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