Citation

Akai N, Morales LY, Hirayama T, Murase H. Trans. Soc. Automot. Eng. Jpn. 2019; 50(2): 609-615.

Copyright

(Copyright © 2019, Society of Automotive Engineers of Japan)

DOI

10.11351/jsaeronbun.50.609

PMID

unavailable

Abstract

This paper presents a reliability estimation method of localization results. In the method, an egovehicle pose and reliability are treated as hidden variables and are estimated simultaneously via Rao- Blackwellized particle filter (RBPF). The ego-vehicle pose is estimated by a sampling-based method, i.e., particle filter, and the reliability is estimated by an analytical method using prediction results of convolutional neural network (CNN). The CNN learns whether localization has failed or not and its output is used as an observable variable to estimate the reliability in the RBPF. Through experiments, it is shown that the estimated reliability could be used as an exact criterion for describing successful and fault localization results.

Language: ja

Keywords

Electronics and control; Autonomous Driving; Localization