A constrained spectral clustering method for lane identification using trajectory data

The rapid development of information and communications technologies acts as an enabler for the successful implementation of vehicle automation and advanced traffic management applications. In particular, the appearance of new sources of high-resolution trajectory data, such as videos obtained from drones, provides an opportunity to build accurate maps and enrich applications in traffic research at an unprecedented resolution. However, existing methods cannot handle certain features, such as, among others, accurate of road lane identification. This paper proposes a constrained spectral clustering method to identify lane information from high-resolution trajectory data. Contrary to state-of-the-art methods, such as the Gaussian mixture model, the proposed method is directly applicable to two-dimensional trajectory data, without assuming a constant number of lanes characterised by the same lane width. The trajectory data is clustered by taking into account the neighbourhood distances and prior knowledge via defining so-called must-link and cannot-link constraints, which significantly improve the clustering results, especially in cases where the number of lanes or the lane width changes. The proposed method has been evaluated through numerical experiments using data obtained from drone videos, and the results indicate that the method performs well on complex road segments, even in the presence of a varying number of lanes or lane-changing manoeuvres.