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]
