Christoph Stiller

research

∙ 05/03/2023

HD Map Generation from Noisy Multi-Route Vehicle Fleet Data on Highways with Expectation Maximization

High Definition (HD) maps are necessary for many applications of automat...

0 Fabian Immel, et al. ∙

research

∙ 02/12/2023

Self-supervised Pseudo-colorizing of Masked Cells

Self-supervised learning, which is strikingly referred to as the dark ma...

0 Royden Wagner, et al. ∙

research

∙ 10/17/2022

Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving

Trajectory data analysis is an essential component for highly automated ...

0 Kevin Rösch, et al. ∙

research

∙ 07/28/2022

Robust Self-Tuning Data Association for Geo-Referencing Using Lane Markings

Localization in aerial imagery-based maps offers many advantages, such a...

0 Miguel Angel Munoz-Banon, et al. ∙

research

∙ 04/19/2022

Sensor Data Fusion in Top-View Grid Maps using Evidential Reasoning with Advanced Conflict Resolution

We present a new method to combine evidential top-view grid maps estimat...

0 Sven Richter, et al. ∙

research

∙ 04/16/2022

Mapping LiDAR and Camera Measurements in a Dual Top-View Grid Representation Tailored for Automated Vehicles

We present a generic evidential grid mapping pipeline designed for imagi...

0 Sven Richter, et al. ∙

research

∙ 03/02/2022

Fast and Robust Ground Surface Estimation from LIDAR Measurements using Uniform B-Splines

We propose a fast and robust method to estimate the ground surface from ...

12 Sascha Wirges, et al. ∙

research

∙ 03/02/2022

Improving Lidar-Based Semantic Segmentation of Top-View Grid Maps by Learning Features in Complementary Representations

In this paper we introduce a novel way to predict semantic information f...

1 Frank Bieder, et al. ∙

research

∙ 02/28/2022

Large-Scale 3D Semantic Reconstruction for Automated Driving Vehicles with Adaptive Truncated Signed Distance Function

The Large-scale 3D reconstruction, texturing and semantic mapping are no...

0 Haohao Hu, et al. ∙

research

∙ 02/28/2022

TEScalib: Targetless Extrinsic Self-Calibration of LiDAR and Stereo Camera for Automated Driving Vehicles with Uncertainty Analysis

In this paper, we present TEScalib, a novel extrinsic self-calibration a...

0 Haohao Hu, et al. ∙

research

∙ 11/17/2021

DA-LMR: A Robust Lane Markings Representation for Data Association Methods

While complete localization approaches are widely studied in the literat...

0 Miguel Angel Munoz-Banon, et al. ∙

research

∙ 10/14/2021

Modeling dynamic target deformation in camera calibration

Most approaches to camera calibration rely on calibration targets of wel...

0 Annika Hagemann, et al. ∙

research

∙ 08/18/2021

Deployment of Deep Neural Networks for Object Detection on Edge AI Devices with Runtime Optimization

Deep neural networks have proven increasingly important for automotive s...

0 Lukas Stäcker, et al. ∙

research

∙ 07/28/2021

Inferring bias and uncertainty in camera calibration

Accurate camera calibration is a precondition for many computer vision a...

0 Annika Hagemann, et al. ∙

research

∙ 07/15/2021

Minimizing Safety Interference for Safe and Comfortable Automated Driving with Distributional Reinforcement Learning

Despite recent advances in reinforcement learning (RL), its application ...

0 Danial Kamran, et al. ∙

research

∙ 07/01/2021

MASS: Multi-Attentional Semantic Segmentation of LiDAR Data for Dense Top-View Understanding

At the heart of all automated driving systems is the ability to sense th...

1 Kunyu Peng, et al. ∙

research

∙ 05/14/2021

Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems

Full-stack autonomous driving perception modules usually consist of data...

0 Hannes Reichert, et al. ∙

research

∙ 05/10/2021

PillarSegNet: Pillar-based Semantic Grid Map Estimation using Sparse LiDAR Data

Semantic understanding of the surrounding environment is essential for a...

0 Juncong Fei, et al. ∙

research

∙ 03/26/2021

YOLinO: Generic Single Shot Polyline Detection in Real Time

The detection of polylines in images is usually either bound to branchle...

0 Annika Meyer, et al. ∙

research

∙ 03/05/2021

An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving

Systems and functions that rely on machine learning (ML) are the basis o...

0 Florian Heidecker, et al. ∙

research

∙ 12/13/2020

Decision-Time Postponing Motion Planning for Combinatorial Uncertain Maneuvering

Motion planning involves decision making among combinatorial maneuver va...

0 Omer Sahin Tas, et al. ∙

research

∙ 09/25/2020

SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation

3D pedestrian detection is a challenging task in automated driving becau...

5 Juncong Fei, et al. ∙

research

∙ 08/26/2020

RNN-based Pedestrian Crossing Prediction using Activity and Pose-related Features

Pedestrian crossing prediction is a crucial task for autonomous driving....

0 Javier Lorenzo, et al. ∙

research

∙ 05/13/2020

Exploiting Multi-Layer Grid Maps for Surround-View Semantic Segmentation of Sparse LiDAR Data

In this paper, we consider the transformation of laser range measurement...

5 Frank Bieder, et al. ∙

research

∙ 04/09/2020

Risk-Aware High-level Decisions for Automated Driving at Occluded Intersections with Reinforcement Learning

Reinforcement learning is nowadays a popular framework for solving diffe...

0 Danial Kamran, et al. ∙

research

∙ 03/02/2020

Learned Enrichment of Top-View Grid Maps Improves Object Detection

We propose an object detector for top-view grid maps which is additional...

0 Sascha Wirges, et al. ∙

research

∙ 02/04/2020

Tackling Existence Probabilities of Objects with Motion Planning for Automated Urban Driving

Motion planners take uncertain information about the environment as an i...

0 Omer Sahin Tas, et al. ∙

research

∙ 02/03/2020

Single-Stage Object Detection from Top-View Grid Maps on Custom Sensor Setups

We present our approach to unsupervised domain adaptation for single-sta...

49 Sascha Wirges, et al. ∙

research

∙ 09/30/2019

INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps

Behavior-related research areas such as motion prediction/planning, repr...

19 Wei Zhan, et al. ∙

research

∙ 06/06/2019

Anytime Lane-Level Intersection Estimation Based on Trajectories

Estimating and understanding the current scene is an inevitable capabili...

0 Annika Meyer, et al. ∙

research

∙ 04/17/2019

Self-Supervised Flow Estimation using Geometric Regularization with Applications to Camera Image and Grid Map Sequences

We present a self-supervised approach to estimate flow in camera image a...

0 Sascha Wirges, et al. ∙

research

∙ 03/25/2019

Accurate Global Trajectory Alignment using Poles and Road Markings

Currently, digital maps are indispensable for automated driving. However...

2 Haohao Hu, et al. ∙

research

∙ 01/31/2019

Capturing Object Detection Uncertainty in Multi-Layer Grid Maps

We propose a deep convolutional object detector for automated driving ap...

0 Sascha Wirges, et al. ∙

research

∙ 10/30/2018

Limited Visibility and Uncertainty Aware Motion Planning for Automated Driving

Adverse weather conditions and occlusions in urban environments result i...

0 Omer Sahin Tas, et al. ∙

research

∙ 05/14/2018

Generating Comfortable, Safe and Comprehensible Trajectories for Automated Vehicles in Mixed Traffic

While motion planning approaches for automated driving often focus on sa...

0 Maximilian Naumann, et al. ∙

research

∙ 05/02/2018

Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks

A detailed environment perception is a crucial component of automated ve...

0 Sascha Wirges, et al. ∙

research

∙ 01/16/2018

Evidential Occupancy Grid Map Augmentation using Deep Learning

A detailed environment representation is a crucial component of automate...

0 Sascha Wirges, et al. ∙

research

∙ 09/15/2017

Cooperative Motion Planning for Non-Holonomic Agents with Value Iteration Networks

Cooperative motion planning is still a challenging task for robots. Rece...

0 Eike Rehder, et al. ∙

research

∙ 08/23/2017

Towards Cooperative Motion Planning for Automated Vehicles in Mixed Traffic

While motion planning techniques for automated vehicles in a reactive an...

0 Maximilian Naumann, et al. ∙

research

∙ 07/11/2017

RegNet: Multimodal Sensor Registration Using Deep Neural Networks

In this paper, we present RegNet, the first deep convolutional neural ne...

0 Nick Schneider, et al. ∙

research

∙ 06/19/2017

Pedestrian Prediction by Planning using Deep Neural Networks

Accurate traffic participant prediction is the prerequisite for collisio...

0 Eike Rehder, et al. ∙

research

∙ 08/02/2016

Semantically Guided Depth Upsampling

We present a novel method for accurate and efficient up- sampling of spa...

0 Nick Schneider, et al. ∙

Christoph Stiller

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