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Adaptive Steering Behaviour for Heavy Duty Vehicles
KTH, School of Electrical Engineering (EES), Automatic Control.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Today the majority of the driver assistance systems are rule-basedcontrol systems that help the driver control the truck. But driversare looking for something more personal and exible that can controlthe truck in a human way with their own preferences. Machine learningand articial intelligence can help achieve this aim. In this studyArticial Neural Networks are used to model the driver steering behaviourin the Scania Lane Keeping Assist. Based on this, trajectoryplanning and steering wheel torque response are modelled to t thedriver preference. A model predictive controller can be used to maintainstate limitations and to weigh the two modelled driver preferencestogether. Due to the diculties in obtaining an internal plant modelfor the model predictive controller a variant of a PI-controller is addedfor integral action instead. The articial neural network also containsan online learning feature to further customize the t to the driverpreference over time.

Abstract [sv]

Idag används till största del regelbaserade reglersystem förförarassistanssystem i lastbilar. Men lastbilschaufförer vill ha någotmer personligt och flexibelt, som kan styra lastbilen på ett mänskligtsätt med förarens egna preferenser. Maskininlärning och artificiell intelligenskan hjälpa till för att uppnå detta mål. I denna studie användsartificiella neurala nätverk för att modellera förarens styrbeteende genomScania Lane Keeping Assist. Med användning av detta modellerasförarens preferenser med avseende på placering på vägbanan och momentpåslag på ratten. En modell prediktiv kontroller kan användas föratt begränsa tillstånd och för att väga de två modellerade preferensernamot varann. Eftersom det var mycket svårt att ta fram den internaprocessmodellen som krävdes för regulatorn används istället en variantav en PI-kontroller för att styra lastbilen. De artificiella neuralanätverken kan också tillåtas att lära sig under körning för att anpassasig till förarens preferenser över tid.

Place, publisher, year, edition, pages
2017. , p. 52
Series
TRITA-EE, ISSN 1653-5146 ; 2017:108
Keyword [en]
Scania, ANN, Articial Neural Network, MPC, Model Predictive Control, System identication, Vehicle dynamics, Recursive ltering, Human behaviour, Machine learning, Online learning, Sample based processing
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-215134OAI: oai:DiVA.org:kth-215134DiVA, id: diva2:1146562
External cooperation
SCANIA
Available from: 2017-10-03 Created: 2017-10-03 Last updated: 2017-10-03Bibliographically approved

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Automatic Control
Electrical Engineering, Electronic Engineering, Information Engineering

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CiteExportLink to record
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Citation style
  • apa
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