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State Estimation for Truck and Trailer Systems using Deep Learning
Linköping University, Department of Electrical Engineering, Automatic Control.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Tillståndsskattning med hjälp av djupinlärning för lastbilar med dolly och semitrailer (Swedish)
Abstract [en]

High precision control of a truck and trailer system requires accurate and robust state estimation of the system.

This thesis work explores the possibility of estimating the states with high accuracy from sensors solely mounted on the truck. The sensors used are a LIDAR sensor, a rear-view camera and a RTK-GNSS receiver.

Information about the angles between the truck and the trailer are extracted from LIDAR scans and camera images through deep learning and through model-based approaches. The estimates are fused together with a model of the dynamics of the system in an Extended Kalman Filter to obtain high precision state estimates. Training data for the deep learning approaches and data to evaluate and compare these methods with the model-based approaches are collected in a simulation environment established in Gazebo.

The deep learning approaches are shown to give decent angle estimations but the model-based approaches are shown to result in more robust and accurate estimates. The flexibility of the deep learning approach to learn any model given sufficient training data has been highlighted and it is shown that a deep learning approach can be viable if the trailer has an irregular shape and a large amount of data is available.

It is also shown that biases in measured lengths of the system can be remedied by estimating the biases online in the filter and this improves the state estimates.

Place, publisher, year, edition, pages
2018. , p. 63
Keywords [en]
state estimation, observer, deep learning, neural network, convolutional neural network, EKF, truck, trailer, semi-trailer
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-148630ISRN: LiTH-ISY-EX--18/5150--SEOAI: oai:DiVA.org:liu-148630DiVA, id: diva2:1219127
Subject / course
Automatic Control
Presentation
2018-06-08, Systemet, 09:30 (Swedish)
Supervisors
Examiners
Available from: 2018-06-15 Created: 2018-06-15 Last updated: 2018-06-15Bibliographically approved

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CiteExportLink to record
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