The integration of AI into automotive systems has become essential for meeting evolving customer expectations. It yet presents a unique set of challenges. These include, for example, down-scaling AI onto affordable embedded control units for the mass market segment as well as the requirement for large-scale access to high-quality, real-world automotive sensor data. This calls for modular end-to-end AI ADAS stacks, thus enabling superior performance and flexibility for customized ADAS features across the range of functional complexity. Integrating real-world knowledge from foundation models into ADAS stacks will further boost performance and efficiency. Finally, ensuring the safety and regulatory compliance of AI-based ADAS systems is another critical consideration.
