How AI Transforms Freight Safety: Real-Time Risk Detection and Intervention

Introduction

Safety is a fundamental concern for everyone, and any small oversight can lead to serious accidents. In the freight industry, ensuring the safety of personnel and cargo is a basic responsibility. With rapid growth in demand and business volume, freight safety faces increasing challenges, such as passenger‑in‑cargo, prohibited goods transport, dangerous driving, and in‑vehicle conflicts. AI offers new solutions for freight safety prevention.

Implementation Challenges

Traditional safety control methods struggle to detect and intervene promptly in internet‑based freight scenarios, lacking pre‑ and in‑process control capabilities, leading platforms to rely on post‑incident handling. Real‑time monitoring, identification, and intervention across the entire order chain face several challenges.

AI‑Based Prevention Approach

By integrating AI recognition technology, the Huolala AI safety control system monitors, identifies, and intervenes in real time for passenger‑in‑cargo risks, shifting safety management from passive post‑incident mitigation to proactive pre‑ and in‑process prevention, while meeting governmental responsibility requirements.

The AI safety control follows three stages: pre‑incident, during‑incident, and post‑incident governance.

AI Safety Control System

To manage passenger‑in‑cargo orders, a self‑developed AI safety control system was built to achieve real‑time monitoring and intervention for massive platform orders.

Overall architecture:

Data Perception

Raw data is collected, desensitized, cleaned, and features are extracted. The data used by the system is licensed and not permanently stored, with multiple measures to ensure user privacy and security.

During data cleaning, sensitive fields are encrypted and stored, and processed in real‑time streaming tasks using Flink.

Large models, computer‑vision (CV) algorithms, and NLP algorithms extract passenger‑in‑cargo feature data from cleaned and desensitized data.

Risk Detection

Analyze risk feature data to assess order risk. Based on extracted passenger‑in‑cargo risk features, a strategy + rule engine further analyzes data features and evaluates risk levels. Multi‑feature combination analysis improves accuracy and reduces false positives.

Intervention

Real‑time tiered intervention is applied to orders with passenger‑in‑cargo risk, linked with the customer‑service system. When drivers dispute a cancelled order, explanations are provided to improve understanding and acceptance. Confirmed risky orders are penalized for drivers/users.

High‑confidence orders: strong intervention, intercept or force cancel.

Medium‑confidence orders: strong reminder via pop‑up alerts.

Low‑confidence orders: weak reminder through safety center, voice prompts, or AI outbound calls.

Conclusion

The Huolala AI safety control system achieves millisecond‑level identification and management of illegal passenger orders, with a detection accuracy exceeding 95%, significantly improving safety levels. Future advancements will make the system more intelligent and automated, providing strong support for the logistics industry's development.