Proximity Detection and Collision Avoidance Alert Automation

This workflow automates the critical site-safety bottleneck of manually monitoring personnel and asset proximity around heavy machinery like cranes and excavators. By fusing real-time data from RFID tags, radar, and on-board cameras, it detects hazardous encroachment with sub-second latency. The operational upside is a direct reduction in high-cost collision incidents, insurance claims, and project delays, while creating a defensible, automated audit trail for safety compliance across the entire jobsite.

Implementation requires deploying ruggedized edge compute units on machinery to run sensor fusion models, ensuring low-latency response. The orchestrator, built with frameworks like LangGraph, manages alert priority, integrates with machine CAN bus for automated slowdown via APIs, and routes incident data to systems like SAP or Procore for reporting. Critical controls include human-in-the-loop overrides for automatic stops, geofencing rules, and staged rollout to validate detection accuracy before full site deployment.

This workflow automates the detection of personnel and asset proximity hazards by orchestrating data from RFID tags, radar, and on-board cameras. It eliminates the latency and blind spots of manual spotter systems, directly reducing collision risk, insurance premiums, and project delays from safety incidents. The operational upside comes from continuous, 360-degree monitoring that enables real-time intervention, protecting both human safety and high-value machinery from costly impacts.

Implementation requires deploying edge compute nodes on machinery to run sensor fusion and low-latency risk models, ensuring sub-second alerting. The architecture integrates via APIs with machine control systems for automatic slowdown and with safety platforms like Procore or SAP EHS for audit-ready reporting. Critical controls include human-override mechanisms, alert severity thresholds, and regular simulation testing to validate system response under diverse site conditions before full rollout.

This workflow directly mitigates the high-cost risks of jobsite collisions, which cause injury, equipment damage, and project delays. By automating real-time hazard detection, it eliminates reliance on sporadic human vigilance. The operational upside comes from preventing catastrophic incidents, reducing insurance premiums, and maintaining uninterrupted project velocity. Implementation requires integrating edge-processing units on machinery with site-wide sensor networks, feeding a central safety orchestrator that manages alert logic and machine control interfaces.

Implementation centers on a containerized orchestrator, likely built with LangGraph or a custom microservice, deployed on a site server or cloud edge. It ingests normalized sensor streams via MQTT or gRPC, executes rule-based and ML models to classify hazard severity, and triggers actions through APIs to machine ECUs and safety platforms like Intelex. Critical controls include human-in-the-loop overrides for automatic stops, geofencing to adjust sensitivity by zone, and rigorous data validation to prevent false positives that halt productivity. Rollout requires phased sensor calibration and operator training.

Rollout begins with a pilot on a single, high-risk machine like a crane or excavator, integrating RFID, radar, and camera feeds into a dedicated edge processor. This isolated phase validates sensor fusion accuracy, alert latency, and integration with the machine's control system for automatic slowdown. Governance mandates a human-in-the-loop review for all initial alerts, with logs fed to a central safety dashboard for analysis. This controlled start mitigates risk while building the data foundation for fleet-wide scaling.

Full-scale deployment follows a site-by-site schedule, coordinated with project timelines to avoid disruption. Governance is enforced through immutable audit trails linking every alert to sensor data, control actions, and reviewer decisions. Continuous monitoring tracks false-positive rates, system uptime, and near-miss trends, triggering model retraining or rule adjustments. This structured approach ensures the automation enhances safety without introducing new operational or liability risks, creating a defensible, continuously improving safety layer.

This workflow automates the detection of personnel and asset proximity hazards by fusing data from RFID tags, radar, and on-board cameras. It directly addresses the operational bottleneck of manual spotter reliance and delayed reaction times, which are primary contributors to costly collisions and safety incidents. The business value comes from reducing downtime due to accidents, lowering insurance premiums through improved safety records, and protecting project schedules by preventing catastrophic equipment damage and associated investigations.

Implementation requires integrating the edge processor's API with the machine's CAN bus or PLC via a secure gateway to enact automatic slowdowns. Critical controls include configurable alert thresholds, a mandatory human review gate for high-severity incidents before logging, and real-time dashboard visibility for site supervisors. The architecture must support rollback capabilities and continuous monitoring of sensor health to ensure the system's reliability does not become a single point of failure in safety-critical operations.