The presentation will highlight the challenges and requirements in designing advanced driver-assistance systems (ADAS) for the future, emphasizing the need for expertise in systems, software, and specialized hardware algorithms to keep up with market pressures. In the context of sensing, a combination of various sensors such as cameras, LiDAR, radar, and thermal sensors is crucial for accurate classification and achieving higher levels of autonomy. Integrating all these sensors creates unprecedented data volumes that must be efficiently transported within and between semiconductor devices. We will discuss the elevated design challenges posed by artificial intelligence (AI) and machine learning (ML) applications in ADAS, including design complexity, power and thermal management, memory management, and integration and verification challenges. We will also outline the importance of efficient memory hierarchy, integration of multiple components, and exhaustive testing for ensuring the reliability and performance of AI/ML system-on-chips (SoCs). Finally, we will discuss the central role of network-on-chips (NoCs) in enabling the evolution of automotive electronic architectures, focusing on safety, security, and semiconductor development productivity as per ISO 26262 standards.