As the automotive industry accelerates towards autonomous, connected, electrified and shared mobility, the integration of AI and ML technologies is essential for achieving added value for virtually all customer facing products and services.

At BMW, we are at the forefront of this innovation, but the journey is fraught with complex challenges that must be overcome. This presentation will explore the critical hurdles in finding, inventing and deploying AI and ML end-to-end applications within the entire BMW’s vehicle program.

We will briefly delve into the intricacies of big-data processing, model accuracy, and real-world testing, offering insights into how BMW is addressing these challenges to pave the way for the future of mobility.

In this session, you will learn more about:

• Ensuring innovative AI and ML solutions with adequate model performance and safety across all relevant domains
• Organizing and managing complex, large-scale data for AI/ML applications
• Addressing innovative real-world deployment challenges and continuous model adaptation

• What are the key benefits and limitations of using digital twins in the testing and validation of ADAS?
• How do digital twins enhance the accuracy and efficiency of simulations for real-world driving scenarios, and what challenges remain in replicating complex environments?
• In what ways can digital twins contribute to the continuous learning and adaptation of AI models in AVs?
• What role do digital twins play in optimizing vehicle design and performance before physical prototypes are built?
• How can the automotive industry ensure the security and integrity of data within digital twin environments, especially when dealing with sensitive or proprietary information?