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Statistical Sensor Modelling for Autonomous Driving Using Autoregressive Input-Output HMMs

Conference paper
Authors Edvin Listo Zec
Nasser Mohammadiha
Alexander Schliep
Published in 21st International Conference on Intelligent Transportation Systems, {ITSC} 2018
ISBN 978-1-7281-0321-1
Publisher IEEE
Publication year 2018
Published at Department of Computer Science and Engineering, Computing Science (GU)
Language en
Links https://ieeexplore.ieee.org/documen...
Subject categories Information processing

Abstract

Advanced driver assistance systems (ADAS) are standard features in many vehicles today and they have been proven to significantly increase the traffic safety. This paved way for development of autonomous driving (AD). To enable this, the vehicles are equipped with many sensors such as cameras and radars in order to scan the surrounding environment. The sensor outputs are used to implement decision and control modules. Verification of AD is a challenging task and requires collecting data from at least hundreds of millions of autonomously driven miles. We are therefore interested in virtual verification methods that simulate interesting and relevant situations, so that many scenarios can be tested in parallel. Realistic simulations require accurate sensor models, and in this paper we propose a probabilistic model based on the hidden Markov model (HMM) for modelling the sequential data produced by the sensors used in ADAS and AD. Moreover, we propose an efficient way to estimate parameters that scales well to big data sets. The results show that extending the HMM to use autoregression and input dependent transition probabilities is important in order to model the sensor characteristics and substantially improves the performance.

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