May 13 – 15, 2025 | Frankfurt/Hanau, Germany

Auto[nom]Mobil

May 14 – 15, 2025

Here you see a recap of the program of the Auto[nom]Mobil 2024. As soon as the program of the 2025 conference is finalized, you will find it here.

Wednesday, May 15, 2024

Intro
Prof. Klaus Kompaß - KKo4Safety, formerly BMW Group; Direktor & Professor Andre Seeck - BASt - German Federal Highway Research Institute; Dr.-Ing. Michael Fausten - Robert Bosch GmbH

Keynote: The Human Factor in Automated Transport
Dr. Jan Grippenkoven - German Aerospace Center (DLR) – Institute of Transport Research
  • What do people actually need? – Purposes of automated driving
  • Perceived Safety and Acceptance
  • Influence of societal trends on traffic innovations
  • Application areas: Passenger transportation and logistics
  • What else is needed: Framework conditions for the establishment of automated driving
Keynote: From Trusted Technologies to Trustworthy Companies
Prof. Bryant Walker Smith - University of South Carolina

"Does the public trust the technology?" is a familiar but unhelpful question in discussions about automated vehicles specifically and innovation generally. I shift this inquiry to instead ask whether "the companies behind a given technology are worthy of the public's trust." To do this, I propose an affirmative theory of "the trustworthy company," identify conduct that signals a lack of trustworthiness, and describe how existing legal doctrines can be reconceived as trust-based duties. Driving automation serves as my primary case study throughout.

Teleoperation

Chair: Tom Michael Gasser - BASt - German Federal Highway Research Institute
Research Needs in Teleoperation
Dr. Alexander Frey - BASt - German Federal Highway Research Institute
  • BASt-Working Group on Research Needs in Teleoperation
  • New control mode for vehicles
  • Definitions and use cases
  • 5 Clusters of research topics
Interaction Concepts for Event-driven and Permanent Teleoperation
Dr. Frank Diermeyer - Technical University of Munich
  • Use cases for permanent and event-driven teleoperation
  • Safety aspects and considerations
  • Teleoperation concepts in research and development
Teleoperation - Recent Activities in Standardization Same Title - Different Viewpoints
Prof. Dr. phil. Klaus Bengler - Technische Universität München
  • International teleoperation activities
  • Functional development perspective
  • Human Factors perspective
  • Need for exchange
Level 4 Autonomous Driving and Technical Supervision - Interaction Concept for Dynamic Driving Tasks
Dr. Johannes Springer - T-Systems International GmbH
  • Fundamentals for the interaction between Automated Vehicles and Technical Supervision
  • Technical Concept of Safety Corridors Dynamics of Driving and requirements for indirect teleoperated driving of Technical Supervisors
  • HMI for Technical Supervision in Dynamic Driving Tasks
  • Functional Safety Constraints
  • Relevant 5G capabilities

Focus China

Chair: Dr.-Ing. Michael Fausten - Robert Bosch GmbH
Momenta- The Path From Highly Automated to Self-Driving
Gerhard Steiger - Momenta Europe GmbH

Scalability is one of the key challenge in autonomous driving to solve the long tail problem. For this, an AD system needs to be compatible with various vehicle architectures, different sensor combinations, and operating systems. Momenta with its unique technical approach, is capable of delivering efficient AD solutions for vehicles that can be scaled up across OEMs and international customer projects.
L4 Autonomous Driving Midst Transformations
Dr. Yong Gessner - WeRide
  • Prevailing transformations in the automotive industry
  • L4 autonomous driving status quo  
  • Ecosystems perspective
Wrap Up Day 1
Dr.-Ing. Michael Fausten - Robert Bosch GmbH; Prof. Klaus Kompaß - KKo4Safety, formerly BMW Group; Prof. Dr.-Ing. Thomas Helmer - Technical University of Applied Sciences Ingolstadt

Thursday, May 16, 2024

Validation and Verification of Automated Driving

Chair: Dr.-Ing. Christian Gold - BMW Group
The 4 Pillars of Validation and Type Approval of Automated Driving Functions
Prof. Dr.-Ing. Günther Prokop - Technische Universität Dresden; Dr. Jürgen Bönninger - FSD Fahrzeugsystemdaten GmbH
  • Methodology
  • Infrastructure for test and simulation
  • Networked process for data gathering
  • Rulemaking and worldwide harmonization
Validation Approach at Torc
Dushyant Wadivkar - Torc Robotics, Inc.
  • Solvability of the validation challenge of open world by describing the methods we are using and how they interact.
  • Challenges with exposure-based evaluation and their advantages.
Safety Model to Verify the Road Safety of Autonomous Vehicles
Dr. Andreas Zeller - Robert Bosch GmbH
  • Context and objectives of security models
  • Formalization of rules
  • Data-driven identification of suitable model parameters
  • Concrete application example on L4 data
Hard- and Software Safety Considerations of a Steer-by-Wire-System
Dr. rer. nat. Maximilian Kühn - Mercedes Benz AG
  • Replacing the steering wheel shaft with a wire: Advantages and new possibilities for Autonomous Driving
  • Challenges to overcome for Steer-by-Wire-Systems
  • Possible system architectures: Advantages and Disadvantages
  • Functionality and safety considerations of the components of a distributed Steer-by-Wire-System
  • How to handle redundancy on ECU-Hardware level
  • Towards a “Fail Operational” software architecture and design
  • Including external vehicle system for lateral control in fallback scenarios
  • Parallels of Steer-by-Wire and Autonomous-Driving-Systems

Automated Driving and AI

Chair: Reinhard Böswirth - Magna Electronics
Deep Learning Meets Safety
Dr. rer. nat. Shervin Raafatnia - Robert Bosch GmbH
  • What is a safety concern?
  • Safety concerns regarding deep learning
  • Example of a mitigation approach: Pixel-wise out-of-distribution detection
Unveiling Safety Assurance Cases
Sascha Hackmann - INVENSITY GmbH
  • Presenting an approach to create a Safety Assurance Case for self-driving vehicles that employ Machine Learning algorithms, aiming to compile a robust safety argument.
  • This approach goes beyond the standard safety requirements outlined in ISO 26262, ensuring that the intelligent learning systems are both reliable and consistent.
  • The Safety Assurance Case method is designed to demonstrate that AI-driven features have been thoroughly investigated, thus fostering trust and responsibility as we advance towards the future of automated driving.
Demystifying AI Challenges
Dr. Marc Großerüschkamp - INVENSITY GmbH
  • A brief overview of the way AI learns and the use of AI models. Clarification of common misconceptions regarding the challenges of deploying Artificial Intelligence (AI).
  • Explanation of example applications for the use of AI in Automated Driving.
  • Illustration of specific challenges in AI development that make the deployment of Artificial Intelligence challenging.
  • Presentation of the role that Explainable AI (XAI) plays as a key technology for improving the transparency and traceability of Artificial Intelligence.
  • Overview of XAI methods and their application possibilities using examples.
Uncertainty Quantifications With Guarantees Using Uncertainty Wrappers
Dr. Michael Kläs - Fraunhofer IESE

In this talk, Dr. Michael Kläs, AI safety expert at Fraunhofer IESE, will give an overview of uncertainty quantification with probabilistic guarantees in ML models. The talk will deal in particular with uncertainty wrappers and their application in perception. In addition to conveying the basic features of this model-agnostic approach and its place in the world of uncertainty quantification, it will provide insight into current research work. This includes the application of uncertainty wrappers in uncertainty fusion for autocorrelated image series as well as their differences and combinability with conformal predictions. Uncertainty wrappers, which are also explicitly mentioned in the recently published DIN SPEC 92005 (Artificial Intelligence – Uncertainty Quantification in ML), offer a model-agnostic approach for reliable uncertainty estimates with probabilistic guarantees. Moreover, they differ from many other approaches to uncertainty quantification in ML due to their transparency and holistic consideration of various sources of uncertainty.

Validation and Verification of ADAS

Chair: Prof. Dr.-Ing. Thomas Helmer - Technical University of Applied Sciences Ingolstadt
Field Effectiveness of Driver Assistance Systems
Patrick Semrau - BMW AG
  • Research question, method and results of a broad field effectiveness study
  • Fokus on effectiveness of  AEB (Automatic Emergency Braking) with FCW (Forward Collision Warning)
  • Based on US field data in frontal collisions
Addressing Potential Challenges for Hands-free Monitoring of Level 2 Functions
Dr. phil. Johanna Josten - fka GmbH
  • Hands-free monitoring of Level 2 functions has been associated with challenges such as prolonged transition times.
  • The L2H-off project provides a data basis to assess potentially adverse interaction behavior.
  • Results from the field tests and simulator studies are used to derive recommendations for hands-free monitoring.
Wrap Up and Fare Well
Prof. Klaus Kompaß - KKo4Safety, formerly BMW Group; Dr.-Ing. Michael Fausten - Robert Bosch GmbH

 

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