## 6 Limitations and Future Work  
The primary objective of this project was to explore the potential of available technologies like AI and machine learning to streamline and automate the process of collecting LPD of vehicles to cyclists. While we have made progress, the current setup is not entirely complete. The combination of microcontrollers and sensors employed might not be the most efficient or cost-effective, suggesting that there could be alternatives better aligned with our goals and potentially more economical. A more comprehensive testing of available commercial distance sensors would be needed to ensure that our setup remains the most cost-efficient and reliable. In addition, the current setup, which uses the Raspberry Pi, may be better off using a more space-efficient microcontroller such as the Arduino or the micro-Raspberry Pi. Moreover, the current setup relies on separated components and is not yet packaged as one. This makes the prototype less robust and portable--requiring some technical expertise from the users to properly set up. In the future, packaging the setup into one singular product would allow for easier data collection and testing on our part, but also allow for other researchers to replicate our results more easily.  
Additionally, different hyperparameters or even a different clustering technique altogether could enhance our results by minimizing false positives, which requires additional testing and collection of data. Future research would consist of not only a larger volume of data collection, but also a comprehensive evaluation of different clustering algorithms across diverse datasets to affirm the universal applicability and reliability of our chosen techniques. The current data collected are only from a single path in the Jurong West region of Singapore. In future research, we would not only increase the volume of data collected, but also vary the regions that data are collected from.  
Another improvement would be to fully automate the process of data extraction and analysis. Currently, we are manually extracting the data and running analysis algorithms. However, in efforts to create a standalone product for others to use, our setup might come with an application that not only collects data but analyzes and plots it for the user. Furthermore, we can fully utilize the features of the AI camera to not only filter out noise, but to also take a picture during each vehicle pass, which would provide a useful visual corresponding to the data collected. Such a feature would be well suited for a commercial product used by other researchers to assist in the collection and visualization of data.  
Looking forward, the project's evolution will encompass the assessment of a wider array of sensors, accompanied by extensive on-road data collection. These amassed data will help to test various clustering techniques, adjusting parameters to discern the optimal  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Test Run**} & \multirow{2}{*}{**Distance from Car (m)**} & **Calculated Speed** & **Calculated Speed** & **Speedometer Reading** \\  & & **(km/h)—Manual** & **(km/h)—Image Recognition** & **(km/h)** \\ \hline
1 & 2.28 & 29.5 & 28.3 & 30 \\ \hline
2 & 1.92 & 30.9 & 30.0 & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of measured and calculated speed values.  
Figure 22: Speedometer readings at test run 1 and 2, respectively.  
method. Upon establishing this foundation, our vision is to consolidate the hardware and software into a unified product kit that is readily accessible by micromobility riders.