Automotive Systems and Software Engineering (eBook)

Preface 5
Acknowledgments 8
Contents 9
Part I Introduction 11
Automotive Software Engineering: Past, Present, and Future 12
1 Introduction 12
2 Evolution of Automotive Software Engineering 13
3 C-ITS 15
4 Towards Autonomous and Cooperative Driving 16
References 16
Part II Automotive Software Development 18
Requirements Engineering for Automotive Embedded Systems 19
1 Introduction 19
2 Requirements and Requirements Engineering 21
3 Types of Requirements in Automotive Software Development 22
3.1 Textual Requirements 23
3.2 Use Cases 24
3.3 Model-Based Requirements 25
3.4 Requirements as Models 27
4 Measuring Requirements and Requirement Specifications 28
5 How All These Requirements Come Together 29
6 Current Trends of Software Requirements Engineering in the Automotive Domain 30
7 Further Reading 31
7.1 Requirements Specification Languages 33
8 Conclusions 34
References 34
Status Report on Automotive Software Development 37
1 Introduction 37
2 Recent Challenges in Automotive Software Engineering 39
2.1 Virtual Development and Validation 39
2.2 New Development Techniques 41
2.3 Feasible Development Methods 41
2.4 Validation and Release Process 41
2.5 Cyber Security 42
3 Related Work 43
4 Common Tools and Toolchains 44
4.1 Function Development and Simulation 44
4.1.1 Automotive Open System Architecture 45
4.1.2 Automotive Data and Time-Triggered Framework 46
4.1.3 Electronics Architecture and Software Technology-Architecture Description Language 47
4.1.4 MATLAB/Simulink and TargetLink 48
4.1.5 Rational Rhapsody/Harmony 49
4.1.6 Safety-Critical Application Design Environment 50
4.1.7 Simulation and Test of Anything 51
4.2 Traffic Simulation 53
4.2.1 Aimsun Next 53
4.2.2 Simulation of Urban MObility 54
4.2.3 Vissim and Viswalk 55
4.2.4 Virtual Test Drive 56
4.2.5 CarMaker 56
4.2.6 Pedestrian and Cyclist Simulation 57
4.3 System Specification and Documentation 57
4.3.1 Office 58
4.3.2 Rational DOORS 59
5 Classification in the Automotive Development Process 59
6 Outlook: The Future of Automotive Development 62
References 63
State-of-the-Art Tools and Methods Used in the Automotive Industry 66
1 When Reading This Chapter 66
2 A Short Introduction upon Software within Cars 67
3 Development Process and Available Documents 71
4 Tool Usage 74
5 Testing Approaches 75
6 Software Fault Prediction (SFP): A New Idea to Be Integrated 77
References 78
Part III Automotive Software Reuse 81
Software Reuse: From Cloned Variants to Managed Software Product Lines 82
1 Introduction 82
2 Background 84
2.1 Software Product Lines 84
2.2 Running Example Automotive Body Comfort System 86
3 Variability Realization Mechanisms 87
3.1 State of Practice in Variability Realization 87
3.2 State of the Art in Variability Realization Mechanisms 89
3.2.1 Annotative Variability Realization Mechanisms 89
3.2.2 Compositional Variability Realization Mechanisms 91
3.2.3 Transformational Variability Realization Mechanisms 93
4 From Cloned Variants to Managed Software Product Lines 95
4.1 Mining Variability from Cloned Variants 97
4.1.1 Compare Phase 98
4.1.2 Match Phase 99
4.1.3 Merge Phase 100
4.2 Generating a Delta-Oriented Software Product Line 102
4.2.1 Delta Operation Identification 102
4.2.2 Delta Language Generation 104
4.2.3 Delta Module Generation 104
5 Realization as Tool Suite DeltaEcore 106
5.1 Delta Language Creation 106
5.2 Software Product Line Definition 109
5.3 Variant Derivation 109
6 Conclusion 110
References 111
Variability Identification and Representation for Automotive Simulink Models 114
1 Introduction 115
2 Variability Identification and Representation Framework 116
3 Variability Identification 119
3.1 Simone: An Initial Approximation 119
4 Variability Operators 120
5 Tagging Subsystem Variability 122
5.1 Tagging Using #ifdef 123
5.2 Tagging via Graph Algorithms 130
6 Representing Variability 133
6.1 Block Variability 133
6.2 Input/Output Variability 135
6.3 Function Variability 135
6.4 Layout Variability 137
6.5 Subsystem Name Variability 138
6.6 Combinations of Operators 138
6.7 Creating Variability Models Directly in Simulink 138
7 Related Work 139
8 Conclusion 142
References 143
Defining Architecture Framework for Automotive Systems 145
1 Introduction 145
1.1 Chapter Outline 147
2 Automotive AFs and Viewpoints 147
2.1 Automotive Architecture Frameworks 148
2.2 Extracting Viewpoints from Automotive AFs 149
2.3 Discussion 154
3 Automotive ADLs and Viewpoints 154
3.1 Automotive ADLs 155
3.2 Extracting Viewpoints from Automotive ADLs 158
3.3 Discussion 164
4 Architecture Framework for Automotive Systems 165
5 Conclusion 170
References 170
Part IV E/E Architecture and Safety 173
The RACE Project: An Informatics-Driven Greenfield Approach to Future E/E Architectures for Cars 174
1 Introduction 175
2 A Brief History of ICT E/E Architectures for Cars 176
3 A Set of Requirements for a New Architecture 180
3.1 Integration of New Functions in Software to Achieve Faster Development Times 180
3.2 Enabling New Business Models by Software Updates and Opening Function Development to Third Parties 181
3.3 Built-In Safety and Security 182
3.4 Simplifying Migration from Other Platforms 182
4 RACE Architecture Concepts 183
4.1 General Structure and Communications 184
4.2 Built-In Safety and Security 185
4.2.1 Separation Concept 185
4.2.2 Scalable Safety 185
5 Implementation and Tooling 187
5.1 Information Flow 187
5.2 Software Design 189
6 Realization on the Hardware Level 192
7 Deployment and Business Opportunities 194
8 Summary 196
References 198
Development of ISO 11783 Compliant Agricultural Systems: Experience Report 199
1 Introduction 200
2 Background of the ISO 11783 Standard 201
2.1 Virtual Terminal 207
2.2 ISOAgLib Open-Source Library 210
2.3 Tool Chain 211
3 System Architecture of the VT Server ECU 211
4 System Architecture of VT Client ECU 218
5 Architecture of PGN Analyzer 219
6 Experimental Results 222
7 Conclusion and Future Work 222
References 225
Safety-Driven Development and ISO 26262 226
1 Introduction 226
1.1 ISO 26262 227
1.2 Functional Safety Definition 227
1.3 Functional Safety Goals 229
2 Safety Management 230
2.1 Safety Culture 232
2.2 Safety Culture Metrics 234
2.3 Confirmation Measures 235
3 Safety Lifecycle: Integrated V Model 235
4 Safety Architecture Patterns 240
5 Model-Driven Design for Safety Assessment 242
5.1 Modeling Safety Standards 243
5.2 Modeling Safety Argumentation 244
5.2.1 Safety Case Construction with Controlled Language 245
5.2.2 A GSN Editor with SBVR Functionality 246
5.3 Safety Case Assessment 246
5.3.1 Overview of Safety Assessment Approaches 246
5.3.2 An Alternative Safety Assessment Process 250
5.3.3 The AGSN Editor 251
6 Conclusions 253
References 253
Part V C-ITS and Security 256
Introduction to Cooperative Intelligent Transportation Systems 257
1 Introduction 257
2 Vehicle Networking 258
3 View on C-ITS 261
4 Overview 263
References 263
In-Vehicle Networks and Security 264
1 Introduction 264
2 Connectivity: Driving the Need for Security 265
2.1 Potential Risks 266
2.2 The Connected Vehicle: An Attractive Target for Hackers 267
2.3 The Challenge 268
3 No Safety Without Security 269
4 Applying Best Practices 270
4.1 Defense in Depth 270
4.2 From Afterthought to Integral Approach 270
4.3 Adoption of Existing Technologies 271
4.4 Risk Analysis 271
5 How to Secure a Vehicle 272
5.1 The Vehicle Architecture Axis 272
5.2 The Time Axis 272
6 A Multilayer Security Framework 274
6.1 Layer 1: Secure Interface 275
6.2 Layer 2: Secure Gateway 275
6.3 Layer 3: Secure Network 276
6.4 Layer 4: Secure Processing 276
6.5 Which Layers to Apply and in Which Order? 277
7 Hardware Trust Anchors 277
8 Life-Cycle Management 278
8.1 Key Management and Crypto Agility 278
8.2 Secure Firmware Upgrades 279
9 Standardization 279
10 Conclusions 280
References 280
Security for V2X 282
1 Introduction 282
2 Use Cases and Requirements for C-ITS 283
3 V2X Communication 285
3.1 Ensuring Trust Using ECDSA 285
3.2 Privacy of Sender 286
4 Public Key Infrastructure 287
4.1 Life-Cycle Management 290
4.1.1 At Production 290
4.1.2 Before or At Sales 291
4.1.3 After Sales 291
4.1.4 In Operation (While Driving) 291
4.1.5 End of Life 292
5 Standardization 292
6 Conclusion 292
Bibliography 293
Intelligent Transportation System Infrastructure and Software Challenges 294
1 Motivation 294
2 Goal 297
2.1 Key Characteristics 298
2.1.1 Openness of Interfaces 298
2.1.2 Operator Independence 298
2.1.3 Security and Privacy 299
2.1.4 Economical Feasibility 299
2.2 Reuse of Existing Architectures 299
3 Architecture 301
3.1 Hybrid Communication 301
3.2 GeoMessaging and Bridge 302
3.3 Security 306
3.4 Service Concepts 307
3.4.1 Service Usage 308
3.4.2 Pseudonym Service Usage 308
3.4.3 Service Directory 311
3.4.4 Service Announcement 315
3.5 Role Models 315
4 Outlook 317
References 317
Part VI Future Trends 319
Future Trends in Electric Vehicles Enabled by Internet Connectivity, Solar, and Battery Technology 320
1 Introduction 321
2 The Evolution of the Automotive Ecosystem in the Coming Decade 321
3 Solar Energy Will Disrupt the Energy Market and Vehicle Energy Source 323
4 Grid Connection Stays Important 327
5 Battery Electric EV Powertrain Best Efficiency 330
6 Lightweight Urban Vehicle and Aerodynamic Highway Vehicle 332
7 Battery EV Is Ideal for Ride and Car Sharing 332
8 Solar Cars Are Most Energy Efficient and Can Have a Driving Range Up to 1500 Km 333
9 Hybrid Vehicles 334
10 TU/e Automotive Teams 336
10.1 University Racing Eindhoven 337
10.2 TU/ecomotive 338
10.3 Solar Team Eindhoven 339
10.4 STORM 340
11 Conclusions 341
References 342
Autonomous Vehicles: State of the Art, Future Trends, and Challenges 344
1 Introduction 344
1.1 Levels of Vehicle Automation 345
1.2 Autonomous Vehicles Ecosystem 346
2 Autonomous Driving: State of the Art 347
2.1 Vehicle Functionality 348
2.2 Vehicle Architectures 350
3 Autonomous Driving: Trends and Future Direction 351
3.1 Artificial Intelligence 352
3.2 Self-adaptive Systems 353
3.3 Continuous Software Engineering 354
3.4 User Aspects 355
4 Verification of Autonomous Driving: Challenges for Guaranteeing Safety 356
4.1 Safety Standards Are Not Ready for Autonomous Vehicles 357
4.2 Uncertainty Is Everywhere 358
4.3 The Use of Machine Learning 358
4.4 Validation Process Is Not Clear 360
4.5 Nontechnical Challenges 360
5 Conclusions 361
References 361