As automotive safety standards evolve, in-cabin sensing is an increasingly critical component of vehicle design. “Augmenting In-Cabin Radar Through Artificial Intelligence” explores how AI enables radar technology to transform occupant monitoring systems. Unlike traditional camera-based solutions, radar offers superior performance in low-light conditions, sees through obstructions like blankets, and ensures privacy by generating non-identifying data. These advantages theoretically make radar ideal for applications such as child presence detection, vital signs monitoring, and driver awareness. The challenge with radar is making sense of “noisy” or minimal data scenarios in a way that ensures consistent system accuracy.
This session will delve into the technical pipeline from raw radar point clouds to intelligent seat zone classification using lightweight neural networks. Attendees will gain insights into an end-to-end radar + AI stack and learn how to optimize for real-time edge deployment on embedded platforms. The session addresses real-world challenges such as radar noise, class imbalance, and deployment constraints, offering practical lessons learned for developing a production-ready solution.