A Robust Methodology for Dynamic Proximity Sensing of Vehicles Overtaking Micromobility Devices in a Noisy Environment

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.