## 6 Discussion
### Challenges and Implications of Integrating Autonomous Technology into Forklifts
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Challenge Description** & **Possible/Potential Solutions** & **Selected References** \\ \hline Limited Visibility & Implementation of improved sensor technologies, including the use of advanced LiDAR systems and radar for increased range and precision. These sensors enhance detection capabilities in adverse conditions such as fog, rain, or low light. & [39; 121; 122; 123; 124] \\ \hline Object Recognition and Classification & Utilization of advanced machine learning algorithms and models, including deep learning techniques, to improve the accuracy of object recognition and classification under various environmental conditions and in complex scenes. & [125; 126; 127] \\ \hline Sensor Calibration & Establishment of standardized regular maintenance routines and precise calibration procedures to ensure sensor accuracy and alignment, which are crucial for reliable data and performance over time. & [131; 132; 133; 134; 135] \\ \hline Real-time Processing & Integration of high-performance Graphics Processing Units (GPUs), advanced computational hardware and optimized parallel processing frameworks to achieve efficient, real-time data processing and minimize latency in autonomous applications. & [131; 132; 133; 134; 135] \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Selected publications on key challenges and proposed solutions. and real-world testing is vital for robust system development, as real-world validation accounts for unpredictable conditions not present in simulations. Handling edge cases and long-tail problems is another challenge, as the system must effectively manage rare and unusual scenarios, such as navigating through unexpected obstructions or dealing with unusual inventory sizes and shapes. Continuous learning is needed for the system to adapt to new situations without overfitting or degrading performance in known scenarios. The research in [136] seeks to investigate people's receptiveness towards autonomous vehicles by respecting their trust and sustainability concerns. This objective was accomplished by formulating the technology acceptance model (TAM). A questionnaire was administered to 391 participants. In 2021, ref. [137] presents the next challenge for autonomous driving in 2021, which is the technological equivalent of the space race of this century. The authors argue that a rethink is required and offer an alternative vision. Also, [PERSON] in [138] proposed a comprehensive review and introduction of simulator investigation, and user portrayal was utilized in this research to close the divide between the present and the future. [PERSON] in [139] examined the effects of autonomous vehicles and their implications for transportation planning. The study suggests Level 5 autonomous vehicles, capable of operating \"without a driver, may be commercially available and legal for use in some jurisdic by late 2020\" and will only become popular when autonomous vehicles become widespread and affordable, possibly in the timeframe of 2040 to 2060, according to their expectation. In [140], the paper \"explores differences in perceptions of AV safety across 33,958 individuals in 51 countries\". The master thesis written by [PERSON] in [141] illustrates research examining the essential pillars of autonomous technology in general. It further addresses the \"development of autonomous vehicles and their future implications in a sharing economy\". Finally, the authors raised their concerns in their home country of Japan, where major concerns about the aging workforce are a critical issue. The current techniques for pallet identification and positioning, which rely on a single source of data such as RGB images or point cloud, can result in inaccurate placement or require significant computational resources, increasing costs significantly.