Schulze / Müller / Meyer Advanced Microsystems for Automotive Applications 2016

Smart Systems for the Automobile of the Future
1. Auflage 2016
ISBN: 978-3-319-44766-7
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 264 Seiten, eBook

Reihe: Lecture Notes in Mobility

This book contains the papers presented at the 20th anniversary edition of the AMAA conference held in Brussels, Belgium in 2016. The theme of the conference was “Smart Systems for the Automobile of the Future”. The automobile is currently being reshaped at unprecedented pace. Automation and electrification are the two dominant megatrends which dramatically change the choice and design of components, systems, vehicular architectures and ultimately the way we use cars in the coming decades. Novel E/E architectures, vehicular connectivity and cloud services will be key to extending the perception and decision-making horizons of automated vehicles, to enable cooperative functions and a seamless digital user experience. The AMAA’s ongoing mission to detect novel trends in automotive ICT, electronics and smart systems and to discuss the technological implications is once again reflected in this volume. The book will be a valuable read for research experts and professionals in the automotive and smart systems industry but the book may also be beneficial for graduate students.

Zielgruppe

Research

Autoren/Hrsg.

Schulze, Tim

Müller, Beate

Meyer, Gereon

Inhaltsverzeichnis

1;Preface;6
2;Supporters and Organisers;8
3;Steering Committee;9
4;Contents;10
5;Networked Vehicles & Navigation;13
6;1 Requirements and Evaluation of a Smartphone Based Dead Reckoning Pedestrian Localization for Vehicle Safety Applications;14
6.1;Abstract;14
6.2;1 Introduction;15
6.3;2 Localization Estimation Filter;16
6.3.1;2.1 Sensor Error Models and Impact of the Error Terms;17
6.3.2;2.2 Error-State Model;18
6.3.3;2.3 Observation Models;18
6.3.3.1;2.3.1 Loosely Coupled GNSS Measurement;19
6.3.3.2;2.3.2 Tightly Coupled GNSS Measurement;19
6.3.3.3;2.3.3 Barometric Height Measurement;19
6.4;3 Methods;20
6.4.1;3.1 Reference Measurement System;20
6.4.2;3.2 Measurement Environment;21
6.5;4 Results;22
6.5.1;4.1 GNSS Receiver and Method Comparison;22
6.5.2;4.2 Velocity Accuracy;22
6.5.3;4.3 Simulated Short-Time GNSS Outage;23
6.5.4;4.4 Location Estimation Accuracy Requirements for Pedestrian Protection Systems;23
6.6;5 Discussion;26
6.7;6 Conclusions;27
6.8;References;28
7;2 Probabilistic Integration of GNSS for Safety-Critical Driving Functions and Automated Driving—the NAVENTIK Project;29
7.1;Abstract;29
7.2;1 Introduction to GNSS in Automotive Applications;30
7.3;2 Confidence Adaptive Use Cases;33
7.3.1;2.1 E-Call Extension;33
7.3.2;2.2 Active Navigation;33
7.4;3 NAVENTIK Measures and System Architecture;35
7.5;4 Conclusion;38
7.6;Acknowledgments;38
7.7;References;39
8;3 Is IEEE 802.11p V2X Obsolete Before it is Even Deployed?;40
8.1;Abstract;40
8.2;1 Introduction;40
8.3;2 Related Work;41
8.4;3 The ETSI ITS-G5 Standard;42
8.4.1;3.1 Access Layer;42
8.4.2;3.2 Networking and Transport Layer;43
8.4.3;3.3 The Common Data Dictionary;44
8.4.4;3.4 Cooperative Awareness Basic Service;44
8.4.5;3.5 Security Services;45
8.5;4 Evaluation Framework and Methodology;45
8.6;5 Results;47
8.7;6 Conclusion and Future Work;49
8.8;Acknowledgments;49
8.9;References;49
9;4 Prototyping Framework for Cooperative Interaction of Automated Vehicles and Vulnerable Road Users;51
9.1;Abstract;51
9.2;1 Introduction;52
9.3;2 Prototyping Hardware Equipment and Sensorial Systems;52
9.3.1;2.1 Overview of Sensorial Systems;52
9.3.2;2.2 Research Vehicle for Automated Driving;53
9.3.3;2.3 Prototyping Testbed—Mobile Road Side Unit;55
9.3.4;2.4 Mobile Devices for VRUs;55
9.4;3 Software Framework for Prototyping;55
9.4.1;3.1 Software Modules Overview;55
9.4.2;3.2 Algorithmic Components;56
9.4.2.1;3.2.1 Vehicle Trajectory Representation;56
9.4.2.2;3.2.2 Intent Estimation;57
9.5;4 Application Scenarios;58
9.5.1;4.1 Manoeuvre Planning for Automated Green Driving and VRU Safety;58
9.5.2;4.2 Cooperative Interactions Between VRU and Automated Vehicles;59
9.6;5 Conclusion;60
9.7;Acknowledgment;60
9.8;References;60
10;5 Communication Beyond Vehicles—Road to Automated Driving;62
10.1;Abstract;62
10.2;1 Trends—Automated Driving and Smart System;63
10.3;2 Robustness—the Need for Smart Vehicles;64
10.4;3 Evolution—Communication Architectures;64
10.5;4 Essentiality—V2X Communication;67
10.6;5 Urgency—Secured Vehicle Architectures;69
10.7;6 Outlook—Requirements Secured Car Communication;71
10.8;References;71
11;6 What About the Infrastructure?;72
11.1;Abstract;72
11.2;1 Variation in Vehicles;72
11.3;2 Evolution in Car Systems;73
11.3.1;2.1 Introduction;73
11.3.2;2.2 Lateral Assistance Systems;73
11.3.3;2.3 Longitudinal Assistance Systems;74
11.3.4;2.4 Automated Cars;75
11.3.5;2.5 Fleets;76
11.3.6;2.6 Location, Communication, Maps;76
11.4;3 Involved Parties;77
11.4.1;3.1 The User;77
11.4.2;3.2 Road Operators;78
11.4.3;3.3 Law Makers;79
11.5;4 Conclusion;79
11.6;References;80
12;Advanced Sensing, Perception and Cognition Concepts;81
13;7 Towards Dynamic and Flexible Sensor Fusion for Automotive Applications;82
13.1;Abstract;82
13.2;1 Introduction;83
13.3;2 Related Work;84
13.4;3 SADA System Architecture;85
13.4.1;3.1 Overview;85
13.4.2;3.2 Distributed System;86
13.5;4 Communication Architecture;89
13.6;5 Preliminary Experimental Results;91
13.7;6 Conclusion;93
13.8;Acknowledgments;93
13.9;References;93
14;8 Robust Facial Landmark Localization for Automotive Applications;95
14.1;Abstract;95
14.2;1 Introduction;96
14.3;2 Related Work;96
14.4;3 Overview—AAM Framework Using MCT Features;97
14.5;4 AAM Using MCT Features;98
14.5.1;4.1 Initial Guess Generation;100
14.5.2;4.2 Model Generation and Parameter Optimization for Matching;100
14.5.3;4.3 Parameter Constraints and Weighting for Matching;101
14.5.4;4.4 Occlusion Handling, Quality and Intelligent Stopping Criterion;101
14.5.5;4.5 Head-Pose Estimation;103
14.6;5 Evaluation;103
14.7;6 Conclusion;105
14.8;References;106
15;9 Using eHorizon to Enhance Camera-Based Environmental Perception for Advanced Driver Assistance Systems and Automated Driving;107
15.1;Abstract;107
15.2;1 Introduction;107
15.3;2 The eHorizon;108
15.4;3 The Problem and Solution of Mapping a Camera Picture to the Real World and Vise Versa;110
15.4.1;3.1 Coordinate System and the Perceptible Range of the Camera;110
15.4.2;3.2 Analysis Based on Linear Geometric Optics;110
15.4.3;3.3 The Inverse-Light-Ray and Its Construction;112
15.4.4;3.4 The Inverse-Light-Ray-Method for Mapping a Picture to the Real Word;113
15.4.5;3.5 The Mapping from Real Word to Picture;114
15.5;4 Conclusion and Outlook;115
15.6;References;115
16;10 Performance Enhancements for the Detection of Rectangular Traffic Signs;117
16.1;Abstract;117
16.2;1 Introduction;117
16.3;2 Related Work;118
16.4;3 Radial Symmetry Detection;119
16.5;4 Improvement Potential;120
16.5.1;4.1 Scaled Voting Arrays;120
16.5.2;4.2 Altered Voting Process;121
16.6;5 Implementation;122
16.7;6 Results;123
16.7.1;6.1 Benchmark;123
16.7.2;6.2 Qualitative Performance;123
16.7.3;6.3 Quantitative Performance;125
16.7.4;6.4 Conclusion;126
16.8;7 Future Work;126
16.9;References;126
17;11 CNN Based Subject-Independent Driver Emotion Recognition System Involving Physiological Signals for ADAS;128
17.1;Abstract;128
17.2;1 Introduction;129
17.3;2 Physiological Signals Used;131
17.4;3 Research Methodology;131
17.4.1;3.1 Physiological Datasets Used;132
17.4.2;3.2 Feature Extraction;132
17.4.3;3.3 Classification Concept;133
17.4.4;3.4 Fusion Concept;136
17.5;4 Experimental Results;137
17.6;5 Conclusion;139
17.7;References;140
18;Safety and Methodological Challenges of Automated Driving;142
19;12 Highly Automated Driving—Disruptive Elements and Consequences;143
19.1;Abstract;143
19.2;1 Disruptive Elements;143
19.2.1;1.1 The Physical Change;144
19.2.2;1.2 The Change of Responsibility;145
19.2.2.1;1.2.1 Higher Safety Expectations;145
19.2.2.2;1.2.2 Safe and Comfortable Use of New Freedom;147
19.2.2.3;1.2.3 Common and Permanent Observation and Learning;148
19.2.3;1.3 The Change of the Vehicle Getting Part of a Mobile Network (Data-Driven Mobility Ecosystem);149
19.3;2 Conclusions;152
19.4;3 Summary and Outlook;153
19.5;Reference;154
20;13 Scenario Identification for Validation of Automated Driving Functions;155
20.1;Abstract;155
20.2;1 Introduction;155
20.3;2 Validation of ADS Functions Using Real-World Scenarios;157
20.3.1;2.1 Definition of a Scenario;157
20.3.2;2.2 Real-World Scenarios for Testing and Validation of ADS;158
20.4;3 Detection of Driving Events in Microscopic Traffic Data;159
20.4.1;3.1 Data Set;159
20.4.2;3.2 Detection Methods;160
20.4.3;3.3 Results;162
20.5;4 Discussion and Conclusion;163
20.6;References;165
21;14 Towards Characterization of Driving Situations via Episode-Generating Polynomials;166
21.1;Abstract;166
21.2;1 Introduction;166
21.3;2 Definition of Situation and Episode;167
21.4;3 Generation and Evaluation of Episodes;168
21.4.1;3.1 Generation;169
21.4.2;3.2 Evaluation;169
21.5;4 Identification of Collisions;170
21.5.1;4.1 Coarse Collision Check;170
21.5.2;4.2 Fine Collision Check;171
21.6;5 Criticality Assessment of the Situation;171
21.7;6 Evaluation of Example Situations;172
21.8;7 Discussion;173
21.9;8 Conclusion;174
21.10;References;174
22;15 Functional Safety: On-Board Computing of Accident Risk;175
22.1;Abstract;175
22.2;1 Introduction;175
22.3;2 A New Solution for Measuring the on-Board Risk of Accident;176
22.4;3 Results and Discussion of Validation Tests;177
22.5;4 Conclusion;179
22.6;References;180
23;Smart Electrified Vehicles and Power Trains;181
24;16 Optimal Predictive Control for Intelligent Usage of Hybrid Vehicles;182
24.1;Abstract;182
24.2;1 Context of This Development for Connected Vehicles;183
24.2.1;1.1 The “from Well to Tank” Path;183
24.2.2;1.2 The “from Tank to Wheels” Path;184
24.2.3;1.3 The “from Wheels to Miles” Path;184
24.2.4;1.4 The Energy Optimization Purpose;185
24.2.5;1.5 The PMP Method;186
24.3;2 Power and Torque Efficiency Optimization in Hybrid Configurations;187
24.3.1;2.1 “Local” Optimization;187
24.3.2;2.2 “PMP”-Based Optimization of Torque Split;188
24.3.3;2.3 Predictive Complement in Connected Configurations;190
24.3.4;2.4 Actual Results;190
24.4;3 Trajectory Optimization on Given Trip;191
24.4.1;3.1 General Rules for Eco-Driving;191
24.4.2;3.2 “PMP”-Based Optimization;192
24.4.3;3.3 Actual Results;193
24.5;4 Merged Optimization;194
24.5.1;4.1 General Optimization System;194
24.6;5 Model-Based Control Impacts on Embedded SW Architectures;195
24.6.1;5.1 Model-Based Concepts;195
24.6.1.1;5.1.1 “External” Plant Model for Validation;195
24.6.1.2;5.1.2 “Internal” Plant Model in SW;196
24.6.2;5.2 Model-in-the-Software (“MIS”) Concepts;196
24.6.2.1;5.2.1 Delay ‘Compensation’;196
24.6.2.2;5.2.2 Onboard Diagnostic;197
24.6.2.3;5.2.3 Model-Based Predictive Control (“MBPC”);197
24.6.2.4;5.2.4 Pontryagin Maximum Principle;197
24.6.3;5.3 Consequences on Hardware Architecture;198
24.7;6 Conclusion;198
24.8;References;199
25;17 Light Electric Vehicle Enabled by Smart Systems Integration;200
25.1;Abstract;200
25.2;1 A Comprehensive Approach for LEV Development;201
25.3;2 Multi-disciplinary Investigation and Definition of the Specifications;202
25.3.1;2.1 Lightweight Seats;203
25.3.2;2.2 Assisted Rear e-Lift;204
25.3.3;2.3 HMI Based on Gesture Recognition;204
25.3.4;2.4 LEV Test in a Realistic Scenario;204
25.4;3 Energy Efficient Torque Management System;205
25.4.1;3.1 Handling Performance and Energy Efficiency;205
25.4.2;3.2 Parking Capability;206
25.5;4 Advanced Steering and Suspension System Design;206
25.5.1;4.1 Front Suspension/Steering System Design;207
25.6;5 Direct-Drive Air Cooled In-Wheel Motor with an Integrated Inverter;207
25.6.1;5.1 Flexible Integration;208
25.6.2;5.2 Thermal Performance Optimization and Mechanical/Thermo-mechanical Robustness Analysis;209
25.7;6 Innovative HMI Based on Gesture Recognition;210
25.8;7 E/E Architecture and Control Systems Development;211
25.9;8 Conclusions;213
25.10;Acknowledgments;214
25.11;References;214
26;18 Next Generation Drivetrain Concept Featuring Self-learning Capabilities Enabled by Extended Information Technology Functionalities;215
26.1;Abstract;215
26.2;1 Introduction;215
26.3;2 State-of-the-Art for Electrical Drive-Train Systems;216
26.4;3 Novel Concept for Drive-Train Architecture;217
26.4.1;3.1 System Architecture and Design;217
26.4.2;3.2 Main Technological Challenges;220
26.5;4 Conclusion;221
26.6;References;222
27;19 Embedding Electrochemical Impedance Spectroscopy in Smart Battery Management Systems Using Multicore Technology;223
27.1;Abstract;223
27.2;1 Introduction;224
27.3;2 Deployment of Safe and Secure Multicore-Based Computing Platforms;225
27.3.1;2.1 Migration of BMS Control Strategies to Multicore Platforms;225
27.3.2;2.2 INCOBAT Multicore Development Framework;225
27.3.3;2.3 Hardware Safety and Security Approach;227
27.4;3 Embedding EIS in Automotive Control Units;229
27.5;4 Thermo-Mechanical Stress Investigations;231
27.5.1;4.1 Ensuring Functionality of the Modules During Development Phase;232
27.5.2;4.2 Environmental and Lifetime Testing;233
27.6;5 Outlook: Demonstrator Vehicle Integration;233
27.7;6 Conclusion;234
27.8;References;235
28;20 Procedure for Optimization of a Modular Set of Batteries in a High Autonomy Electric Vehicle Regarding Control, Maintenance and Performance;236
28.1;Abstract;236
28.2;1 Introduction;236
28.3;2 Modeling of Gorila EV’s Batteries;238
28.3.1;2.1 Batteries Operation;238
28.3.2;2.2 Choice of Batteries;238
28.4;3 Methodology;240
28.5;4 Testing and Analysis;240
28.5.1;4.1 Definition of Parameters;240
28.5.2;4.2 Test Characteristics;241
28.5.2.1;4.2.1 Vehicle in Initial State;241
28.5.2.2;4.2.2 Vehicle After a Balancing of Batteries and Its Corresponding Full Charge;242
28.6;5 Results;242
28.6.1;5.1 Vehicle in Initial Stage;242
28.6.2;5.2 Vehicle After a Balancing of Batteries and Its Corresponding Full Charge;245
28.7;6 Protocol for Selective Charging of the Unbalanced Batteries;246
28.8;7 Summary and Conclusions;247
28.9;References;248
29;21 Time to Market—Enabling the Specific Efficiency and Cooperation in Product Development by the Institutional Role Model;249
29.1;Abstract;249
29.2;1 Introduction;250
29.3;2 Institutional Economic Role Model as Methodological Approach;252
29.4;3 Literature Review—Hypothesis Development;253
29.5;4 Methodology—Research Design;257
29.6;5 Statistical Analysis and Results;258
29.7;6 Approach for a Procedure Model;260
29.8;7 Conclusion;262