Simulation of Predicting the Safety Level of Objects in the Blind Spot Area of a Forklift Unit Using Fuzzy and Artificial Neural Networks

Forklifts are widely used in industrial and construction sectors for material handling, yet they pose significant safety risks due to blind spots, particularly at the rear of the vehicle. These blind spots are a leading cause of accidents involving forklifts, often resulting in severe injuries, especially among pedestrians. This study aims to enhance blind spot detection in forklifts by developing a system that integrates fuzzy logic with machine learning techniques. The proposed system incorporates critical variables such as steering angle and vehicle velocity to improve the accuracy of assessing severity of the obstacles within blind spots. Fuzzy logic is deployed to translate the expert or standard knowledge to machine. Three scenarios are analysed using Fuzzy logic Mamdani based on various sensors. Ultrasonic sensors were employed for close-range detection, while Artificial Neural Networks (ANNs) were used to analyze the frequency of dangerous states during forklift operations. The system's performance was evaluated across various scenarios, demonstrating its effectiveness in real-time safety assessments. The findings showst the potential to reduce the risk of accidents in forklift operations by providing timely alerts and accurate safety level predictions.