From Seeing Clearly to Understanding: LLMs and Security Agents at Security China 2026

For the first time, large AI models and public-safety agents became the headline narrative at the expo. For in-vehicle devices, intelligence is shifting from recognizing targets to understanding scenes.

Over the past decade, in-vehicle AI has mostly been single-task models: recognizing a face, reading a license plate, detecting a lane line. The ceiling of such perception is obvious — it knows there is a person there, but cannot explain what happened and whether an alert is warranted.

The Fact

Security China 2026 listed AI+ as its top highlight. Huawei, Huawei Terminal, China Telecom, Tiandy, Vimicro, Fullhan, VSigma and Goke Microelectronics showcased security industry large models, public-safety-specific agents, and multimodal perception algorithms. Officials framed security as the most representative industry where AI has landed earliest and most broadly, moving from deep-learning-driven perceptual intelligence toward large-model-driven cognitive intelligence. The keyword is driving large-model technology toward scaled real-world deployment.

Our Take

What large models bring is not another recognition model but multimodal scene understanding. If a vehicle unit can simultaneously understand the composite context of a front accident, right-side illegal parking and a distracted driver, and proactively suggest a response, the benchmark for in-vehicle surveillance should upgrade from recognition accuracy to scene comprehension. This directly rewrites the product definition of an MDVR: from a recording box into a thinking on-board safety hub.

But we must stay clear-eyed: the compute, power and disconnected environments of in-vehicle scenarios mean it will not be a simple lift of cloud large models, but distilled and quantized on-device small models plus cloud collaboration when needed. Whoever can compress the large model into automotive-grade hardware earns the ticket to the next leg.