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Journal of Advanced Transportation
Volume 2019, Article ID 7496017, 13 pages
https://doi.org/10.1155/2019/7496017
Research Article

Sensitivity and Performance Evaluation of Multiple-Model State Estimation Algorithms for Autonomous Vehicle Functions

1Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics, Stoczek u.2., H-1111 Budapest, Hungary
2Computer and Automation Research Institute, Hungarian Academy of Sciences, Kende u. 13-17, H-1111 Budapest, Hungary

Correspondence should be addressed to Tamás Bécsi; uh.emb.liam@samat.isceb

Received 15 December 2018; Revised 14 March 2019; Accepted 17 March 2019; Published 4 April 2019

Academic Editor: Yair Wiseman

Copyright © 2019 Olivér Törő et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Robust object tracking and maneuver estimation methods play significant role in the design of advanced driver assistant systems and self-driving cars. As an input to situation understanding and awareness, the performance of such algorithms influences the overall effectiveness of motion planning and plays high role in safety. The paper examines the suitability of different probabilistic state estimation methods, namely, the Extended Kalman Filter (EKF) and the more general Particle Filter (PF) with the addition of the Interacting Multiple Model (IMM) approach. These algorithms are not capable of predicting motion for long term in road traffic conditions, though their robustness and model classification capability are essential for the overall system. The performance is evaluated in road traffic scenarios where the tracked object imitates the motion characteristics of a road vehicle and is observed from a stationary sensor. The measurements are generated according to standard automotive radar models. The analysis conducted along two aspects emphasizes the different performance and scaling properties of the examined state estimation algorithms. The presented evaluation framework serves as a customizable method to test and develop advanced autonomous functions.