Advances in in-cabin sensing are transforming vehicle safety, comfort, and user experience. This presentation explores a next generation sensing architecture that integrates sensor fusion, on device machine learning, and energy efficient system design to deliver robust, real time occupant monitoring. By combining complementary modalities sensor fusion enhances environmental understanding and significantly improves resilience against challenging conditions like low light, occlusions, and complex occupant postures. Deploying machine learning on the edge enables ultra low latency decision making. Additionally, the talk highlights new techniques for energy optimization, including adaptive sensor activation, dynamic model scaling, and efficient hardware software co design—crucial for electric vehicles and architectures with constrained power budgets. Together, these innovations outline a scalable, high performance approach to in cabin sensing that supports emerging applications such as child presence detection, driver state monitoring, personalized comfort features, and regulatory compliance.
