conventional image data by an additional channel in which the associated projection properties are encoded. This addresses the issue of sen…
Cyclist Trajectory Forecasts by Incorporation of Multi-View Video Information
Stefan Zernetsch, Oliver Trupp, Viktor Kress +2
This article presents a novel approach to incorporate visual cues from video-data from a wide-angle stereo camera system mounted at an urban intersection into the forecast of cycli…
Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users' Trajectories
Viktor Kress, Fabian Jeske, Stefan Zernetsch +2
In this article, an approach for probabilistic trajectory forecasting of vulnerable road users (VRUs) is presented, which considers past movements and the surrounding scene. Past m…
Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems
Hannes Reichert, Lukas Lang, Kevin Rösch +6
Full-stack autonomous driving perception modules usually consist of data-driven models based on multiple sensor modalities. However, these models might be biased to the sensor setu…
Cyclist Intention Detection: A Probabilistic Approach
Stefan Zernetsch, Hannes Reichert, Viktor Kress +2
This article presents a holistic approach for probabilistic cyclist intention detection. A basic movement detection based on motion history images (MHI) and a residual convolutiona…
Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence
Maarten Bieshaar, Günther Reitberger, Stefan Zernetsch +3
Vulnerable road users (VRUs, i.e. cyclists and pedestrians) will play an important role in future traffic. To avoid accidents and achieve a highly efficient traffic flow, it is imp…
Intentions of Vulnerable Road Users - Detection and Forecasting by Means of Machine Learning
Michael Goldhammer, Sebastian Köhler, Stefan Zernetsch +3
Avoiding collisions with vulnerable road users (VRUs) using sensor-based early recognition of critical situations is one of the manifold opportunities provided by the current devel…
Highly Automated Learning for Improved Active Safety of Vulnerable Road Users
Maarten Bieshaar, Günther Reitberger, Viktor Kreß +4
Highly automated driving requires precise models of traffic participants. Many state of the art models are currently based on machine learning techniques. Among others, the require…
Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network
Stefan Zernetsch, Viktor Kress, Bernhard Sick +1
In this article, we present a novel approach to detect starting motions of cyclists in real world traffic scenarios based on Motion History Images (MHIs). The method uses a deep Co…
Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble
Maarten Bieshaar, Stefan Zernetsch, Andreas Hubert +2
In future, vehicles and other traffic participants will be interconnected and equipped with various types of sensors, allowing for cooperation on different levels, such as situatio…
Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure
Günther Reitberger, Stefan Zernetsch, Maarten Bieshaar +3
In future traffic scenarios, vehicles and other traffic participants will be interconnected and equipped with various types of sensors, allowing for cooperation based on data or in…