From Radar to LLMs: A Journey Through Sensor Fusion and AI

Radar has been a cornerstone of perception systems for decades, providing reliable range and velocity measurements regardless of lighting conditions. But radar alone gives us only a partial view of the world—we can detect objects and measure their motion, but we can't understand what they are or what they're doing.

Enter large language models. While initially developed for text, their ability to understand and reason about complex relationships has proven surprisingly general. By combining radar's reliable physical measurements with LLMs' semantic understanding, we can build perception systems that are both robust and intelligent.

The fusion happens at multiple levels. At the lowest level, we use radar to provide reliable object detection and tracking. At the intermediate level, we combine radar with other sensor modalities—camera, lidar, IMU—to build a richer representation of the scene. And at the highest level, we use language models to reason about what's happening, to predict future states, and to make decisions.

This isn't just about adding more sensors or bigger models. It's about creating a unified representation that captures both the physical reality measured by sensors and the semantic understanding encoded in language. The radar tells us where things are; the LLM tells us what they mean.

The results are systems that can operate in challenging conditions—low light, bad weather, occlusions—while still maintaining a rich understanding of the scene. They can reason about intent, predict behavior, and make decisions that account for both physical constraints and semantic context.

As we move toward more autonomous systems, this fusion of traditional sensors and modern AI will become increasingly important. The future belongs to systems that can both see and understand.