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Radar-Based Two-Dimensional Ego-Motion Estimation for Heavy Duty Vehicles
KTH, School of Electrical Engineering (EES).
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The development of Advanced Driver Assistance Systems (ADASs) has during recent years paved the way for important improvements within the industry of long haulage driving. Never have heavy duty vehicles been that safe and efficient as they are today and one may argue that high-level competition among manufacturers and strongly regulated legislations are the major underlying causes of contribution. Further on, driver support is currently taking further steps towards autonomous driving which makes vehicle ego-motion estimation a more crucial task to deal with. Novel estimation techniques should guarantee robustness, precision and redundancy to further increase the level of performance in tomorrow’s driver support systems.

This thesis presents a multi-sensor approach for two-dimensional ego-motion estimation based on a sensor set-up comprised by the above-ground type sensors Doppler radar and accelerometer along with the conventional inductive wheel encoder and yaw-rate sensor. A decentralized Kalman filter architecture with radar-based fusion state feedback and incorporated outlier detection has been implemented to gain robust long-term accuracy. Estimation of instantaneous longitudinal speed and yaw-rate are delivered by system, developed to suit a modern Scania produced heavy duty vehicle with standard specifications.

The Proposed ego-motion estimation system has been, in estimation of longitudinal speed, shown to perform better than currently employed system. The proposed system is, based on tests in estimation of longitudinal speed on highway, shown capable to reduce Maximum Absolute Error (MAE) and Root Mean Square Error (RMSE) with up to 27 % and 45 %, respectively. Further on, increased sensitivity at low-speed start-stop driving has been achieved.

Abstract [sv]

Tillverkningsindustrin för tunga fordon har på senare år tagit stora steg framåt i utvecklingen av avancerade förarassistansfunktioner. Dagens system möjliggör effektivare transportlösningar som är både säkrare och mer miljövänliga än någonsin tidigare. Tänkbara orsaker till grund för denna utveckling är hårt åtstramade lagkrav, tuff konkurrens samt ett allt tydligare fokus på automatisering. För att i framtiden nå fulländad autonomi spås krav på högre precision och robusthet samt en utvecklad funktionalitet i framtida fordonssystem. Som ett led i denna utvecklingsprocess har estimering av fordonsdynamik i form av hastighetsskattning fått en alltmer betydande roll som indata för flertalet essentiella fordonssystem.

Utfört arbete har genererat ett nytt hastighetsskattningssystem för tunga fordon. Med, för sammanhanget nya givare såsom accelerometer och dopplerradar samt de mer konventionella givartyperna, hjulsensor och rotationsgivare, estimeras hastighet i två dimensioner. Longitudinell hastighet och rotationshastighet kring fordonets vertikala axel. Föreslaget system utgörs av en decentraliserad och kalmanfilterbaserad arkitektur med radarfusionerad återkoppling och inbyggd feldetektion i syfte passa dagens tunga fordon tillverkade av Scania.

Det visar sig att föreslaget system är kapabelt till konkurrenskraftig hastighetsskattning, varvid longitudinell hastighet skattas med bäst resultat. Test av motorvägskörning i tät traffik visar att maximalt absolutfel och kvadratiskt medelvärdesfel kan reduceras med upp till 27 % respektive 45 % mätt relativt dagens metodik. Vidare, signifikanta prestandaförbättringar har påvisats vid test av start- och stoppdetektion under låghastighetstester.

Place, publisher, year, edition, pages
2016. , 77 p.
Series
EES Examensarbete / Master Thesis, TRITA EE 2016:089
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-192378OAI: oai:DiVA.org:kth-192378DiVA: diva2:968133
External cooperation
Scania
Examiners
Available from: 2016-09-12 Created: 2016-09-12 Last updated: 2016-09-12Bibliographically approved

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