Event Recap | Carlinx Senior Product Director Lu Xiwen Explains the Synergy Between Data Acquisition and Intelligent Analysis

When smart vehicles generate massive amounts of data every day, why is truly valuable data still so scarce?

At the “Equipment Power – Entering Changan” event held on May 20, 2026, Lu Xiwen, Product Director of Carlinx, delivered a presentation on the theme of “vehicle-cloud data closed loop.” He systematically explained how Carlinx achieves synergy between high‑precision data acquisition and intelligent analysis to create a complete data pipeline – from collection and flow to value feedback.

In response to the challenges of traditional data acquisition – high cost, heavy transmission pipelines, and low value utilization – Carlinx proposed an integrated solution of “precision collection + data flow + closed‑loop reasoning.” The core objective is simple: make data truly “accessible, understandable, and actionable.”

The following is excerpted from his speech:

As vehicle E/E architectures grow increasingly complex and the number of ECUs continues to rise, passenger‑centric AI‑driven scenario services and immersive experiences are constantly expanding. The amount of data generated by vehicles has exploded. However, truly high‑value data that can support R&D iteration, maintenance diagnostics, and AI training remains severely insufficient. The core contradiction is not a lack of data, but an inability to move data efficiently and use it properly.

Key pain points of traditional data collection:

• High cost of coarse data acquisition – Heavy collection pipelines, slow adjustments, and lack of high compression ratios waste large amounts of resources and bandwidth, while also failing to support flexible configuration and upgrades based on business needs.

• Poor accuracy and real‑time performance of data backhaul – Original pipelines were not designed for high‑quality acquisition and struggle to maintain quality under high‑concurrency data loads, leading to high latency, packet loss, and congestion.

• Disconnection between data closed loop and business scenarios – High‑precision full‑scale acquisition remains difficult, and even collected data is often hard to feed into downstream diagnostics and analysis, making it impossible for massive data to effectively support business applications.

Carlinx’s solution paradigm is to build a flexible data acquisition platform that creates a “highway” for vehicle data. At the two ends of this highway, the vehicle side performs efficient collection of multi‑source data (body signals, logged signals, etc.), while the cloud side hosts diagnostic analysis and model training. Through cross‑domain collaboration, key capabilities such as model deployment, edge computing, and scenario engines are realized, forming a complete closed loop from data collection to intelligent analysis.

In actual deployments, Carlinx has already validated the commercial value of high‑precision data acquisition. Take the collision detection model as an example – this solution was mass‑produced and deployed at a leading OEM last year, generating approximately RMB 500 million in incremental revenue within one year. Compared to traditional approaches, light and moderate collision detection require extremely high data precision – real‑time collection at intervals of 200‑300 milliseconds, which has long been an industry difficulty. Through the synergy of high‑precision real‑time acquisition and intelligent analysis, Carlinx has achieved accurate detection under complex scenarios, providing key support for automakers to improve service efficiency and business value.

This event at Changan marked another step in Carlinx’s in‑depth dialogue with industry chain partners. Going forward, Carlinx will continue to strengthen the foundational capabilities of vehicle‑cloud data infrastructure and work with more industry partners to explore new data‑driven R&D, diagnostic, and service systems. The goal is to ensure that data is no longer merely “collected” but becomes a core productivity that enables the continuous evolution of smart vehicles.