Zachäus / Müller / Meyer Advanced Microsystems for Automotive Applications 2017

Smart Systems Transforming the Automobile
1. Auflage 2018
ISBN: 978-3-319-66972-4
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 243 Seiten, eBook

Reihe: Lecture Notes in Mobility

This volume of the Lecture Notes in Mobility series contains papers written by speakers and poster presenters at the 21st International Forum on Advanced Microsystems for Automotive Applications (AMAA 2017) "Smart Systems Transforming the Automobile" that was held in Berlin, Germany in September 2017. The authors report about recent breakthroughs in electric and electronic components and systems, driver assistance and vehicle automation as well as safety and testing. Furthermore, legal aspects and impacts of connected and automated driving are covered. The target audience primarily comprises research experts and practitioners in industry and academia, but the book may also be beneficial for graduate students alike.

Zielgruppe

Research

Autoren/Hrsg.

Zachäus, Carolin

Müller, Beate

Meyer, Gereon

Inhaltsverzeichnis

1;Preface;6
2;Organisation Committee;8
2.1;Funding Authority;8
2.2;Supporting Organisations;8
2.3;Organisers;8
2.4;Steering Committee;8
2.5;Conference Chair;9
2.6;Conference Organizing Team;9
3;Contents;10
4;Smart Sensors;13
5;1 Smart Sensor Technology as the Foundation of the IoT: Optical Microsystems Enable Interactive Laser Projection;14
5.1;Abstract;14
5.2;1 MEMS Sensors—The Hidden Champions;15
5.2.1;1.1 Enablers for the Internet of Things;15
5.2.2;1.2 Challenges and Barriers for IoT Sensors;15
5.2.3;1.3 The Role of Smart Sensors in the IoT;16
5.3;2 Interactive Laser Projection;16
5.3.1;2.1 Making User Interfaces Simpler, More Flexible … and More Fun;17
5.3.2;2.2 Interactive Projection in Practice;18
5.3.3;2.3 A Window to the IoT;18
5.3.4;2.4 Interactive Projection for the Automotive Industry;20
5.3.4.1;2.4.1 Industry Teamwork;20
5.3.5;2.5 Wearables and Beyond;20
5.3.6;2.6 A Compact Module;21
5.4;3 Conclusion;22
6;2 Unit for Investigation of the Working Environment for Electronics in Harsh Environments, ESU;23
6.1;Abstract;23
6.2;1 Introduction;24
6.3;2 Monitoring Unit, ESU;24
6.3.1;2.1 ESU Main Data;29
6.3.1.1;2.1.1 Condensation Measurement;29
6.3.1.2;2.1.2 Relative Humidity Measurement;29
6.3.1.3;2.1.3 Vibration Measurement;30
6.3.1.4;2.1.4 Temperature Measurement;30
6.3.1.5;2.1.5 RTC;30
6.3.1.6;2.1.6 User Interface;31
6.3.2;2.2 Reliability of the ESU;31
6.3.3;2.3 EMC Test;31
6.4;3 Market Assessments;32
6.5;Acknowledgements;32
6.6;Reference;32
7;3 Automotive Synthetic Aperture Radar System Based on 24 GHz Series Sensors;33
7.1;Abstract;33
7.2;1 Introduction;34
7.2.1;1.1 Automotive Radar Sensors;35
7.2.2;1.2 Odometry;35
7.3;2 Related Work;35
7.4;3 SAR Algorithm;36
7.5;4 Performance Estimation;37
7.5.1;4.1 Azimuth Resolution;37
7.5.2;4.2 Range Resolution;38
7.5.3;4.3 Maximum Velocity;39
7.6;5 Evaluation Environment;39
7.7;6 Evaluation of Automotive Relevant SAR Properties;40
7.7.1;6.1 Incorrect Trajectory Measurement;41
7.7.2;6.2 Time-Based Sampling;42
7.8;7 Simulation and Measurement;43
7.8.1;7.1 Measurement;44
7.8.2;7.2 Simulation;45
7.9;8 Conclusion;45
7.10;Acknowledgements;46
7.11;References;46
8;4 SPAD-Based Flash Lidar with High Background Light Suppression;47
8.1;Abstract;47
8.2;1 Introduction;47
8.3;2 Sensor Principle;48
8.4;3 Technology and Measurements;49
8.5;4 Summary;52
8.6;References;53
9;Driver Assistance and Vehicle Automation;54
10;5 Enabling Robust Localization for Automated Guided Carts in Dynamic Environments;55
10.1;Abstract;55
10.2;1 Introduction;55
10.3;2 Related Work;57
10.4;3 The MCL/MU Approach;58
10.4.1;3.1 Map Update Control;59
10.4.2;3.2 Map Update and Map Update Fusion;60
10.5;4 Evaluation;62
10.6;5 Conclusion;64
10.7;References;65
11;6 Recognition of Lane Change Intentions Fusing Features of Driving Situation, Driver Behavior, and Vehicle Movement by Means of Neural Networks;66
11.1;Abstract;66
11.2;1 Introduction;66
11.3;2 Features Indicating Upcoming Lane Changes;68
11.4;3 Implementation and Sensor Data;69
11.5;4 Naturalistic Driving Study;70
11.6;5 Neural Network for Feature Classification;70
11.6.1;5.1 Artificial Neural Networks;71
11.6.2;5.2 Network Design;72
11.6.3;5.3 Network Parameterization;73
11.7;6 Experimental Results;73
11.8;7 Conclusion and Future Work;74
11.9;Acknowledgements;76
11.10;References;76
12;7 Applications of Road Edge Information for Advanced Driver Assistance Systems and Autonomous Driving;77
12.1;Abstract;77
12.2;1 Introduction;77
12.3;2 Road Edge Detection;78
12.3.1;2.1 Target Road Edge;78
12.3.2;2.2 Road Edge Detection Result;79
12.4;3 Application for Advanced Driver Assistance Systems;79
12.4.1;3.1 Euro NCAP;79
12.4.2;3.2 Integrated Lateral Assist System;80
12.4.2.1;3.2.1 Overview of Virtual Lane Guide;80
12.4.2.2;3.2.2 Target of VLG;83
12.4.2.3;3.2.3 Coordination of EPS and ESC;84
12.4.3;3.3 Experimental Result;85
12.5;4 Application for Autonomous Driving;87
12.5.1;4.1 Path Planning Algorithm;87
12.5.1.1;4.1.1 Path Planner;87
12.5.1.2;4.1.2 Path Selector;87
12.5.2;4.2 Simulation Result;90
12.5.3;4.3 Experimental Result;90
12.6;5 Conclusion;91
12.7;References;91
13;8 Robust and Numerically Efficient Estimation of Vehicle Mass and Road Grade;93
13.1;Abstract;93
13.2;1 Introduction;93
13.3;2 Methodology;95
13.3.1;2.1 Test Vehicle and Test Tracks;95
13.3.2;2.2 System Model;96
13.3.3;2.3 Recursive Least Squares (RLS) Algorithm;97
13.4;3 Sensitivity Analysis and Parameter Estimation;99
13.4.1;3.1 Sensitivity Analysis;99
13.4.2;3.2 Identification of Parameters and Validation of the Vehicle Model;100
13.5;4 Results;102
13.5.1;4.1 Validation with a Numerical Model;103
13.5.2;4.2 Results in Real-World Driving Conditions;103
13.6;5 Summary;104
13.7;References;106
14;9 Fast and Accurate Vanishing Point Estimation on Structured Roads;107
14.1;Abstract;107
14.2;1 Introduction;107
14.3;2 Vanishing Point;108
14.4;3 System Overview;108
14.4.1;3.1 Double-Edge Detection;108
14.4.2;3.2 Double-Edge Filtering;110
14.4.3;3.3 Double-Edge Grouping to Lane Markings;111
14.4.4;3.4 Lane Marking Filtering;112
14.4.5;3.5 Lane Marking Simplification;113
14.4.6;3.6 Vanishing Point Estimation;113
14.5;4 Results;114
14.6;5 Conclusion;116
14.7;References;116
15;10 Energy-Efficient Driving in Dynamic Environment: Globally Optimal MPC-like Motion Planning Framework;117
15.1;Abstract;117
15.2;1 Introduction;118
15.3;2 Problem Definition;119
15.3.1;2.1 Optimal Control Problem;119
15.3.2;2.2 Computational Complexity;120
15.4;3 Optimal Motion Planner;120
15.4.1;3.1 Dynamic Programming;121
15.4.2;3.2 Strategic Planning;121
15.4.3;3.3 Situation-Dependent Replanning;122
15.4.3.1;3.3.1 Prediction Horizon;125
15.4.3.2;3.3.2 Replanning Triggering;126
15.5;4 Simulation Results;126
15.6;5 Conclusion;127
15.7;Acknowledgements;127
15.8;References;127
16;Data, Clouds and Machine learning;129
17;11 Automated Data Generation for Training of Neural Networks by Recombining Previously Labeled Images;130
17.1;Abstract;130
17.2;1 Introduction;130
17.3;2 Related Work;132
17.3.1;2.1 Available Public Datasets;132
17.3.2;2.2 Image Manipulation and Recombination;133
17.4;3 Semi-artificial Dataset Creation;133
17.5;4 Evaluation;135
17.6;5 Summary and Outlook;137
17.7;References;139
18;12 Secure Wireless Automotive Software Updates Using Blockchains: A Proof of Concept;141
18.1;Abstract;141
18.2;1 Introduction;142
18.3;2 Background;143
18.3.1;2.1 Wireless Automotive Software Updates;143
18.3.2;2.2 Blockchains;144
18.4;3 Architecture Enabling Wireless Software Updates;145
18.4.1;3.1 Blockchain-Based Architecture Securing Wireless Software Updates;146
18.4.2;3.2 Employing Our Architecture to Distribute New SW;147
18.5;4 Proof of Concept;148
18.6;5 Evaluation;150
18.6.1;5.1 Overhead Due to the Use of Blockchains;150
18.6.2;5.2 Latency Comparison: Local SW Update Versus SW Distribution Using BC;150
18.6.3;5.3 Comparison of BC- and Certificate-Based Approaches;151
18.7;6 Conclusion;152
18.8;References;152
19;13 DEIS: Dependability Engineering Innovation for Industrial CPS;154
19.1;Abstract;154
19.2;1 Introduction;155
19.3;2 The Digital Dependability Identity (DDI) Concept;156
19.4;3 The Four Industrial Use Cases in DEIS Project;158
19.4.1;3.1 Automotive: Development of a Stand-Alone System for Intelligent Physiological Parameter Monitoring;158
19.4.2;3.2 Automotive: Enhancement of an Advanced Driver Simulator for Evaluation of Automated Driving Functions;160
19.4.3;3.3 Railway: Enabling Plug-and-Play Scenarios for Heterogeneous Railway Systems;161
19.4.4;3.4 Health Care: Enhancement of Clinical Decision App for Oncology Professional;162
19.5;4 Opportunities for DDI Applications;164
19.6;5 Conclusions;165
19.7;References;166
20;Safety and Testing;167
21;14 Smart Features Integrated for Prognostics Health Management Assure the Functional Safety of the Electronics Systems at the High Level Required in Fully Automated Vehicles;168
21.1;Abstract;168
21.2;1 Introduction;168
21.3;2 Prognostics Health Management;170
21.4;3 PHM Strategy;172
21.5;4 PHM Indicators and Parameters for the RUL Estimation;175
21.6;Acknowledgements;178
21.7;References;178
22;15 Challenges for the Validation and Testing of Automated Driving Functions;180
22.1;Abstract;180
22.2;1 Introduction;180
22.3;2 Challenges for Validation and Testing;182
22.3.1;2.1 Complexity of Automated Driving Functions;182
22.3.2;2.2 Variation of Scenarios and Parameters;183
22.3.3;2.3 Scenario Selection and Test Generation;183
22.4;3 Current Methodologies/Technology Overview;184
22.5;4 Validation—Global Approach;185
22.6;5 Supporting Tools in the Validation Task;185
22.7;6 Standardization;187
22.8;7 Conclusion;188
22.9;Acknowledgements;188
22.10;References;188
23;16 Automated Assessment and Evaluation of Digital Test Drives;189
23.1;Abstract;189
23.2;1 Introduction;190
23.3;2 State of the Art in Automotive Testing;191
23.3.1;2.1 Test Processes and Methodologies;191
23.3.2;2.2 Digital Test Drive;193
23.4;3 Requirements and Constraints for Automated Assessment of Digital Test Drives;193
23.5;4 Automated Assessment Concept;194
23.5.1;4.1 HiL System;195
23.5.2;4.2 Assessment Domain;196
23.5.3;4.3 Visualization and Data Analytics Domain;196
23.6;5 Application on Exemplary Driver-Assistance System;197
23.7;6 Conclusion and Outlook;198
23.8;References;198
24;17 HiFi Visual Target—Methods for Measuring Optical and Geometrical Characteristics of Soft Car Targets for ADAS and AD;200
24.1;Abstract;200
24.2;1 Background;200
24.3;2 Soft Car Targets;201
24.4;3 Project Goals;202
24.5;4 Initial Measurements and Results;203
24.5.1;4.1 Measurement Setup;203
24.5.1.1;4.1.1 Optical Measurement Setup;203
24.5.1.2;4.1.2 Geometry Measurement Setup;204
24.5.2;4.2 Preliminary Results;205
24.5.2.1;4.2.1 Optical Measurement Results;205
24.5.2.2;4.2.2 Geometry Variation Due to Assembly;206
24.6;5 Conclusions and Future Work;207
24.7;Acknowledgments;207
24.8;References;207
25;Legal Framework and Impact;209
26;18 Assessing the Impact of Connected and Automated Vehicles. A Freeway Scenario;210
26.1;Abstract;210
26.2;1 Introduction;211
26.3;2 Review of the Literature;211
26.4;3 Case-Study Simulation;212
26.4.1;3.1 The Traffic Model of Antwerp’s Ring Road;213
26.4.2;3.2 Human and CACC Drivers;214
26.4.3;3.3 Assessment Metrics;216
26.4.4;3.4 Simulation Scenarios;217
26.5;4 Results;217
26.5.1;4.1 Energy Consumption;219
26.6;5 Conclusions;220
27;19 Germany’s New Road Traffic Law—Legal Risks and Ramifications for the Design of Human-Machine Interaction in Automated Vehicles;223
27.1;Abstract;223
27.2;1 Introduction;223
27.3;2 The Amendments to the Federal Road Traffic Act;224
27.3.1;2.1 Levels of Automation Addressed;224
27.3.2;2.2 Definition of “Driver”;225
27.3.3;2.3 Interaction Between the Automation System and the Driver;225
27.4;3 The Statutory Amendments from the Driver’s Perspective;226
27.4.1;3.1 Brief Overview of the Statutory Liability Regime for Drivers;226
27.4.2;3.2 Ramifications of the Obligations Imposed on Automated System Users;226
27.4.2.1;3.2.1 Obligation to Use the Automation System Properly;227
27.4.2.2;3.2.2 Sharing of the Driving Task Between the Driver and the Automation System;227
27.5;4 Liability Issues from the Manufacturer’s Perspective;228
27.5.1;4.1 Brief Overview of the Statutory Liability Regime for Manufacturers;228
27.5.2;4.2 Product Liability Issues in Relation to Automated Vehicles;229
27.5.2.1;4.2.1 Constructional Deficiencies;229
27.5.2.2;4.2.2 Instructional Errors;230
27.6;5 Summary;231
27.7;References;232
28;20 Losing a Private Sphere? A Glance on the User Perspective on Privacy in Connected Cars;233
28.1;Abstract;233
28.2;1 Introduction;233
28.3;2 Literature Review;234
28.3.1;2.1 Methodology;234
28.3.2;2.2 Relevant Privacy Factors for the Adoption of Connected Services;235
28.4;3 User Study;238
28.4.1;3.1 Results;238
28.4.2;3.2 Discussion;240
28.5;4 Conclusion and Practical Implications;241