Real-Time AI-Based Forklift Collision Avoidance System using Multi-Zone Safety Control
DOI:
https://doi.org/10.15662/IJEETR.2026.0802410Keywords:
Forklift safety, Collision avoidance, AI-based detection, Industrial automation, Time-To-Collision, Smart safety systemsAbstract
The cases of collision associated with forklifts have continued to be quite rampant in the industrial environments because of blind spots of the operators, slow human response, and lack of intelligent safety measures. The current systems are mostly based on manual monitoring or simple proximity sensors, which do not give predictive and adaptive safety control. This restriction points to a very serious gap in the research of combining real-time perception based on AI with active motion control to prevent collisions in advance. The present paper suggests a real-time forklift collision avoidance system, which integrates an AI-based object detection system, ultrasonic proximity sensing, and a multi-zone safety control approach. The system is designed in three safety zones; a warning (approximately 2 m), slow-down (1 to 1.5 m), and emergency stop (less than 1 m) and adaptive control measures, which comprise visual/audio notifications, speed control, and controlled stop. The methodology uses Time-To-Collision (TTC) estimation in predictive hazard estimation and has an embedded control unit in real-time decision execution. The validation of the experimental results was performed on a battery-powered industrial forklift with the help of real-time video data and proximity sensing. The system obtained 96.8% detection, 52.3% collision reduction, and a response time of less than 120 ms. The suggested model greatly improves workplace safety by making the operation of forklifts rely on proactive, rather than reactive control
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