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Hybrid Workshop


Tuesday, 11 October 2022


Is AI a dependable technology for autonomous driving? What are the limits of AI-based components in autonomous vehicles? What about sensors and communication? What about validation of L4 autonomous vehicles?


Autonomous cars are said to be the most complex cognitive systems ever built by humans because they must have a deep understanding of their surroundings and conditions in order to accomplish the complex task of driving.


Dependability is the ability to deliver service that can justifiably be trusted. Safety means the absence of catastrophic consequences on the users and the environment. However, dependability is an integrating concept that comprises not only safety, but also availability, reliability, integrity, security, and more. Trust can be defined as accepted dependability.


AI techniques promise to add more human-like cognitive intelligence needed to automate the driving task and are therefore regarded as the key enabler for L4 autonomous vehicles. However, AI algorithms, whether rule-based or relying on machine learning, depend on real-time data from sensors and communication links to recognize objects to identify ever-changing free spaces, path planning, and selection.


A connected and highly automated vehicle has to be considered as a networked end-to-end mobility system rather than a standalone vehicle. The former integrates functions and properties of various distributed subsystems during operation based on resources controlled by various stakeholders, from OEMs to road and network operators and all kinds of service providers. These dependencies impose even more challenges to dependability engineering and add more complexity to verification and validation.




This workshop brings together leading experts and decision makers from all over the globe, including the United States, Europe, and China, to share state-of-the-art solutions and discuss what is coming next. 


There are four sessions that will address some of the key industrial challenges in an effort to help L4 autonomous vehicles become a reality on public roads in the next few years.

  1. How to establish trust and specify accepted dependability?
  2. Communication and advanced automotive sensing for AI-based perception and environmental modeling
  3. AI components and their limitations
  4. Approaches for verification and validation of L4 autonomous vehicles to get ready for type approval and self-certification

This hybrid workshop is taking place both online and in-person at CAPGEMINI, Olof-Palme Str. 14; 81829 München in Munich, Germany. If you are interested in attending in-person, please reach out to Rosalinda Saravia at [email protected]




Key industry leaders from Europe, the United States, and Asia will share their views. The panel discussion with the audience will open up opportunities for attendees to debate the tech, business, and regulatory challenges while figuring out how standards-related activities can help to monetize technologies and to shape markets in order to facilitate the large-scale deployment of highly automated vehicles. Researchers are encouraged to share latest research results and innovators to come up with disruptive concepts.


  • Vehicle manufacturers and automotive suppliers
  • Autonomous vehicle developers
  • Software and internet companies
  • Semiconductor firms
  • Telecom operators
  • Road operators
  • Mobility service providers
  • Transportation infrastructure stakeholders
  • Industry alliances
  • Academics
  • Research institutions
  • Start-ups and Scale-ups


KEYNOTE | 09:05 - 09:30 CET

Welcome to Innovation Center

AI and Automotive

Speakers: Daniel Garschagen and Peter Fintl, CAPGEMINI


SESSION 1 | 09:30 - 11:00 CET

Trustworthiness: Public Trust in AI in Automotive Applications - What is Missing Still?

Moderator: Jürgen Neises


Topics and Speakers:

  • Ethics Criteria for Autonomous Vehicles | Ali G. Hessami, Chair, IEEE Systems Counsel UK & Ireland
  • A Principles-Based Ethical Assurance Argument for AI and Autonomous Systems | Zoe Porter/ Ibrahim Habli, University of York
  • Safety and Risk Aversion in Autonomous Driving Through Data Labeling | Alxander Wöllarth-Lauterburg

SESSION 2 | 11:15 - 13:15 CET

Sensors: Latest Developments - The Optimal Data Input for Fusion?

Moderator: Kasra Haghighi


Topics and Speakers:

  • Automotive Radars Advances, Testing Challenges and Standardization | Fahimeh Rafieinia, Uniqsec
  • Lidar Technologies - Latest Developments | Terje Noevig, Blickfeld GmbH
  • Automotive Camera Performance Indicators for AI Algorithms | Alexander Braun, University Düsseldorf
  • Build a Scalable Wireless Network System for Autonomous Vehicles | Lei Sun,

SESSION 3 | 14:00 - 16:00 CET

AI Technologies: The Key Technology - What are the Latest Developments?


Topics and Speakers:

  • Optimizing Battery Usage and Lifetime Using Data-Driven Modelling | Tijs Donkers, Eindhoven University of Technology
  • Momenta Flywheel: The Scalable Path Towards Full Autonomy | Qing Rao, Momenta
  • Scaling AI in Automotive with Hailo AI Acceleration | Yaniv Sulkes/ Jan Friso, Hailo
  • Standard for Functional Safety Data Format for Interoperability Within the Dependability Lifecycle | Jyotika Athavale, NVIDIA

SESSION 4 | 16:15 - 17:15 CET

Validation: Challenges and Solutions, Overall Validation/ Virtual and Physical Testing

Moderator: Hermann Brand


Topics and Speakers:

  • Challenges and Requirements of Real Environment ADAS Validation | Zsolt Szalay, Zalazone/ University Budapest
  • Compliance Platform for Safer Mobility | Daniel Gamber, Kontrol
  • Challenges and Requirements of ADAS Validation Frameworks with a Case Study on Surround View ADAS Application | Balvinder Khurana, NXP


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