Estimating Road Segments Using Natural Point Correspondences of GPS Trajectories

Artem Leichter,Martin Werner

doi:10.3390/app9204255

Artem Leichter, Martin Werner

Open Access

https://doi.org/10.3390/app9204255

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Abstract

This work proposes a fast and straightforward method, called natural point correspondences (NaPoCo), for the extraction of road segment shapes from trajectories of vehicles. The algorithm can be expressed with 20 lines of code in Python and can be used as a baseline for further extensions or as a heuristic initialization for more complex algorithms. In this paper, we evaluate the performance of the proposed method. We show that (1) the order of the points in a trajectory can be used to cluster points among the trajectories for road segment shape extraction and (2) that preprocessing using polygonal approximation improves the results of the approach. Furthermore, we show based on “averaging GPS segments” competition results, that the algorithm despite its simplicity and low computational complexity achieves state-of-the-art performance on the challenge dataset, which is composed of data from several cities and countries.

Highlights

The automated extraction of road networks and their attributes is crucial for the success of challenging applications like dynamic routing, autonomous driving or artificial intelligence (AI)-driven urban planning
Introducing the trajectory reversion step leads to an increased measure of 0.66
The competition results are available at http://cs.uef.fi/sipu/segments/results.html [6]

Summary

Introduction

The automated extraction of road networks and their attributes is crucial for the success of challenging applications like dynamic routing, autonomous driving or artificial intelligence (AI)-driven urban planning. The pure size and rapid changes of the infrastructure make it impossible to survey road networks manually in required spatial resolution, level of detail, and update frequency. Data-driven approaches have been developed to tackle the automated generation or to update existing road networks. A typical strategy is to use remote sensing data [1,2] or to gather information from traffic participants in the form of trajectories which can be processed to obtain road segments. Sometimes the waypoints include further information such as the traveling speed or the type of traffic participant which can be included in the processing

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