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Cases

  • Application of Integrated AI Fatigue and Distraction Detection System in Mining Fleet Safety Management
    This project deployed an advanced safety management system for a large mining fleet in Asia, which effectively reduced the risk of driver fatigue and distraction by integrating a fatigue detection system (DMS) and a GPS tracker (MRVL). The system uses AI to monitor driving behavior (such as drowsiness and distraction) in real time, triggering sharp alarms in the cab, and transmitting alarm signals, positioning data and on-site pictures to the fleet management platform.
    2025/4/16
  • Anti-Fatigue Safety Management For Hazardous Chemicals Transportation Fleet Drivers
    When the system detects driver fatigue or distracted driving, the driver's cabin will trigger a sound alarm and seat vibration simultaneously. At the same time, the alarm signal, positioning data and driver status pictures will be uploaded to the management backend to assist managers in formulating targeted safety strategies and significantly reduce the accident rate.
    2025/4/16
  • Australia's Top Transport Operator Case Study
    1. AI-driven risk prioritization: Upgraded algorithms to sort alarms by severity (e.g., prolonged fatigue vs. brief distraction) to enable prioritized incident response.
    2. Driver feedback loop: Added a post-alert in-cab interface for drivers to self-report fatigue levels, improving the accuracy of behavioral data.
    3. Predictive maintenance integration: Correlate vehicle telemetry (e.g., braking patterns) with collision alarms to identify mechanical risks in advance.
    2025/4/14

Caring All Ways Safely

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