Advanced Microsystems for Automotive Applications 2017
Smart Systems Transforming the Automobile
1st Edition
Published on 29. August 2017
XI, 247 pages
978-3-319-66972-4 (ISBN)
Description
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.
More details
Series
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
XI, 247 p. 116 illus., 104 illus. in color.
File size
8,93 MB
ISBN-13
978-3-319-66972-4 (9783319669724)
DOI
10.1007/978-3-319-66972-4
Schweitzer Classification
Other editions
Additional editions
Carolin Zachäus | Beate Müller | Gereon Meyer
Advanced Microsystems for Automotive Applications 2017
Smart Systems Transforming the Automobile
Book
08/2017
Springer
€160.49
Shipment within 10-15 days
Persons
Content
1 - Preface [Seite 6]
2 - Organisation Committee [Seite 8]
2.1 - Funding Authority [Seite 8]
2.2 - Supporting Organisations [Seite 8]
2.3 - Organisers [Seite 8]
2.4 - Steering Committee [Seite 8]
2.5 - Conference Chair [Seite 9]
2.6 - Conference Organizing Team [Seite 9]
3 - Contents [Seite 10]
4 - Smart Sensors [Seite 13]
5 - 1 Smart Sensor Technology as the Foundation of the IoT: Optical Microsystems Enable Interactive Laser Projection [Seite 14]
5.1 - Abstract [Seite 14]
5.2 - 1 MEMS Sensors-The Hidden Champions [Seite 15]
5.2.1 - 1.1 Enablers for the Internet of Things [Seite 15]
5.2.2 - 1.2 Challenges and Barriers for IoT Sensors [Seite 15]
5.2.3 - 1.3 The Role of Smart Sensors in the IoT [Seite 16]
5.3 - 2 Interactive Laser Projection [Seite 16]
5.3.1 - 2.1 Making User Interfaces Simpler, More Flexible . and More Fun [Seite 17]
5.3.2 - 2.2 Interactive Projection in Practice [Seite 18]
5.3.3 - 2.3 A Window to the IoT [Seite 18]
5.3.4 - 2.4 Interactive Projection for the Automotive Industry [Seite 20]
5.3.4.1 - 2.4.1 Industry Teamwork [Seite 20]
5.3.5 - 2.5 Wearables and Beyond [Seite 20]
5.3.6 - 2.6 A Compact Module [Seite 21]
5.4 - 3 Conclusion [Seite 22]
6 - 2 Unit for Investigation of the Working Environment for Electronics in Harsh Environments, ESU [Seite 23]
6.1 - Abstract [Seite 23]
6.2 - 1 Introduction [Seite 24]
6.3 - 2 Monitoring Unit, ESU [Seite 24]
6.3.1 - 2.1 ESU Main Data [Seite 29]
6.3.1.1 - 2.1.1 Condensation Measurement [Seite 29]
6.3.1.2 - 2.1.2 Relative Humidity Measurement [Seite 29]
6.3.1.3 - 2.1.3 Vibration Measurement [Seite 30]
6.3.1.4 - 2.1.4 Temperature Measurement [Seite 30]
6.3.1.5 - 2.1.5 RTC [Seite 30]
6.3.1.6 - 2.1.6 User Interface [Seite 31]
6.3.2 - 2.2 Reliability of the ESU [Seite 31]
6.3.3 - 2.3 EMC Test [Seite 31]
6.4 - 3 Market Assessments [Seite 32]
6.5 - Acknowledgements [Seite 32]
6.6 - Reference [Seite 32]
7 - 3 Automotive Synthetic Aperture Radar System Based on 24 GHz Series Sensors [Seite 33]
7.1 - Abstract [Seite 33]
7.2 - 1 Introduction [Seite 34]
7.2.1 - 1.1 Automotive Radar Sensors [Seite 35]
7.2.2 - 1.2 Odometry [Seite 35]
7.3 - 2 Related Work [Seite 35]
7.4 - 3 SAR Algorithm [Seite 36]
7.5 - 4 Performance Estimation [Seite 37]
7.5.1 - 4.1 Azimuth Resolution [Seite 37]
7.5.2 - 4.2 Range Resolution [Seite 38]
7.5.3 - 4.3 Maximum Velocity [Seite 39]
7.6 - 5 Evaluation Environment [Seite 39]
7.7 - 6 Evaluation of Automotive Relevant SAR Properties [Seite 40]
7.7.1 - 6.1 Incorrect Trajectory Measurement [Seite 41]
7.7.2 - 6.2 Time-Based Sampling [Seite 42]
7.8 - 7 Simulation and Measurement [Seite 43]
7.8.1 - 7.1 Measurement [Seite 44]
7.8.2 - 7.2 Simulation [Seite 45]
7.9 - 8 Conclusion [Seite 45]
7.10 - Acknowledgements [Seite 46]
7.11 - References [Seite 46]
8 - 4 SPAD-Based Flash Lidar with High Background Light Suppression [Seite 47]
8.1 - Abstract [Seite 47]
8.2 - 1 Introduction [Seite 47]
8.3 - 2 Sensor Principle [Seite 48]
8.4 - 3 Technology and Measurements [Seite 49]
8.5 - 4 Summary [Seite 52]
8.6 - References [Seite 53]
9 - Driver Assistance and Vehicle Automation [Seite 54]
10 - 5 Enabling Robust Localization for Automated Guided Carts in Dynamic Environments [Seite 55]
10.1 - Abstract [Seite 55]
10.2 - 1 Introduction [Seite 55]
10.3 - 2 Related Work [Seite 57]
10.4 - 3 The MCL/MU Approach [Seite 58]
10.4.1 - 3.1 Map Update Control [Seite 59]
10.4.2 - 3.2 Map Update and Map Update Fusion [Seite 60]
10.5 - 4 Evaluation [Seite 62]
10.6 - 5 Conclusion [Seite 64]
10.7 - References [Seite 65]
11 - 6 Recognition of Lane Change Intentions Fusing Features of Driving Situation, Driver Behavior, and Vehicle Movement by Means of Neural Networks [Seite 66]
11.1 - Abstract [Seite 66]
11.2 - 1 Introduction [Seite 66]
11.3 - 2 Features Indicating Upcoming Lane Changes [Seite 68]
11.4 - 3 Implementation and Sensor Data [Seite 69]
11.5 - 4 Naturalistic Driving Study [Seite 70]
11.6 - 5 Neural Network for Feature Classification [Seite 70]
11.6.1 - 5.1 Artificial Neural Networks [Seite 71]
11.6.2 - 5.2 Network Design [Seite 72]
11.6.3 - 5.3 Network Parameterization [Seite 73]
11.7 - 6 Experimental Results [Seite 73]
11.8 - 7 Conclusion and Future Work [Seite 74]
11.9 - Acknowledgements [Seite 76]
11.10 - References [Seite 76]
12 - 7 Applications of Road Edge Information for Advanced Driver Assistance Systems and Autonomous Driving [Seite 77]
12.1 - Abstract [Seite 77]
12.2 - 1 Introduction [Seite 77]
12.3 - 2 Road Edge Detection [Seite 78]
12.3.1 - 2.1 Target Road Edge [Seite 78]
12.3.2 - 2.2 Road Edge Detection Result [Seite 79]
12.4 - 3 Application for Advanced Driver Assistance Systems [Seite 79]
12.4.1 - 3.1 Euro NCAP [Seite 79]
12.4.2 - 3.2 Integrated Lateral Assist System [Seite 80]
12.4.2.1 - 3.2.1 Overview of Virtual Lane Guide [Seite 80]
12.4.2.2 - 3.2.2 Target of VLG [Seite 83]
12.4.2.3 - 3.2.3 Coordination of EPS and ESC [Seite 84]
12.4.3 - 3.3 Experimental Result [Seite 85]
12.5 - 4 Application for Autonomous Driving [Seite 87]
12.5.1 - 4.1 Path Planning Algorithm [Seite 87]
12.5.1.1 - 4.1.1 Path Planner [Seite 87]
12.5.1.2 - 4.1.2 Path Selector [Seite 87]
12.5.2 - 4.2 Simulation Result [Seite 90]
12.5.3 - 4.3 Experimental Result [Seite 90]
12.6 - 5 Conclusion [Seite 91]
12.7 - References [Seite 91]
13 - 8 Robust and Numerically Efficient Estimation of Vehicle Mass and Road Grade [Seite 93]
13.1 - Abstract [Seite 93]
13.2 - 1 Introduction [Seite 93]
13.3 - 2 Methodology [Seite 95]
13.3.1 - 2.1 Test Vehicle and Test Tracks [Seite 95]
13.3.2 - 2.2 System Model [Seite 96]
13.3.3 - 2.3 Recursive Least Squares (RLS) Algorithm [Seite 97]
13.4 - 3 Sensitivity Analysis and Parameter Estimation [Seite 99]
13.4.1 - 3.1 Sensitivity Analysis [Seite 99]
13.4.2 - 3.2 Identification of Parameters and Validation of the Vehicle Model [Seite 100]
13.5 - 4 Results [Seite 102]
13.5.1 - 4.1 Validation with a Numerical Model [Seite 103]
13.5.2 - 4.2 Results in Real-World Driving Conditions [Seite 103]
13.6 - 5 Summary [Seite 104]
13.7 - References [Seite 106]
14 - 9 Fast and Accurate Vanishing Point Estimation on Structured Roads [Seite 107]
14.1 - Abstract [Seite 107]
14.2 - 1 Introduction [Seite 107]
14.3 - 2 Vanishing Point [Seite 108]
14.4 - 3 System Overview [Seite 108]
14.4.1 - 3.1 Double-Edge Detection [Seite 108]
14.4.2 - 3.2 Double-Edge Filtering [Seite 110]
14.4.3 - 3.3 Double-Edge Grouping to Lane Markings [Seite 111]
14.4.4 - 3.4 Lane Marking Filtering [Seite 112]
14.4.5 - 3.5 Lane Marking Simplification [Seite 113]
14.4.6 - 3.6 Vanishing Point Estimation [Seite 113]
14.5 - 4 Results [Seite 114]
14.6 - 5 Conclusion [Seite 116]
14.7 - References [Seite 116]
15 - 10 Energy-Efficient Driving in Dynamic Environment: Globally Optimal MPC-like Motion Planning Framework [Seite 117]
15.1 - Abstract [Seite 117]
15.2 - 1 Introduction [Seite 118]
15.3 - 2 Problem Definition [Seite 119]
15.3.1 - 2.1 Optimal Control Problem [Seite 119]
15.3.2 - 2.2 Computational Complexity [Seite 120]
15.4 - 3 Optimal Motion Planner [Seite 120]
15.4.1 - 3.1 Dynamic Programming [Seite 121]
15.4.2 - 3.2 Strategic Planning [Seite 121]
15.4.3 - 3.3 Situation-Dependent Replanning [Seite 122]
15.4.3.1 - 3.3.1 Prediction Horizon [Seite 125]
15.4.3.2 - 3.3.2 Replanning Triggering [Seite 126]
15.5 - 4 Simulation Results [Seite 126]
15.6 - 5 Conclusion [Seite 127]
15.7 - Acknowledgements [Seite 127]
15.8 - References [Seite 127]
16 - Data, Clouds and Machine learning [Seite 129]
17 - 11 Automated Data Generation for Training of Neural Networks by Recombining Previously Labeled Images [Seite 130]
17.1 - Abstract [Seite 130]
17.2 - 1 Introduction [Seite 130]
17.3 - 2 Related Work [Seite 132]
17.3.1 - 2.1 Available Public Datasets [Seite 132]
17.3.2 - 2.2 Image Manipulation and Recombination [Seite 133]
17.4 - 3 Semi-artificial Dataset Creation [Seite 133]
17.5 - 4 Evaluation [Seite 135]
17.6 - 5 Summary and Outlook [Seite 137]
17.7 - References [Seite 139]
18 - 12 Secure Wireless Automotive Software Updates Using Blockchains: A Proof of Concept [Seite 141]
18.1 - Abstract [Seite 141]
18.2 - 1 Introduction [Seite 142]
18.3 - 2 Background [Seite 143]
18.3.1 - 2.1 Wireless Automotive Software Updates [Seite 143]
18.3.2 - 2.2 Blockchains [Seite 144]
18.4 - 3 Architecture Enabling Wireless Software Updates [Seite 145]
18.4.1 - 3.1 Blockchain-Based Architecture Securing Wireless Software Updates [Seite 146]
18.4.2 - 3.2 Employing Our Architecture to Distribute New SW [Seite 147]
18.5 - 4 Proof of Concept [Seite 148]
18.6 - 5 Evaluation [Seite 150]
18.6.1 - 5.1 Overhead Due to the Use of Blockchains [Seite 150]
18.6.2 - 5.2 Latency Comparison: Local SW Update Versus SW Distribution Using BC [Seite 150]
18.6.3 - 5.3 Comparison of BC- and Certificate-Based Approaches [Seite 151]
18.7 - 6 Conclusion [Seite 152]
18.8 - References [Seite 152]
19 - 13 DEIS: Dependability Engineering Innovation for Industrial CPS [Seite 154]
19.1 - Abstract [Seite 154]
19.2 - 1 Introduction [Seite 155]
19.3 - 2 The Digital Dependability Identity (DDI) Concept [Seite 156]
19.4 - 3 The Four Industrial Use Cases in DEIS Project [Seite 158]
19.4.1 - 3.1 Automotive: Development of a Stand-Alone System for Intelligent Physiological Parameter Monitoring [Seite 158]
19.4.2 - 3.2 Automotive: Enhancement of an Advanced Driver Simulator for Evaluation of Automated Driving Functions [Seite 160]
19.4.3 - 3.3 Railway: Enabling Plug-and-Play Scenarios for Heterogeneous Railway Systems [Seite 161]
19.4.4 - 3.4 Health Care: Enhancement of Clinical Decision App for Oncology Professional [Seite 162]
19.5 - 4 Opportunities for DDI Applications [Seite 164]
19.6 - 5 Conclusions [Seite 165]
19.7 - References [Seite 166]
20 - Safety and Testing [Seite 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 [Seite 168]
21.1 - Abstract [Seite 168]
21.2 - 1 Introduction [Seite 168]
21.3 - 2 Prognostics Health Management [Seite 170]
21.4 - 3 PHM Strategy [Seite 172]
21.5 - 4 PHM Indicators and Parameters for the RUL Estimation [Seite 175]
21.6 - Acknowledgements [Seite 178]
21.7 - References [Seite 178]
22 - 15 Challenges for the Validation and Testing of Automated Driving Functions [Seite 180]
22.1 - Abstract [Seite 180]
22.2 - 1 Introduction [Seite 180]
22.3 - 2 Challenges for Validation and Testing [Seite 182]
22.3.1 - 2.1 Complexity of Automated Driving Functions [Seite 182]
22.3.2 - 2.2 Variation of Scenarios and Parameters [Seite 183]
22.3.3 - 2.3 Scenario Selection and Test Generation [Seite 183]
22.4 - 3 Current Methodologies/Technology Overview [Seite 184]
22.5 - 4 Validation-Global Approach [Seite 185]
22.6 - 5 Supporting Tools in the Validation Task [Seite 185]
22.7 - 6 Standardization [Seite 187]
22.8 - 7 Conclusion [Seite 188]
22.9 - Acknowledgements [Seite 188]
22.10 - References [Seite 188]
23 - 16 Automated Assessment and Evaluation of Digital Test Drives [Seite 189]
23.1 - Abstract [Seite 189]
23.2 - 1 Introduction [Seite 190]
23.3 - 2 State of the Art in Automotive Testing [Seite 191]
23.3.1 - 2.1 Test Processes and Methodologies [Seite 191]
23.3.2 - 2.2 Digital Test Drive [Seite 193]
23.4 - 3 Requirements and Constraints for Automated Assessment of Digital Test Drives [Seite 193]
23.5 - 4 Automated Assessment Concept [Seite 194]
23.5.1 - 4.1 HiL System [Seite 195]
23.5.2 - 4.2 Assessment Domain [Seite 196]
23.5.3 - 4.3 Visualization and Data Analytics Domain [Seite 196]
23.6 - 5 Application on Exemplary Driver-Assistance System [Seite 197]
23.7 - 6 Conclusion and Outlook [Seite 198]
23.8 - References [Seite 198]
24 - 17 HiFi Visual Target-Methods for Measuring Optical and Geometrical Characteristics of Soft Car Targets for ADAS and AD [Seite 200]
24.1 - Abstract [Seite 200]
24.2 - 1 Background [Seite 200]
24.3 - 2 Soft Car Targets [Seite 201]
24.4 - 3 Project Goals [Seite 202]
24.5 - 4 Initial Measurements and Results [Seite 203]
24.5.1 - 4.1 Measurement Setup [Seite 203]
24.5.1.1 - 4.1.1 Optical Measurement Setup [Seite 203]
24.5.1.2 - 4.1.2 Geometry Measurement Setup [Seite 204]
24.5.2 - 4.2 Preliminary Results [Seite 205]
24.5.2.1 - 4.2.1 Optical Measurement Results [Seite 205]
24.5.2.2 - 4.2.2 Geometry Variation Due to Assembly [Seite 206]
24.6 - 5 Conclusions and Future Work [Seite 207]
24.7 - Acknowledgments [Seite 207]
24.8 - References [Seite 207]
25 - Legal Framework and Impact [Seite 209]
26 - 18 Assessing the Impact of Connected and Automated Vehicles. A Freeway Scenario [Seite 210]
26.1 - Abstract [Seite 210]
26.2 - 1 Introduction [Seite 211]
26.3 - 2 Review of the Literature [Seite 211]
26.4 - 3 Case-Study Simulation [Seite 212]
26.4.1 - 3.1 The Traffic Model of Antwerp's Ring Road [Seite 213]
26.4.2 - 3.2 Human and CACC Drivers [Seite 214]
26.4.3 - 3.3 Assessment Metrics [Seite 216]
26.4.4 - 3.4 Simulation Scenarios [Seite 217]
26.5 - 4 Results [Seite 217]
26.5.1 - 4.1 Energy Consumption [Seite 219]
26.6 - 5 Conclusions [Seite 220]
27 - 19 Germany's New Road Traffic Law-Legal Risks and Ramifications for the Design of Human-Machine Interaction in Automated Vehicles [Seite 223]
27.1 - Abstract [Seite 223]
27.2 - 1 Introduction [Seite 223]
27.3 - 2 The Amendments to the Federal Road Traffic Act [Seite 224]
27.3.1 - 2.1 Levels of Automation Addressed [Seite 224]
27.3.2 - 2.2 Definition of "Driver" [Seite 225]
27.3.3 - 2.3 Interaction Between the Automation System and the Driver [Seite 225]
27.4 - 3 The Statutory Amendments from the Driver's Perspective [Seite 226]
27.4.1 - 3.1 Brief Overview of the Statutory Liability Regime for Drivers [Seite 226]
27.4.2 - 3.2 Ramifications of the Obligations Imposed on Automated System Users [Seite 226]
27.4.2.1 - 3.2.1 Obligation to Use the Automation System Properly [Seite 227]
27.4.2.2 - 3.2.2 Sharing of the Driving Task Between the Driver and the Automation System [Seite 227]
27.5 - 4 Liability Issues from the Manufacturer's Perspective [Seite 228]
27.5.1 - 4.1 Brief Overview of the Statutory Liability Regime for Manufacturers [Seite 228]
27.5.2 - 4.2 Product Liability Issues in Relation to Automated Vehicles [Seite 229]
27.5.2.1 - 4.2.1 Constructional Deficiencies [Seite 229]
27.5.2.2 - 4.2.2 Instructional Errors [Seite 230]
27.6 - 5 Summary [Seite 231]
27.7 - References [Seite 232]
28 - 20 Losing a Private Sphere? A Glance on the User Perspective on Privacy in Connected Cars [Seite 233]
28.1 - Abstract [Seite 233]
28.2 - 1 Introduction [Seite 233]
28.3 - 2 Literature Review [Seite 234]
28.3.1 - 2.1 Methodology [Seite 234]
28.3.2 - 2.2 Relevant Privacy Factors for the Adoption of Connected Services [Seite 235]
28.4 - 3 User Study [Seite 238]
28.4.1 - 3.1 Results [Seite 238]
28.4.2 - 3.2 Discussion [Seite 240]
28.5 - 4 Conclusion and Practical Implications [Seite 241]
2 - Organisation Committee [Seite 8]
2.1 - Funding Authority [Seite 8]
2.2 - Supporting Organisations [Seite 8]
2.3 - Organisers [Seite 8]
2.4 - Steering Committee [Seite 8]
2.5 - Conference Chair [Seite 9]
2.6 - Conference Organizing Team [Seite 9]
3 - Contents [Seite 10]
4 - Smart Sensors [Seite 13]
5 - 1 Smart Sensor Technology as the Foundation of the IoT: Optical Microsystems Enable Interactive Laser Projection [Seite 14]
5.1 - Abstract [Seite 14]
5.2 - 1 MEMS Sensors-The Hidden Champions [Seite 15]
5.2.1 - 1.1 Enablers for the Internet of Things [Seite 15]
5.2.2 - 1.2 Challenges and Barriers for IoT Sensors [Seite 15]
5.2.3 - 1.3 The Role of Smart Sensors in the IoT [Seite 16]
5.3 - 2 Interactive Laser Projection [Seite 16]
5.3.1 - 2.1 Making User Interfaces Simpler, More Flexible . and More Fun [Seite 17]
5.3.2 - 2.2 Interactive Projection in Practice [Seite 18]
5.3.3 - 2.3 A Window to the IoT [Seite 18]
5.3.4 - 2.4 Interactive Projection for the Automotive Industry [Seite 20]
5.3.4.1 - 2.4.1 Industry Teamwork [Seite 20]
5.3.5 - 2.5 Wearables and Beyond [Seite 20]
5.3.6 - 2.6 A Compact Module [Seite 21]
5.4 - 3 Conclusion [Seite 22]
6 - 2 Unit for Investigation of the Working Environment for Electronics in Harsh Environments, ESU [Seite 23]
6.1 - Abstract [Seite 23]
6.2 - 1 Introduction [Seite 24]
6.3 - 2 Monitoring Unit, ESU [Seite 24]
6.3.1 - 2.1 ESU Main Data [Seite 29]
6.3.1.1 - 2.1.1 Condensation Measurement [Seite 29]
6.3.1.2 - 2.1.2 Relative Humidity Measurement [Seite 29]
6.3.1.3 - 2.1.3 Vibration Measurement [Seite 30]
6.3.1.4 - 2.1.4 Temperature Measurement [Seite 30]
6.3.1.5 - 2.1.5 RTC [Seite 30]
6.3.1.6 - 2.1.6 User Interface [Seite 31]
6.3.2 - 2.2 Reliability of the ESU [Seite 31]
6.3.3 - 2.3 EMC Test [Seite 31]
6.4 - 3 Market Assessments [Seite 32]
6.5 - Acknowledgements [Seite 32]
6.6 - Reference [Seite 32]
7 - 3 Automotive Synthetic Aperture Radar System Based on 24 GHz Series Sensors [Seite 33]
7.1 - Abstract [Seite 33]
7.2 - 1 Introduction [Seite 34]
7.2.1 - 1.1 Automotive Radar Sensors [Seite 35]
7.2.2 - 1.2 Odometry [Seite 35]
7.3 - 2 Related Work [Seite 35]
7.4 - 3 SAR Algorithm [Seite 36]
7.5 - 4 Performance Estimation [Seite 37]
7.5.1 - 4.1 Azimuth Resolution [Seite 37]
7.5.2 - 4.2 Range Resolution [Seite 38]
7.5.3 - 4.3 Maximum Velocity [Seite 39]
7.6 - 5 Evaluation Environment [Seite 39]
7.7 - 6 Evaluation of Automotive Relevant SAR Properties [Seite 40]
7.7.1 - 6.1 Incorrect Trajectory Measurement [Seite 41]
7.7.2 - 6.2 Time-Based Sampling [Seite 42]
7.8 - 7 Simulation and Measurement [Seite 43]
7.8.1 - 7.1 Measurement [Seite 44]
7.8.2 - 7.2 Simulation [Seite 45]
7.9 - 8 Conclusion [Seite 45]
7.10 - Acknowledgements [Seite 46]
7.11 - References [Seite 46]
8 - 4 SPAD-Based Flash Lidar with High Background Light Suppression [Seite 47]
8.1 - Abstract [Seite 47]
8.2 - 1 Introduction [Seite 47]
8.3 - 2 Sensor Principle [Seite 48]
8.4 - 3 Technology and Measurements [Seite 49]
8.5 - 4 Summary [Seite 52]
8.6 - References [Seite 53]
9 - Driver Assistance and Vehicle Automation [Seite 54]
10 - 5 Enabling Robust Localization for Automated Guided Carts in Dynamic Environments [Seite 55]
10.1 - Abstract [Seite 55]
10.2 - 1 Introduction [Seite 55]
10.3 - 2 Related Work [Seite 57]
10.4 - 3 The MCL/MU Approach [Seite 58]
10.4.1 - 3.1 Map Update Control [Seite 59]
10.4.2 - 3.2 Map Update and Map Update Fusion [Seite 60]
10.5 - 4 Evaluation [Seite 62]
10.6 - 5 Conclusion [Seite 64]
10.7 - References [Seite 65]
11 - 6 Recognition of Lane Change Intentions Fusing Features of Driving Situation, Driver Behavior, and Vehicle Movement by Means of Neural Networks [Seite 66]
11.1 - Abstract [Seite 66]
11.2 - 1 Introduction [Seite 66]
11.3 - 2 Features Indicating Upcoming Lane Changes [Seite 68]
11.4 - 3 Implementation and Sensor Data [Seite 69]
11.5 - 4 Naturalistic Driving Study [Seite 70]
11.6 - 5 Neural Network for Feature Classification [Seite 70]
11.6.1 - 5.1 Artificial Neural Networks [Seite 71]
11.6.2 - 5.2 Network Design [Seite 72]
11.6.3 - 5.3 Network Parameterization [Seite 73]
11.7 - 6 Experimental Results [Seite 73]
11.8 - 7 Conclusion and Future Work [Seite 74]
11.9 - Acknowledgements [Seite 76]
11.10 - References [Seite 76]
12 - 7 Applications of Road Edge Information for Advanced Driver Assistance Systems and Autonomous Driving [Seite 77]
12.1 - Abstract [Seite 77]
12.2 - 1 Introduction [Seite 77]
12.3 - 2 Road Edge Detection [Seite 78]
12.3.1 - 2.1 Target Road Edge [Seite 78]
12.3.2 - 2.2 Road Edge Detection Result [Seite 79]
12.4 - 3 Application for Advanced Driver Assistance Systems [Seite 79]
12.4.1 - 3.1 Euro NCAP [Seite 79]
12.4.2 - 3.2 Integrated Lateral Assist System [Seite 80]
12.4.2.1 - 3.2.1 Overview of Virtual Lane Guide [Seite 80]
12.4.2.2 - 3.2.2 Target of VLG [Seite 83]
12.4.2.3 - 3.2.3 Coordination of EPS and ESC [Seite 84]
12.4.3 - 3.3 Experimental Result [Seite 85]
12.5 - 4 Application for Autonomous Driving [Seite 87]
12.5.1 - 4.1 Path Planning Algorithm [Seite 87]
12.5.1.1 - 4.1.1 Path Planner [Seite 87]
12.5.1.2 - 4.1.2 Path Selector [Seite 87]
12.5.2 - 4.2 Simulation Result [Seite 90]
12.5.3 - 4.3 Experimental Result [Seite 90]
12.6 - 5 Conclusion [Seite 91]
12.7 - References [Seite 91]
13 - 8 Robust and Numerically Efficient Estimation of Vehicle Mass and Road Grade [Seite 93]
13.1 - Abstract [Seite 93]
13.2 - 1 Introduction [Seite 93]
13.3 - 2 Methodology [Seite 95]
13.3.1 - 2.1 Test Vehicle and Test Tracks [Seite 95]
13.3.2 - 2.2 System Model [Seite 96]
13.3.3 - 2.3 Recursive Least Squares (RLS) Algorithm [Seite 97]
13.4 - 3 Sensitivity Analysis and Parameter Estimation [Seite 99]
13.4.1 - 3.1 Sensitivity Analysis [Seite 99]
13.4.2 - 3.2 Identification of Parameters and Validation of the Vehicle Model [Seite 100]
13.5 - 4 Results [Seite 102]
13.5.1 - 4.1 Validation with a Numerical Model [Seite 103]
13.5.2 - 4.2 Results in Real-World Driving Conditions [Seite 103]
13.6 - 5 Summary [Seite 104]
13.7 - References [Seite 106]
14 - 9 Fast and Accurate Vanishing Point Estimation on Structured Roads [Seite 107]
14.1 - Abstract [Seite 107]
14.2 - 1 Introduction [Seite 107]
14.3 - 2 Vanishing Point [Seite 108]
14.4 - 3 System Overview [Seite 108]
14.4.1 - 3.1 Double-Edge Detection [Seite 108]
14.4.2 - 3.2 Double-Edge Filtering [Seite 110]
14.4.3 - 3.3 Double-Edge Grouping to Lane Markings [Seite 111]
14.4.4 - 3.4 Lane Marking Filtering [Seite 112]
14.4.5 - 3.5 Lane Marking Simplification [Seite 113]
14.4.6 - 3.6 Vanishing Point Estimation [Seite 113]
14.5 - 4 Results [Seite 114]
14.6 - 5 Conclusion [Seite 116]
14.7 - References [Seite 116]
15 - 10 Energy-Efficient Driving in Dynamic Environment: Globally Optimal MPC-like Motion Planning Framework [Seite 117]
15.1 - Abstract [Seite 117]
15.2 - 1 Introduction [Seite 118]
15.3 - 2 Problem Definition [Seite 119]
15.3.1 - 2.1 Optimal Control Problem [Seite 119]
15.3.2 - 2.2 Computational Complexity [Seite 120]
15.4 - 3 Optimal Motion Planner [Seite 120]
15.4.1 - 3.1 Dynamic Programming [Seite 121]
15.4.2 - 3.2 Strategic Planning [Seite 121]
15.4.3 - 3.3 Situation-Dependent Replanning [Seite 122]
15.4.3.1 - 3.3.1 Prediction Horizon [Seite 125]
15.4.3.2 - 3.3.2 Replanning Triggering [Seite 126]
15.5 - 4 Simulation Results [Seite 126]
15.6 - 5 Conclusion [Seite 127]
15.7 - Acknowledgements [Seite 127]
15.8 - References [Seite 127]
16 - Data, Clouds and Machine learning [Seite 129]
17 - 11 Automated Data Generation for Training of Neural Networks by Recombining Previously Labeled Images [Seite 130]
17.1 - Abstract [Seite 130]
17.2 - 1 Introduction [Seite 130]
17.3 - 2 Related Work [Seite 132]
17.3.1 - 2.1 Available Public Datasets [Seite 132]
17.3.2 - 2.2 Image Manipulation and Recombination [Seite 133]
17.4 - 3 Semi-artificial Dataset Creation [Seite 133]
17.5 - 4 Evaluation [Seite 135]
17.6 - 5 Summary and Outlook [Seite 137]
17.7 - References [Seite 139]
18 - 12 Secure Wireless Automotive Software Updates Using Blockchains: A Proof of Concept [Seite 141]
18.1 - Abstract [Seite 141]
18.2 - 1 Introduction [Seite 142]
18.3 - 2 Background [Seite 143]
18.3.1 - 2.1 Wireless Automotive Software Updates [Seite 143]
18.3.2 - 2.2 Blockchains [Seite 144]
18.4 - 3 Architecture Enabling Wireless Software Updates [Seite 145]
18.4.1 - 3.1 Blockchain-Based Architecture Securing Wireless Software Updates [Seite 146]
18.4.2 - 3.2 Employing Our Architecture to Distribute New SW [Seite 147]
18.5 - 4 Proof of Concept [Seite 148]
18.6 - 5 Evaluation [Seite 150]
18.6.1 - 5.1 Overhead Due to the Use of Blockchains [Seite 150]
18.6.2 - 5.2 Latency Comparison: Local SW Update Versus SW Distribution Using BC [Seite 150]
18.6.3 - 5.3 Comparison of BC- and Certificate-Based Approaches [Seite 151]
18.7 - 6 Conclusion [Seite 152]
18.8 - References [Seite 152]
19 - 13 DEIS: Dependability Engineering Innovation for Industrial CPS [Seite 154]
19.1 - Abstract [Seite 154]
19.2 - 1 Introduction [Seite 155]
19.3 - 2 The Digital Dependability Identity (DDI) Concept [Seite 156]
19.4 - 3 The Four Industrial Use Cases in DEIS Project [Seite 158]
19.4.1 - 3.1 Automotive: Development of a Stand-Alone System for Intelligent Physiological Parameter Monitoring [Seite 158]
19.4.2 - 3.2 Automotive: Enhancement of an Advanced Driver Simulator for Evaluation of Automated Driving Functions [Seite 160]
19.4.3 - 3.3 Railway: Enabling Plug-and-Play Scenarios for Heterogeneous Railway Systems [Seite 161]
19.4.4 - 3.4 Health Care: Enhancement of Clinical Decision App for Oncology Professional [Seite 162]
19.5 - 4 Opportunities for DDI Applications [Seite 164]
19.6 - 5 Conclusions [Seite 165]
19.7 - References [Seite 166]
20 - Safety and Testing [Seite 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 [Seite 168]
21.1 - Abstract [Seite 168]
21.2 - 1 Introduction [Seite 168]
21.3 - 2 Prognostics Health Management [Seite 170]
21.4 - 3 PHM Strategy [Seite 172]
21.5 - 4 PHM Indicators and Parameters for the RUL Estimation [Seite 175]
21.6 - Acknowledgements [Seite 178]
21.7 - References [Seite 178]
22 - 15 Challenges for the Validation and Testing of Automated Driving Functions [Seite 180]
22.1 - Abstract [Seite 180]
22.2 - 1 Introduction [Seite 180]
22.3 - 2 Challenges for Validation and Testing [Seite 182]
22.3.1 - 2.1 Complexity of Automated Driving Functions [Seite 182]
22.3.2 - 2.2 Variation of Scenarios and Parameters [Seite 183]
22.3.3 - 2.3 Scenario Selection and Test Generation [Seite 183]
22.4 - 3 Current Methodologies/Technology Overview [Seite 184]
22.5 - 4 Validation-Global Approach [Seite 185]
22.6 - 5 Supporting Tools in the Validation Task [Seite 185]
22.7 - 6 Standardization [Seite 187]
22.8 - 7 Conclusion [Seite 188]
22.9 - Acknowledgements [Seite 188]
22.10 - References [Seite 188]
23 - 16 Automated Assessment and Evaluation of Digital Test Drives [Seite 189]
23.1 - Abstract [Seite 189]
23.2 - 1 Introduction [Seite 190]
23.3 - 2 State of the Art in Automotive Testing [Seite 191]
23.3.1 - 2.1 Test Processes and Methodologies [Seite 191]
23.3.2 - 2.2 Digital Test Drive [Seite 193]
23.4 - 3 Requirements and Constraints for Automated Assessment of Digital Test Drives [Seite 193]
23.5 - 4 Automated Assessment Concept [Seite 194]
23.5.1 - 4.1 HiL System [Seite 195]
23.5.2 - 4.2 Assessment Domain [Seite 196]
23.5.3 - 4.3 Visualization and Data Analytics Domain [Seite 196]
23.6 - 5 Application on Exemplary Driver-Assistance System [Seite 197]
23.7 - 6 Conclusion and Outlook [Seite 198]
23.8 - References [Seite 198]
24 - 17 HiFi Visual Target-Methods for Measuring Optical and Geometrical Characteristics of Soft Car Targets for ADAS and AD [Seite 200]
24.1 - Abstract [Seite 200]
24.2 - 1 Background [Seite 200]
24.3 - 2 Soft Car Targets [Seite 201]
24.4 - 3 Project Goals [Seite 202]
24.5 - 4 Initial Measurements and Results [Seite 203]
24.5.1 - 4.1 Measurement Setup [Seite 203]
24.5.1.1 - 4.1.1 Optical Measurement Setup [Seite 203]
24.5.1.2 - 4.1.2 Geometry Measurement Setup [Seite 204]
24.5.2 - 4.2 Preliminary Results [Seite 205]
24.5.2.1 - 4.2.1 Optical Measurement Results [Seite 205]
24.5.2.2 - 4.2.2 Geometry Variation Due to Assembly [Seite 206]
24.6 - 5 Conclusions and Future Work [Seite 207]
24.7 - Acknowledgments [Seite 207]
24.8 - References [Seite 207]
25 - Legal Framework and Impact [Seite 209]
26 - 18 Assessing the Impact of Connected and Automated Vehicles. A Freeway Scenario [Seite 210]
26.1 - Abstract [Seite 210]
26.2 - 1 Introduction [Seite 211]
26.3 - 2 Review of the Literature [Seite 211]
26.4 - 3 Case-Study Simulation [Seite 212]
26.4.1 - 3.1 The Traffic Model of Antwerp's Ring Road [Seite 213]
26.4.2 - 3.2 Human and CACC Drivers [Seite 214]
26.4.3 - 3.3 Assessment Metrics [Seite 216]
26.4.4 - 3.4 Simulation Scenarios [Seite 217]
26.5 - 4 Results [Seite 217]
26.5.1 - 4.1 Energy Consumption [Seite 219]
26.6 - 5 Conclusions [Seite 220]
27 - 19 Germany's New Road Traffic Law-Legal Risks and Ramifications for the Design of Human-Machine Interaction in Automated Vehicles [Seite 223]
27.1 - Abstract [Seite 223]
27.2 - 1 Introduction [Seite 223]
27.3 - 2 The Amendments to the Federal Road Traffic Act [Seite 224]
27.3.1 - 2.1 Levels of Automation Addressed [Seite 224]
27.3.2 - 2.2 Definition of "Driver" [Seite 225]
27.3.3 - 2.3 Interaction Between the Automation System and the Driver [Seite 225]
27.4 - 3 The Statutory Amendments from the Driver's Perspective [Seite 226]
27.4.1 - 3.1 Brief Overview of the Statutory Liability Regime for Drivers [Seite 226]
27.4.2 - 3.2 Ramifications of the Obligations Imposed on Automated System Users [Seite 226]
27.4.2.1 - 3.2.1 Obligation to Use the Automation System Properly [Seite 227]
27.4.2.2 - 3.2.2 Sharing of the Driving Task Between the Driver and the Automation System [Seite 227]
27.5 - 4 Liability Issues from the Manufacturer's Perspective [Seite 228]
27.5.1 - 4.1 Brief Overview of the Statutory Liability Regime for Manufacturers [Seite 228]
27.5.2 - 4.2 Product Liability Issues in Relation to Automated Vehicles [Seite 229]
27.5.2.1 - 4.2.1 Constructional Deficiencies [Seite 229]
27.5.2.2 - 4.2.2 Instructional Errors [Seite 230]
27.6 - 5 Summary [Seite 231]
27.7 - References [Seite 232]
28 - 20 Losing a Private Sphere? A Glance on the User Perspective on Privacy in Connected Cars [Seite 233]
28.1 - Abstract [Seite 233]
28.2 - 1 Introduction [Seite 233]
28.3 - 2 Literature Review [Seite 234]
28.3.1 - 2.1 Methodology [Seite 234]
28.3.2 - 2.2 Relevant Privacy Factors for the Adoption of Connected Services [Seite 235]
28.4 - 3 User Study [Seite 238]
28.4.1 - 3.1 Results [Seite 238]
28.4.2 - 3.2 Discussion [Seite 240]
28.5 - 4 Conclusion and Practical Implications [Seite 241]
