Mobile Alert System Using Lane Detection Based on Vehicle Clustering

To reduce motorcycle accidents, recent efforts have focused on traffic warning systems. In this study, we developed a highly portable system that utilizes smartphones and web cameras to detect motorcycles approaching at high speeds and to notify drivers of potential dangers. Specifically, we have implemented a feature to detect hazardous scenarios where motorcycles change lanes from behind and overtake on the right. Accurate lane estimation is crucial for precisely assessing situations where motorcycles change lanes. While lane estimation methods based on recognizing white road markings exist, they may not be effective on roads with unclear or faded white lines, which are common in developing countries. An alternative approach involves estimating lanes from fixed camera vehicle trajectories, but this is limited to locations where fixed cameras are installed. Moreover, deep learning methods for lane detection face computational challenges on mobile systems, especially when analyzing both lane positions and vehicle behavior. Here we propose a method to detect vehicles and estimate lanes based on their trajectories from videos captured by in-vehicle cameras in developing countries. The effectiveness of our proposed method was demonstrated through real-world road tests in Indonesia.