###### Abstract  
The safety of cyclists, e-scooters, and micromobility devices in urban environments remains a critical concern in sustainable urban planning. A primary factor affecting this safety is the lateral passing distance (LPD) or dynamic proximity of motor vehicles overtaking micromobility riders. Minimum passing distance laws, where motorists are required to maintain a minimum distance of 1.5 m when passing a cyclist, are difficult to enforce due to the difficulty in determining the exact distance between a moving vehicle and a cyclist. Existing systems reported in the literature are invariably used for research and require manual intervention to record passing vehicles. Further, due to the dynamic and noisy environment on the road, the collected data also need to be manually post-processed to remove errors and false positives, thus making such systems impractical for use by cyclists. This study aims to address these two concerns by providing an automated and robust framework, integrating a low-cost, small single-board computer with a range sensor and a camera, to measure and analyze vehicle-cyclist passing distance and speed. Preliminary deployments in Singapore have demonstrated the system's efficacy in capturing high-resolution data under varied traffic conditions. Our setup, using a Raspberry Pi 4, LiDAR distance sensor, a small camera, and an automated data clustering technique, had a high success rate for correctly identifying the number of close vehicle passes for distances between 1 and 1.5 m. The insights garnered from this integrated setup promise not only a deeper understanding of interactions between motor vehicles and micromobility devices, but also a roadmap for data-driven urban safety interventions. lateral passing distance; minimum passing distance; cyclist safety; overtaking; distance measurement; vehicle speed measurement +
Footnote †: journal: Medical and Nuclear Engineering  
0000-0001-9993-3300-3300-3300-3300-3300-3300-3300-3300-3300-3300-33000-3300-3300-33  
With the rise in cycling, there has been an unfortunate increase in cycling-related accidents. The World Health Organization reports that 41,000 cyclists face fatal accidents annually during commutes [3]. In Singapore, the growth of food delivery services necessitates more cyclists on the road, further escalating the risk. Data reflect an 11% increase in food consumed/ordered via food delivery services during the COVID-19 pandemic [4] which corresponds to a 25% rise in cycling-related accidents during the same period [5]. Nonetheless, many European countries and the USA, Canada, and Australia, where cycling is prevalent, have resisted effective techniques like traffic calming to improve the safety of cyclists and road safety in general [6]. In fact, there seems to be very little that cyclists can do to prevent vehicles from overtaking them dangerously [7], which suggests that structural or legal measures must be taken to reduce cycling-related accidents. To address the safety concerns, some cities, including Singapore, have set a 1.5 m passing distance guideline [8]. However, the effectiveness and enforcement of these guidelines remain contentious. Moreover, a study from Germany showed variable adherence levels, where cars meet the legal passing distance of 1.5 m only 30% of the time [9]. Table 1 shows different countries and their different mandatory passing distance (MPD) rules. Despite guidelines, adherence remains an issue. Uncertainty about distance measurements from motorists and a lack of quantifiable evidence from cyclists create complications. Measuring the lateral passing distance of vehicles passing cyclists involves several technical challenges, primarily because it requires accurate, real-time data collection in a dynamic, outdoor environment. The quality of the measured data is influenced by factors including sensor precision, lighting conditions, and the vibration of the moving bicycle.