## 6 Discussion
### Challenges and Implications of Integrating Autonomous Technology into Forklifts  
[PERSON] and [PERSON] [120] believe that autonomous vehicle manufacturers do not anticipate commercially releasing completely autonomous vehicles in the near future due to many considerations such as human behavior, ethics and traffic management. This has led to the use of these vehicles in less crowded and complex environments such as forklifts, etc. Furthermore, [PERSON] and [PERSON] claimed \begin{table}
\begin{tabular}{l l} \hline \hline
**Metric** & **Impact** \\ \hline Inventory Holding Costs & A 30\% reduction due to AI-powered predictive analytics \\ Procurement Costs & A 15\% decrease through AI-driven supplier evaluation systems \\ Operational Efficiency & A 25\% increase from AI-optimized route planning \\ Overall Productivity & A 40\% boost attributed to AI-enabled automation \\ Labor Requirements & A 20\% projected reduction in low-skilled labor over five years \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Impact of AI implementation on key business metrics as per [119]. that technically, the clear \"detection of obstacles at high speeds and long distances is one of the biggest difficulties\" that require attention for the development of viable solutions. Table 14 showcases selected publications addressing various challenges and the potential solutions that were listed. Designing machine vision systems for autonomous forklifts involves numerous complex challenges critical for ensuring safe and efficient operation in industrial environments. One primary challenge is managing the diverse and dynamic conditions these forklifts encounter. They must function effectively under varied lighting conditions, including the bright lights of loading docks, shadows in warehouses and low-light environments. Additionally, they need to handle dynamic obstacles such as other forklifts, workers and varying types of inventory, accurately predicting their movements. Navigating from narrow aisles to open storage areas requires the vision system to understand different spatial configurations and traffic patterns thoroughly. Accurate and fast object detection and classification are essential, demanding high-speed processing and advanced algorithms to minimize latency. The system must detect small and partially occluded objects, such as pallets, boxes or equipment partially hidden behind shelves, and recognize objects despite variations in appearance due to orientation, distance and environmental conditions. Robustness and reliability are critical, requiring effective sensor fusion that integrates data from multiple sensors like cameras, LiDAR and radar. This integration must be performed efficiently to enhance reliability and ensure that fail-safe mechanisms are in place to handle sensor malfunctions without compromising safety. Real-time processing poses significant computational demands, requiring a balance between high performance and limitations in power and processing power. Efficient algorithms that process data quickly while maintaining accuracy are essential for the real-time operation of autonomous forklifts. Training machine learning models with large and diverse datasets ensures they are comprehensive and representative. Balancing simulation