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dc.contributor.advisorRahman, Md. Khalilur
dc.contributor.authorHossain, Md. Arafat
dc.contributor.authorRahman, Md. Sazidur
dc.contributor.authorIslam, Md. Jisan Bin
dc.contributor.authorBhuiyan, Sazzad Alam
dc.date.accessioned2022-02-23T06:34:58Z
dc.date.available2022-02-23T06:34:58Z
dc.date.copyright2021
dc.date.issued2021-09
dc.identifier.otherID 17201079
dc.identifier.otherID 17201089
dc.identifier.otherID 17201090
dc.identifier.otherID 17201092
dc.identifier.urihttp://hdl.handle.net/10361/16322
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 52-55).
dc.description.abstractThis thesis scrutinizes the problem of perception in the self-driving car system. Selfdriving car is the face of the future and the decade’s research focus. Tech giants like Google, Uber, Tesla, Commai, Intel MobilEye etc. are now immensely investing in this particular technology. In our work, we mainly address the perception problem of autonomous vehicle and try to solve it with only cameras and comparatively lower computational cost. Firstly, to detect the lane we propose QLD (Quick Lane Detection) model on CULane dataset which gives significantly improved results in the roads of countries like Bangladesh than other existing methods. Secondly, for object detection we propose our own dataset BDCO or Bangladeshi Common Objects, and merge it with MS COCO dataset to make it suitable for Bangladeshi roads. We train BDCO dataset in a CNN based object detection model (CbOD) which also gives very promising results in local roads. Finally, we cascade QLD and CbOD with our decision-making system which outputs the warnings based on the analysis of the inputs from cameras in the vehicle. Our hands-on evaluations show that, our cascaded network Bangladeshi Driving Assistant (BD-DA) attains performance competitive to the state-of-the-art systems on a indistinguishable benchmark.en_US
dc.description.statementofresponsibilityMd. Arafat Hossain
dc.description.statementofresponsibilityMd. Sazidur Rahman
dc.description.statementofresponsibilityMd. Jisan Bin Islam
dc.description.statementofresponsibilitySazzad Alam Bhuiyan
dc.format.extent57 pages
dc.language.isoenen_US
dc.publisherBrac Universityen_US
dc.rightsBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectObject detectionen_US
dc.subjectPredictionen_US
dc.subjectMax poolingen_US
dc.subjectConvolutional neural networken_US
dc.subject.lcshMachine learning
dc.subject.lcshNeural networks (Computer science)
dc.titleTowards solving perception based autonomous driving assistant systemen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB. Computer Science


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