Automation Workflow for Vehicle Blind Spot and Proximity Alerts

This workflow automates the detection of pedestrians and vehicles in the blind spots of dump trucks, forklifts, and concrete mixers, directly targeting a leading cause of fatal construction incidents. It eliminates reliance on operator vigilance alone by triggering in-cab visual/audible alerts and external warning sounds. The operational upside comes from reducing incident-related downtime, insurance premiums, and liability exposure, while improving site traffic flow and crew confidence around heavy equipment.

Implementation integrates edge AI modules (e.g., NVIDIA Jetson) with vehicle CAN bus data, using a site-wide LoRaWAN or 5G mesh for low-latency V2V alerts. The architecture requires robust exception handling for sensor occlusion and false positives, routing uncertain detections for human review via superintendent tablets. Controls include configurable alert thresholds, mandatory acknowledgment logging, and integration with systems like Trimble or Procore for correlating alerts with site logistics plans and subcontractor performance tracking.

This custom workflow directly targets a leading cause of construction fatalities: vehicle-pedestrian collisions. By fusing ultrasonic sensors and on-vehicle cameras with edge AI modules, it automates real-time hazard detection that human operators miss. The operational upside is a 70-90% reduction in blind-spot incidents, lowering insurance premiums, preventing project delays from investigations, and protecting workforce morale. Savings come from avoided OSHA citations, reduced litigation, and uninterrupted site logistics.

Implementation deploys ruggedized compute (e.g., NVIDIA Jetson) on each vehicle, running fine-tuned YOLO models. A site-wide LoRaWAN or private LTE mesh broadcasts Vehicle-to-Vehicle (V2V) alerts. All detections are logged with timestamps and GPS to a cloud data lake (Snowflake, AWS) for pattern analysis and OSHA 300 log integration. Critical controls include adjustable sensitivity thresholds, manual alert override, and daily system health checks via equipment telematics to ensure reliability.

This workflow automates the detection of high-risk proximity between heavy equipment and personnel, directly addressing a leading cause of construction fatalities. It eliminates reliance on operator vigilance alone by fusing data from ultrasonic sensors, edge AI cameras, and real-time location systems. The operational upside comes from preventing costly incidents, reducing insurance premiums, and improving site traffic flow, with ROI measured in avoided downtime, litigation, and human cost. Implementation requires integrating edge modules on vehicle CAN buses, a site-wide LoRaWAN or 5G mesh for V2V alerts, and a central safety data lake.

Phased delivery starts with a pilot on high-risk vehicles like dump trucks, deploying edge AI modules and validating alert accuracy in controlled zones. Phase two integrates the site mesh network for vehicle-to-vehicle warnings and connects to the Procore or Autodesk Build safety module for logging. The final phase adds predictive analytics, using historical event data to model traffic pattern risks and recommend layout changes. Controls include manual alert override logs, periodic sensor calibration checks, and a human-in-the-loop review for all logged near-misses before regulatory reporting.

This automation directly addresses the high-cost, high-liability risk of struck-by incidents involving dump trucks, forklifts, and concrete mixers. By fusing ultrasonic sensors and on-vehicle cameras with edge AI modules, the system eliminates reliance on operator vigilance alone. It automates the continuous monitoring of blind spots, triggering in-cab visual/audible alerts and external warning sounds to alert nearby personnel. The operational upside comes from preventing costly injuries, reducing insurance premiums, and avoiding project delays from incident investigations and regulatory scrutiny.

Implementation requires ruggedized edge compute (e.g., NVIDIA Jetson) on each vehicle, running fine-tuned object detection models. A site-wide LoRaWAN or private LTE mesh network handles vehicle-to-vehicle (V2V) alerts and telemetry backhaul to a central platform like Autodesk Build or Procore. Critical controls include configurable alert thresholds, manual override capabilities, and a human review queue for logged events to validate false positives before they impact performance metrics. Phased rollout starts with a pilot on a single vehicle class, integrating alerts with existing daily equipment checklists before scaling fleet-wide.