Road Lane Segmentation Using Vehicle Trajectory Tracking and Lane Demarcation Lines

As levels of road traffic congestion increase relative to population density, it is becoming increasingly necessary for traffic managers to have awareness of road situations in real-time to keep up with traffic management. There are already existing techniques and applications in computer vision that traffic managers use to collect real-time telemetry, such as but not limited to vehicle counting algorithms. However, these algorithms and applications may not be lane-aware. Enabling lane awareness to these systems allows them to be more granular, which enables more in-depth telemetry such as lane usage, driver pattern recognition, and anomaly detection, among others. Lane awareness in these systems are enabled by performing lane segmentation. This study investigates two approaches to this. The first approach uses vehicle trajectories to generate aggregated trajectory maps, which are then clustered to determine trajectory lane membership and to generate representative trajectories that describes the lane. On the other hand, the second approach takes an end-to-end method and uses road lane features such as demarcation lines to segment lanes. The first approach proved to be more viable as a lane segmentation algorithm compared to the second approach as it was able to segment lanes more reliably, given enough vehicle trajectories are present.