Modelling and Simulation for Autonomous Systems - Info and Reading Options

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Intro -- Preface -- MESAS 2021 Organizer -- Organization -- Contents -- M&amp -- S of Intelligent Systems - R&amp -- D and Application -- UAV Based Vehicle Detection on Real and Synthetic Image Pairs: Performance Differences and Influence Analysis of Context and Simulation Parameters -- 1 Introduction -- 2 Object of Research -- 3 Real and Synthetic Datasets -- 3.1 R-UAV/S-UAV: Real Flight Setup and Synthetic Re-modeling -- 3.2 Training Datasets -- 4 Variations of Sensor and Simulation Parameters -- 5 Evaluation and Results -- 5.1 Experimental Setup -- 5.2 Comparison of Performance Differences for Real and Synthetic Training Data -- 5.3 Dependence of Detection Performance on Real Flight Parameter Influences -- 5.4 Dependence of Detection Performance on Sensor and Simulation Parameters -- 6 Conclusion and Future Work -- References -- Obstacle Detection in Real and Synthetic Harbour Scenarios -- 1 Introduction -- 2 LiDAR Point Cloud Acquisition -- 2.1 Synthetic Scenario -- 2.2 Experimental Marine Environment -- 3 Clustering Methods -- 3.1 DBSCAN Approach -- 3.2 Euclidian Approach -- 4 2D Bounding Box -- 5 Object Detection -- 5.1 Trained CNN -- 6 Conclusions -- References -- Fault Detection and Identification on Pneumatic Production Machine -- 1 Introduction -- 2 Signal-Based Condition Indicators -- 3 Data Processing Workflow -- 3.1 Data Acquisition and Pre-processing -- 3.2 Condition Indicator Extraction -- 3.3 Condition Indicator Selection -- 3.4 Verification of the Results -- 4 Testbench Description -- 4.1 Sensors -- 4.2 Reference Settings -- 5 Data Acquisition -- 5.1 Data Acquisition Firmware -- 6 Experimental -- 7 Results -- 8 Conclusion -- References -- Ground Visibility Analyses, Algorithms and Performance -- 1 Introduction -- 2 Problem Statement -- 3 Literature Analyses -- 4 Approach to the Solution

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