Search Results for author:
Found 16 papers, 7 papers with code
Automated Automotive Radar Calibration With Intelligent Vehicles
We, therefore, present an approach for automated and geo-referenced extrinsic calibration of automotive radar sensors that is based on a novel hypothesis filtering scheme.
RT-K-Net: Revisiting K-Net for Real-Time Panoptic Segmentation
Our resulting RT-K-Net sets a new state-of-the-art performance for real-time panoptic segmentation methods on the Cityscapes dataset and shows promising results on the challenging Mapillary Vistas dataset.
Automated Static Camera Calibration with Intelligent Vehicles
Our method does not require any human interaction with the information recorded by both the infrastructure and the vehicle.
Integration of Reinforcement Learning Based Behavior Planning With Sampling Based Motion Planning for Automated Driving
In this work, we propose a method to employ a trained deep reinforcement learning policy for dedicated high-level behavior planning.
Automatic Intersection Management in Mixed Traffic Using Reinforcement Learning and Graph Neural Networks
The present work proposes to leverage reinforcement learning and a graph-based scene representation for cooperative multi-agent planning.
Extrinsic Camera Calibration with Semantic Segmentation
Our approach relies on a coarse initial measurement of the camera pose and builds on lidar sensors mounted on a vehicle with high-precision localization to capture a point cloud of the camera environment.
An Enhanced Graph Representation for Machine Learning Based Automatic Intersection Management
In this work, we build upon a previously presented graph-based scene representation and graph neural network to approach the problem using reinforcement learning.
MGNet: Monocular Geometric Scene Understanding for Autonomous Driving
We define monocular geometric scene understanding as the combination of two known tasks: Panoptic segmentation and self-supervised monocular depth estimation.
Self-Assessment for Single-Object Tracking in Clutter Using Subjective Logic
Therefore, this work presents a novel method for self-assessment of single-object tracking in clutter based on Kalman filtering and subjective logic.
Cooperative Behavior Planning for Automated Driving using Graph Neural Networks
Learning-based behavior planning poses several challenges, demanding for a suited input and output representation as well as large amounts of ground-truth data.
Globally Optimal Multi-Scale Monocular Hand-Eye Calibration Using Dual Quaternions
In this work, we present an approach for monocular hand-eye calibration from per-sensor ego-motion based on dual quaternions.
Anomaly Detection in Radar Data Using PointNets
On the one hand, radar offers a direct measurement of the radial velocity of targets in the environment.
Online Extrinsic Calibration based on Per-Sensor Ego-Motion Using Dual Quaternions
Further, our online calibration approach is tested on the KITTI odometry dataset, which provides data of a lidar and two stereo camera systems mounted on a vehicle.
Robotics
DeepCLR: Correspondence-Less Architecture for Deep End-to-End Point Cloud Registration
This work addresses the problem of point cloud registration using deep neural networks.
Kalman Filter Meets Subjective Logic: A Self-Assessing Kalman Filter Using Subjective Logic
Thus, by embedding classical Kalman filtering into subjective logic, our method additionally features an explicit measure for statistical uncertainty in the self-assessment.
LACI: Low-effort Automatic Calibration of Infrastructure Sensors
While classical approaches are sensor-specific and often need calibration targets as well as a widely overlapping field of view (FOV), within this work, a cooperative intelligent vehicle is used as callibration target.
