ELIV 2024

doi:10.51202/9783181024416

ELIV 2024

Reihe: VDI-Berichte

Band 2441 1. Auflage 2024

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doi.org/10.51202/9783181024416
ISBN print: 978-3-18-092441-0
ISBN online: 978-3-18-102441-6
VDI Verlag, Düsseldorf VDI Verlag, Düsseldorf

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Zusammenfassung

Content AI Automotive ASIL & GenAI How to Integrate GenAI in Automotive Enhance GenAI or Change Development Philosophy? 1 Speeding up Generative-AI in Software-Defined Vehicles 13 Automotive Trend Session AI Automotive Building and scaling a machine learning platform to unlock AI in connected car services 29 Quo vadis Vision Zero – Can AI help us to make our vision come true? 41 Auditing Guidelines for AI-based AD/ADAS Components focusing on AI Security 53 AI Automotive New Dimensions From Niche to Mainstream: Harnessing Generative AI for Automotive Excellence GenAI is not Enough! 67 AI in Traffic: New Dimensions of Vehicle Intelligence New method for optimizing traffic flow through AI-based implementation of real-world traffic data from other road users 81 Enabling Automotive MLOps with Open-Source Based Software 93 Automotive Trend Session Digital Homologation Statistical methods and Monte-Carlo simulation ensure the safety case of the environmental sensor performance in BMW’s first L3 function, the BMW Personal Pilot L3 105 The Path to Virtual Homologation Aspec...

Schlagworte

Kapitel Ausklappen | EinklappenAutorenSeiten
I–XIV Titelei/Inhaltsverzeichnis I–XIV
1–28 AI Automotive ASIL & GenAI 1–28
- 1–12 How to Integrate GenAI in Automotive Enhance GenAI or Change Development Philosophy? U. Bodenhausen U. Bodenhausen 1–12
- 13–28 Speeding up Generative-AI in Software-Defined Vehicles J. Richenhagen, M. Engelhard, J. Jeub, S. Kriebel, A. Kugler J. Richenhagen, M. Engelhard, J. Jeub, S. Kriebel, A. Kugler 13–28
29–66 Automotive Trend Session AI Automotive 29–66
- 29–40 Building and scaling a machine learning platform to unlock AI in connected car services S. Zimmermann, W. Lenders, T. Lian Abt, M. Kuhn, P. Weber, A. Ganser S. Zimmermann, W. Lenders, T. Lian Abt, M. Kuhn, P. Weber, A. Ganser 29–40
- 41–52 Quo vadis Vision Zero – Can AI help us to make our vision come true? P. Dreiseitel, R. Grewe P. Dreiseitel, R. Grewe 41–52
- 53–66 Auditing Guidelines for AI-based AD/ADAS Components focusing on AI Security G. Schneider, F. Woitschek, A. von Twickel, R. Plaga G. Schneider, F. Woitschek, A. von Twickel, R. Plaga 53–66
67–104 AI Automotive New Dimensions 67–104
- 67–80 From Niche to Mainstream: Harnessing Generative AI for Automotive Excellence. GenAI is not Enough! A. Vickers, P. Fintl, D. Hughes, M. Roberts A. Vickers, P. Fintl, D. Hughes, M. Roberts 67–80
- 81–92 AI in Traffic: New Dimensions of Vehicle Intelligence New method for optimizing traffic flow through AI-based implementation of real-world traffic data from other road users K. Szelechowicz, K. A. Rösler K. Szelechowicz, K. A. Rösler 81–92
- 93–104 Enabling Automotive MLOps with Open-Source Based Software X. Wang, A. Hübner, M. Kühl, B. Zlotnik X. Wang, A. Hübner, M. Kühl, B. Zlotnik 93–104
105–126 Automotive Trend Session Digital Homologation 105–126
- 105–116 Statistical methods and Monte-Carlo simulation ensure the safety case of the environmental sensor performance in BMW’s first L3 function, the BMW Personal Pilot L3 A. Schleich, F. Modes, T. Schaller, M. Werling A. Schleich, F. Modes, T. Schaller, M. Werling 105–116
- 117–126 The Path to Virtual Homologation Aspects of the necessary framework for product safety in ADAS/AD C. Wiegand, F. Sontheim, J.-E. Stavesand, A. Amoroso, S. Rößner C. Wiegand, F. Sontheim, J.-E. Stavesand, A. Amoroso, S. Rößner 117–126
127–150 Software/SDV 127–150
- 127–132 Has the holy grail been found? Using Linux for safety-related applications M. Neukirchner M. Neukirchner 127–132
- 133–140 State-of-The-Art of Foundation Software for Software-Defined Vehicle L. Hendrawan, R. Martin, L. Abdelkader L. Hendrawan, R. Martin, L. Abdelkader 133–140
- 141–150 Managing the complexity of joint steering, braking and powertrain coordination in emerging vehicle E/E architectures N. Haegele N. Haegele 141–150
151–156 Software Open Source 151–156
- AUTOSAR and SOAFEE as Part of the SDV Alliance: Unifying the Software Defined Vehicle Ecosystem M. Niklas-Höret, B. Rill M. Niklas-Höret, B. Rill
157–220 Software Cloud, Connect & Rust 157–220
- 157–166 Automotive Vehicle Connectivity 2030 G. Schmitt, F. Fitzek, M. Gruffke G. Schmitt, F. Fitzek, M. Gruffke 157–166
- 167–178 LightOpen – a cloud-based lighting personalization service M. Peter M. Peter 167–178
- 179–212 Bring TSN cloud native support to SDV architectures F. Ozog F. Ozog 179–212
- 213–220 Rust integration based on interoperability in legacy software P. Faymonville, C. Schwager P. Faymonville, C. Schwager 213–220
221–264 Processes SDV 221–264
- 221–232 SpecBook-Copilot – Efficient Formalization of Requirements using Artificial Intelligence in the Development of MB.OS M. Obstbaum, I. Wior, J. Kasper, P. Vaudrevange, S. Khan, M. Keckeisen, M. Staib, R. Freitag, J. Schneider M. Obstbaum, I. Wior, J. Kasper, P. Vaudrevange, S. Khan, M. Keckeisen, M. Staib, R. Freitag, J. Schneider 221–232
- 233–242 Using Simulation in the Development of V2X Applications CarMaker V2X Interface and Local Hazard Warning V. Lizenberg, F. Specka, J. Hauenstein, M. Mayer V. Lizenberg, F. Specka, J. Hauenstein, M. Mayer 233–242
- 243–256 Testing Variant-Rich Software-Defined Mobility Systems Methods, Future Challenges and Innovative Concepts L. Hettich, J. Stümpfle, M. Weyrich L. Hettich, J. Stümpfle, M. Weyrich 243–256
- 257–264 Optimizing Electronics Architecture for the deployment of Convolution Neural Networks using System-Level Modeling D. Shankar, T. Jose D. Shankar, T. Jose 257–264
265–302 Automated Driving 265–302
- 265–280 Using Large Language Models to generate critical driving situations for virtual and hybrid ADAS/AD testing L. Eisemann, L. Toettel, T. Watzl, T. K. Rupp, J. Schaper L. Eisemann, L. Toettel, T. Watzl, T. K. Rupp, J. Schaper 265–280
- 281–292 Ensuring ADAS functionality during periodic technical inspection ADAS/AD functionalities over a vehicle’s lifetim T. Ost, D. Petanjek, M. Beer T. Ost, D. Petanjek, M. Beer 281–292
- 293–298 Ensuring high reliability inside fail-operational systems Key prerequisite for SAE L3->L5 compliant automated driving L. Badescu L. Badescu 293–298
- 299–302 Importance of CATR technology in testing 4D imaging radars A. Jain A. Jain 299–302
303–312 Transformation of Working 303–312
- Collaborate with Chinese Partners to Navigate the SDV Transformation A. Wang A. Wang
313–358 Cockpit & Customer Experience In-Cabin 313–358
- 313–326 Immersive In-car AR Live Gaming Enabled by SDV Architecture, ADAS Cameras & AI Software P. Reilhac, C. Nowakowski, A. Manila, J. Almeida P. Reilhac, C. Nowakowski, A. Manila, J. Almeida 313–326
- 327–342 Biometrics and sensor fusion for enhanced in-cabin safety and comfort Reducing complexity and increasing possibilities through a holistic approach to in-cabin monitoring W. Steinmann, B. Sondermann W. Steinmann, B. Sondermann 327–342
- 343–358 Leveraging AI/ML Techniques in Software Defined Architecture: Towards Emotional Quotient Prediction in Smart Automotive Cabins by Integrating Physiological and Vehicle Data. AI-driven Smart Cabin in S... S Gowrishankar, KV Muralidhara, M. Ghivari S Gowrishankar, KV Muralidhara, M. Ghivari 343–358
359–394 Cockpit & Customer Experience Ecosystems 359–394
- 359–378 Generative AI based GUI reconfiguration using Natural Language Processing Leveraging the benefits of small and local LLMs T. Schäfer, J. Kottig, D. Macke T. Schäfer, J. Kottig, D. Macke 359–378
- 379–394 Electric vehicles in 2024. Current UX challenges and concepts for the coming decade A. Bachmann, A. Matarage A. Bachmann, A. Matarage 379–394
395–428 Mobility System Architecture 395–428
- 395–406 When innovation demand meets E/E architecture Further endeavours into next-gen architectural designs T. Huck, A. Achtzehn T. Huck, A. Achtzehn 395–406
- 407–418 Managing Reuse and Dependencies of Hardware and Software Components in SDV Architectures F. Schreiner F. Schreiner 407–418
- 419–428 Transition from Domain to Zonal Network Architecture for Software Defined Vehicles (SDV) F. Ottofuelling F. Ottofuelling 419–428
429–466 Processes/Virtual, Simulation, Requirements 429–466
- 429–436 Dead in 100 ms. Responsive (customer) functions require well-designed event chains and excellent timing requirements O. Schmidt, R. Münzenberger, M. Glück O. Schmidt, R. Münzenberger, M. Glück 429–436
- 437–452 A New Era for Software Verification: Heterogeneous Multicore Compute With Model-Based Design and Virtual ECUs K. Brand, T. Tang, D. Selvaraj K. Brand, T. Tang, D. Selvaraj 437–452
- 453–466 From Reality to Simulation: Automatized Transfer and Simulation of Critical Driving Scenarios with Digital Twins N. Neis, L. Eisemann, D. Hermann, J. Zhou, L. Toettel N. Neis, L. Eisemann, D. Hermann, J. Zhou, L. Toettel 453–466
467–482 E-Vehicle Mobility Vehicle Range 467–482
- 467–476 Battery-Integrated Multilevel Inverter Technology A Highly Integrated Electric Drivetrain Approach and its Technical Implementation in a Distributed Real-Time System D. Simon, T. Wenka D. Simon, T. Wenka 467–476
- 477–482 Boosting vehicle range by mating semiconductor technologies M. Münzer, S. Zhang, C. Bauer, D. Graovac M. Münzer, S. Zhang, C. Bauer, D. Graovac 477–482
483–516 E-Vehicle Mobility Charging 483–516
- 483–490 Integration of chargers and the grid How to Improve the Charging Experience of Your Customers by Better Integration with the Electricity Grid T. van Wijk T. van Wijk 483–490
- 491–504 Mapping the Future Role of Electric Vehicles as Energy Storage Systems: A Comprehensive Study on Current Market Trends and Future Projections for AC and DC Bidirectional Charging Detailed Review of Ma... F. Cigarini F. Cigarini 491–504
- 505–516 Advances in Electric Vehicle Charging Mapping between User Needs and Technology M. Stapelbroek, R. Savelsberg, M. Wegener, M. Faßbender M. Stapelbroek, R. Savelsberg, M. Wegener, M. Faßbender 505–516
517–540 Electronics Technologies 517–540
- 517–528 Automotive eFuses: Challenges of Today and Solutions for the Future C. Lankeit, R. Dorociak, M. Teuber, O. Lüdtke, J. Ziethen C. Lankeit, R. Dorociak, M. Teuber, O. Lüdtke, J. Ziethen 517–528
- 529–540 Enabling an Open Eco-System for Chiplet based Automotive SoCs Chiplets are the future for automotive SoCs and Road towards first Generations M. Schaffert M. Schaffert 529–540
541–564 Security TARA & More 541–564
- 541–552 TARAs Performed on Different Levels of the Supply Chain – Experiences Based on Real Example ESLF Insights into Applying ISO/SAE 21434:2021 in Automotive Cybersecurity T. Liedtke, R. Messnarz T. Liedtke, R. Messnarz 541–552
- 553–564 Efficiency in UNECE R155. Type Approvals for Small OEMS – Lessons Learned F. Langner, W. McCaig, T. Nilges F. Langner, W. McCaig, T. Nilges 553–564
565–XVI Security/AI 565–XVI
- 565–578 Recommendations for the practical use of Ethernet Security-Protocols and beyond S. Schran, R. Jung, A. Mukherji S. Schran, R. Jung, A. Mukherji 565–578
- 579–XVI Contribution of Artificial Intelligence in Automotive Cyber Security Management System More Cybersecurity through Artificial Intelligence U. Akcakoca U. Akcakoca 579–XVI

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