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Learning Probabilistic Awareness Models for Detecting Abnormalities in Vehicle Motions | IEEE Journals & Magazine | IEEE Xplore

Learning Probabilistic Awareness Models for Detecting Abnormalities in Vehicle Motions


Abstract:

This paper proposes a method to detect abnormal motions in real vehicle situations based on trajectory data. Our approach uses a Gaussian process (GP) regression that fac...Show More

Abstract:

This paper proposes a method to detect abnormal motions in real vehicle situations based on trajectory data. Our approach uses a Gaussian process (GP) regression that facilitates to approximate expected vehicle's movements over a whole environment based on sparse observed data. The main contribution of this paper consists in decomposing the GP regression into spatial zones, where quasi-constant velocity models are valid. Such obtained models are employed to build a set of Kalman filters that encode observed vehicle's dynamics. This paper shows how proposed filters enable the online identification of abnormal motions. Detected abnormalities can be modeled and learned incrementally, automatically by intelligent systems. The proposed methodology is tested on real data produced by a vehicle that interacts with pedestrians in a closed environment. Automatic detection of abnormal motions benefits the traffic scene understanding and facilitates to close the gap between human driving and autonomous vehicle awareness.
Published in: IEEE Transactions on Intelligent Transportation Systems ( Volume: 21, Issue: 3, March 2020)
Page(s): 1308 - 1320
Date of Publication: 26 April 2019

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