Intersection Detection Based on Qualitative Spatial Reasoning on Stopping Point Clusters

Abstract

ABSTRACT:The purpose of this research is to propose and test a method for detecting intersections by analysing collectively acquired trajectories of moving vehicles. Instead of solely relying on the geometric features of the trajectories, such as heading changes, which may indicate turning points and consequently intersections, we extract semantic features of the trajectories in form of sequences of stops and moves. Under this spatiotemporal prism, the extracted semantic information which indicates where vehicles stop can reveal important locations, such as junctions. The advantage of the proposed approach in comparison with existing turning-points oriented approaches is that it can detect intersections even when not all the crossing road segments are sampled and therefore no turning points are observed in the trajectories. The challenge with this approach is that first of all, not all vehicles stop at the same locationthus, the stop-location is blurred along the direction of the road; this, secondly, leads to the effect that nearby junctions can induce similar stop-locations. As a first step, a density-based clustering is applied on the layer of stop observations and clusters of stop events are found. Representative points of the clusters are determined (one per cluster) and in a last step the existence of an intersection is clarified based on spatial relational cluster reasoning, with which less informative geospatial clusters, in terms of whether a junction exists and where its centre lies, are transformed in more informative ones. Relational reasoning criteria, based on the relative orientation of the clusters with their adjacent ones are discussed for making sense of the relation that connects them, and finally for forming groups of stop events that belong to the same junction.

Generating Road Networks for Old Downtown Areas Based on Crowd-Sourced Vehicle Trajectories

“…Intersection-link methods detect road intersections first based on density distribution of trajectory sampling points and their implicit semantic features [15,38], trajectory point direction, speed, and their implicit dynamic features [17,39], and then connect these intersections to form the road network. However, current research mainly focuses on intersection extraction, and seldom conduct further road network generation [40].…”

Estimating Road Segments Using Natural Point Correspondences of GPS Trajectories

“…One way is to use external sensors [3], like mounted cameras that track the location of traffic participants. Another way is to use data, captured by vehicles that log their position and possibly additional information [4]. Similarly, the position of pedestrians, bikers or car drivers is often logged by mobile devices tracking the positions of their users.…”

Extraction of Road Intersections from GPS Traces Based on the Dominant Orientations of Roads

“…To apply more intersection geometry types and variable orientations, stronger constraints and additional knowledge regarding intersections should be refined. Because intersections with the same road connectivity can be intersected with diverse forms associated with road segments with different independent dominant orientations [34], a set of paired segments that are approximately collinear is first identified around the same intersection. Each pair is merged into a single segment and, finally, only the conflicting orientation segments at the same intersection are retained.…”