How Huolala’s AI‑Powered Safety Platform Transforms Freight Risk Management

1. Introduction

In the freight industry, a single accident can cause cargo loss and devastate families, making safety a matter of life and death. Traditional safety operations reacted only after incidents, essentially "fighting fires". Huolala decided to shift to an AI‑enabled approach that moves safety control to the pre‑ and mid‑event stages, aiming to extinguish risks before they occur.

2. Architecture Evolution

Since 2021, Huolala's safety system has iterated from a single AI safety model to a comprehensive safety governance platform. The platform now integrates multiple safety scenarios—such as illegal passenger transport, hazardous goods, and dangerous driving—through a modular, middle‑platform architecture that consolidates data perception, risk identification, and intervention.

3. Platform Design

The platform follows a three‑layer design:

Application Layer: Provides policy configuration, feature management, and data annotation tools for business users.

Engine Layer: Combines algorithm models and a governance engine to process massive data, identify risks, and trigger interventions.

Infrastructure Layer: Handles data transmission, system integration, and persistent storage to ensure data reliability.

Key design ideas include data cleaning, multimodal risk recognition (image, voice, NLP), and a rule‑based intervention engine that creates a closed loop from data input to risk mitigation.

4. Core Advantages & Value

Architecture Optimization: Re‑architected the three core modules for integrated capability and streamlined processes.

Data Fusion: Achieved full‑scope data integration, breaking data silos and building a low‑latency, low‑redundancy data pipeline.

Intelligent Decision‑Making: Established a strategy governance hub for lifecycle management, enabling dynamic policy configuration and automated distribution.

Precise Control: Supports layered intervention matching risk levels to avoid over‑defense.

Cost Reduction: Modular design and automation cut new‑scenario onboarding and maintenance costs significantly.

5. Project Outcomes

After deployment, the platform delivered:

85% faster onboarding of new safety scenarios.

50% reduction in maintenance cost and bug density per thousand lines of code.

30% improvement in data quality monitoring and business analysis efficiency.

6. Conclusion

Over the past year, the R&D team has continuously refined the safety governance middle‑platform, expanding coverage to more risk scenarios and establishing a robust safety barrier for drivers, families, and society.