APP: Augmented Proactive Perception for Driving Hazards with Sparse GPS Trace

Abstract

Driving safety is a persistent concern for urban dwellers who spend hours driving on road in ordinary daily life. Traditional driving hazard detection solutions heavily rely on onboard sensors (e.g., front and rear radars, cameras) with limited sensing range. In this article, we propose a proactive hazard warning system, called APP, which aims to alert drivers when there are vehicles with dangerous behaviors nearby. To this end, APP incorporates several basic techniques (e.g, tensor decomposition, similarity comparison) to estimate behavioral data of a driver based on sparse sampled GPS trace at first. Then, with the estimated unlabelled data, potential dangerous behaviors of a particular vehicle are identified and recognized with a Gaussian Mixture Model (GMM) based approach. We have implemented and evaluated our system with a dataset collected for 30 days from over 13,676 taxicabs. Our method shows on average 81% accuracy in potential dangerous behavior recognition.