###### Abstract
Current studies, while emphasizing the impact and nature of LPD in their research, do not place enough emphasis on their methods of collection. Data collection of existing studies, which is difficult to replicate, often relies on manual, labor-intensive, and costly equipment. Table 2 depicts a sample of LPD studies conducted in the last decade, the majority of which employ manual collection techniques, relying on an operator to filter through video footage and identify the passing distances of vehicles. Some studies by [PERSON] et al. and [PERSON] et al. [11; 12] overcame this challenge by relying on a push button that participants could press to identify a vehicle pass. However, this detracts from the authenticity of the riding experience and may also be prone to inaccuracies on the part of the participant. Of the studies that employ automated data collection, many use expensive and complicated setups like a driving simulator such as [PERSON] et al. [13] and [PERSON] and [PERSON] [14], or a difficult-to-replicate setup consisting of expensive and inaccessible equipment like [PERSON] et al. [15]. Current devices such as the C3 FT [16] are available commercially for use in enforcement, education, and research, such as by [PERSON] et al. [17]. However, the C3 FT costs USD $1480 as of 2024 and had to be supplemented with video footage in the study in which it was used. Other studies that use automated collection techniques face difficulties with filtering noise like [PERSON] et al. [18], or do not describe \begin{table}
\begin{tabular}{c c c} \hline \hline
**Country** & **MPD Advised** & **MPD Mandated** \\ \hline Austria & Yes—1.5 m & No \\ Belgium & No & Yes—1 m \\ Chile & Yes—1.5 m & No \\  & & Yes—1 m on roads with \\  & & \(\leq\)50 km/h speed limit, \\ France & No & and 1.5 m on roads with \\  & & \(>\)50 km/h speed limit. \\ Germany & No & Yes—1.5 m in urban areas, and 2 m out of town. \\ New Zealand & Yes—1.5 m & No \\ Singapore & Yes—1.5 m & No \\ United States & Yes—varies by state & Yes—varies by state \\ \hline \hline \end{tabular} Note. Table containing the mandatory passing distance rules for various countries [10]).  
\end{table} 
Table 1: Countries with a minimum passing distance guideline or law. in-depth the challenges of filtering out false positives, and eliminating spurious data such as [PERSON] et al. [19]. The studies listed above undoubtedly provide comprehensive data analysis and conclusions as to LPD. But while their focus mainly concerns the implications of the data collected, this study aims to investigate the potential of an automated data collection method that reduces manual effort, offering a solution for policymakers, researchers, and consumers. Through the integration of a distance sensor, microcontroller, and an AI camera with computer vision capabilities, we ensure automatic and precise data recording. Post-collection, a clustering analysis technique identifies vehicle passes while filtering out false positives, thereby providing a clear count of close encounters. Additionally, image recognition capabilities of the AI camera in conjunction with the distance sensor enables the accurate determination of vehicles' relative passing speeds. Our system is mounted on a bicycle and identifies vehicles passing within 1.5 m or closer without requiring manual logging or video cross-referencing. Our results show that through judicious application of signal processing techniques to noisy data and images captured by relatively low-cost hardware, we can achieve a robust system for accurately measuring lateral passing distances and speeds [29; 30], with the total cost of our setup being around USD 200. Preliminary testing of our system has shown that it successfully and accurately filters out noise and spurious data to identify close vehicle passes at the range of 1-1.5 m.