Auto[nom]Mobil

WelcomeTalk

Keynotes

Automated Driving - Why, How and When?

• Use cases in trucking and public transportation
• Further research
• Technological sovereignty

Market and Strategy

Life Cycle Compliance of AV

Environmental Perception

Unlocking Collective Awareness for ADAS

• V2X as a sensor: Enhancing perception beyond existing sensor limitations, and supporting real-time decision-making
• Challenges of trustworthiness and V2X technology fragmentation
• Integration considerations for Connected ADAS
• Opportunities for scalable, adaptive architectures to support safety relevant applications

Validation

5 Years on the Road  -  Lessons Learned from Testing AD Systems

• Public road AD testing history- a recap
• Lessons learned from a multi project testing campaign of public road AD testing
• A non-OEM perspective on the challenges of approval for testing
• The importance of safety drivers
• Outlook and today’s boundary conditions

Mixed Reality as a Next Generation Development Tool

• Description Mixed Reality as a development and validation method
• Pros and cons of the method
• BMW concept of a mixed reality vehicle
• Prospect of next steps and further research

Podium Discussion

Homologation

How AI Influences the Validation and Homologation of ADAS and AF from the Perspective of International Standardisation

From Development to Production: How to ensure compliance and type approval of an autonomous eco-system

• Vision of autonomous on-demand mobility services
• Digitisation of the human driver
• Regulatory hurdles and challenges
• Way forward

Test and Validation

Continuous XiL Test Pipeline for the Validation of ADAS Software Updates

• Overview of various real and virtual test environments for ADAS
• The challenge of simulating a complete sensor set
• Generating and feeding synthetic sensor and bus data in real time
• Advantages of using continuous XiL simulation in practice

Statistical Validation of the BMW Personal Pilot L3

• Comprehensive safety framework that guided the development and regulatory approval of BMW's first SAE Level 3 Automated Driving System
• Integration of Systems Engineering, Risk Analysis, and advanced methods from Statistical Learning
• Systematic identification and quantification of uncertainties in hazard scenarios, ensuring a Positive Risk Balance
• Alignment with automotive standards, providing a rigorous and transparent safety assurance process for Automated Driving Systems

Validation of Level 3 and 4 Functions: Challenges and Possible Solutions

• What are the biggest challenges in validating automated driving functions
• What data volume and test depth do we need?
• What methods and disciplines do we need to solve the validation problem?