1.4k citations · 3k across the 64 of their papers we have counts for
14 papers · 1 filter
The Adaptive Labeled Multi-Bernoulli Filter
Andreas Danzer, Stephan Reuter, Klaus Dietmayer
This paper proposes a new multi-Bernoulli filter called the Adaptive Labeled Multi-Bernoulli filter. It combines the relative strengths of the known Delta-Generalized Labeled Multi…
Environment Perception Framework Fusing Multi-Object Tracking, Dynamic Occupancy Grid Maps and Digital Maps
Fabian Gies, Andreas Danzer, Klaus Dietmayer
Autonomously driving vehicles require a complete and robust perception of the local environment. A main challenge is to perceive any other road users, where multi-object tracking o…
Multi-Object Tracking with Interacting Vehicles and Road Map Information
Andreas Danzer, Fabian Gies, Klaus Dietmayer
In many applications, tracking of multiple objects is crucial for a perception of the current environment. Most of the present multi-object tracking algorithms assume that objects…
Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection
Di Feng, Lars Rosenbaum, Fabian Timm +1
We present a robust real-time LiDAR 3D object detector that leverages heteroscedastic aleatoric uncertainties to significantly improve its detection performance. A multi-loss funct…
Long-Term Occupancy Grid Prediction Using Recurrent Neural Networks
Marcel Schreiber, Stefan Hoermann, Klaus Dietmayer
We tackle the long-term prediction of scene evolution in a complex downtown scenario for automated driving based on Lidar grid fusion and recurrent neural networks (RNNs). A bird's…
Fast Trajectory Planning for Automated Vehicles using Gradient-based Nonlinear Model Predictive Control
Franz Gritschneder, Knut Graichen, Klaus Dietmayer
Motion trajectory planning is one crucial aspect for automated vehicles, as it governs the own future behavior in a dynamically changing environment. A good utilization of a vehicl…
Optimal Sensor Data Fusion Architecture for Object Detection in Adverse Weather Conditions
Andreas Pfeuffer, Klaus Dietmayer
A good and robust sensor data fusion in diverse weather conditions is a quite challenging task. There are several fusion architectures in the literature, e.g. the sensor data can b…
Deep Object Tracking on Dynamic Occupancy Grid Maps Using RNNs
Nico Engel, Stefan Hoermann, Philipp Henzler +1
The comprehensive representation and understanding of the driving environment is crucial to improve the safety and reliability of autonomous vehicles. In this paper, we present a n…
Disparity Sliding Window: Object Proposals From Disparity Images
Julian Müller, Andreas Fregin, Klaus Dietmayer
Sliding window approaches have been widely used for object recognition tasks in recent years. They guarantee an investigation of the entire input image for the object to be detecte…
Detecting Traffic Lights by Single Shot Detection
Julian Müller, Klaus Dietmayer
Recent improvements in object detection are driven by the success of convolutional neural networks (CNN). They are able to learn rich features outperforming hand-crafted features.…
Offline Object Extraction from Dynamic Occupancy Grid Map Sequences
Daniel Stumper, Fabian Gies, Stefan Hoermann +1
A dynamic occupancy grid map (DOGMa) allows a fast, robust, and complete environment representation for automated vehicles. Dynamic objects in a DOGMa, however, are commonly repres…
Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection
Di Feng, Lars Rosenbaum, Klaus Dietmayer
To assure that an autonomous car is driving safely on public roads, its object detection module should not only work correctly, but show its prediction confidence as well. Previous…
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…
Object Detection on Dynamic Occupancy Grid Maps Using Deep Learning and Automatic Label Generation
Stefan Hoermann, Philipp Henzler, Martin Bach +1
We tackle the problem of object detection and pose estimation in a shared space downtown environment. For perception multiple laser scanners with 360° coverage were fused in a dyna…