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Improving neural networks for automated driving using corner cases and sensorfusionsion / submitted by Kamil Peter Kowol, M.Sc. Wuppertal, [2023]
Content
Acknowledgments
Foreword
Contents
Introduction
State of the Art and Theoretical Foundation
Feedforward Neural Networks
Learning Process
Convolutional Neural Networks
Image Segmentation
Object Detection
Evaluation Metrics
Model Development Process
Driving Simulator
Introduction
Software
CARLA
Additional Software
Hardware Components
Code Adaptation and Acceleration
Error Occurrence and Correction
Corner Cases in Autonomous Driving
Levels of Driving Automation
Corner Case
YOdar: Uncertainty-based Sensor Fusion for Vehicle Detection with Camera and Radar Sensors kowol21yodar
Characteristics
Object Detection via Radar
Object Detection via Sensor Fusion
Fusion Metrics and Methods
Numerical Experiments
Conclusion
Datasets for Tracking and Retrieval of Out-of-Distribution Objects maag2022oodtracking
Generation of the CARLA-WildLife Dataset
Method
Datasets for Anomaly Detection bogdoll2023AnomalySurvey
Applications for Driving Simulator
A-Eye: Driving with the Eyes of AI for Corner Case Generation kowol22simulator
Experimental Setup
Retrieval of Corner Cases
Evaluation and Results
Conclusion
survAIval: Survival Analysis with the Eyes of AI Kowol2023survAIval
Survival Analysis
Experimental Design
Results
Conclusion
A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving Roesch2022CCTrajectory
Conclusion & Outlook
List of Figures
List of Tables
List of Notations and Abbreviations
Bibliography