Lane-Level Road Information Mining from Vehicle GPS Trajectories Based on Naïve Bayesian Classification

Luliang Tang,Zihan Kan,Qingquan Li,Xue Yang

doi:10.3390/ijgi4042660

Abstract

In this paper, we propose a novel approach for mining lane-level road network information from low-precision vehicle GPS trajectories (MLIT), which includes the number and turn rules of traffic lanes based on naïve Bayesian classification. First, the proposed method (MLIT) uses an adaptive density optimization method to remove outliers from the raw GPS trajectories based on their space-time distribution and density clustering. Second, MLIT acquires the number of lanes in two steps. The first step establishes a naïve Bayesian classifier according to the trace features of the road plane and road profiles and the real number of lanes, as found in the training samples. The second step confirms the number of lanes using test samples in reference to the naïve Bayesian classifier using the known trace features of test sample. Third, MLIT infers the turn rules of each lane through tracking GPS trajectories. Experiments were conducted using the GPS trajectories of taxis in Wuhan, China. Compared with human-interpreted results, the automatically generated lane-level road network information was demonstrated to be of higher quality in terms of displaying detailed road networks with the number of lanes and turn rules of each lane.

Highlights

Accurate lane-based road network data for navigation, such as lane location, lane changes, and turn information, is crucial for ensuring reliable and safe driving, especially for intelligent transportation systems (ITS) such as advanced driver assistance systems and autonomous driving
The contributions of this paper are as follows: (1) We propose a new method, the adaptive density optimization method, for vehicle global position system (GPS) trajectory optimization based on the density clustering method and the spatial distribution of tracking points
We proposed an automated method (MLIT) to extract lane information, such as numbers of lane and lane turns on road segments from low-precision GPS trajectory data

Summary

Introduction

Accurate lane-based road network data for navigation, such as lane location, lane changes, and turn information, is crucial for ensuring reliable and safe driving, especially for intelligent transportation systems (ITS) such as advanced driver assistance systems and autonomous driving. Lane-level information (such as number of lanes and turning in the intersection) is usually acquired from high-definition video/images, laser point clouds, or DGPS/INS trajectories [3,4,5,6,7]. Public vehicles and personal navigation assistants are equipped with single-frequency global position system (GPS) trackers or loggers that monitor the user locations at regular intervals [9,10]

Methods

Results

Conclusion