Lane-Level Map Matching using Hidden Markov Models

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dc.contributor.authorKorsberg, Ellen
dc.contributor.authorNordén, Eliza
dc.contributor.departmentChalmers tekniska högskola / Institutionen för mekanik och maritima vetenskapersv
dc.contributor.departmentChalmers University of Technology / Department of Mechanics and Maritime Sciencesen
dc.date.accessioned2019-07-05T11:53:50Z
dc.date.available2019-07-05T11:53:50Z
dc.date.issued2019
dc.description.abstractMap matching is the procedure of matching vehicle location and sensor data to a digital map. New high-definition maps, designed for autonomous vehicles, open up for the possibility of matching to lanes rather than roads. Inferring the lane-level positions of vehicles will be useful for updating and building probe-sourced maps, and thereby arguably essential for autonomous driving. This thesis seeks to solve the lane-level map matching problem using a Hidden Markov Model. The Viterbi algorithm is used to decode it. The model is tested on a data set yielded through the Volvo Drive Me project and collected by commercial vehicle sensors, including a GPS receiver, an inertial navigation system and a forward-looking camera. For the sake of simplicity, the RADAR and LiDAR sensors are excluded. Among the sensor data used, lane changes and the type of road lane markings as detected by the vehicle proves to be particularly important. Two metrics for evaluating model performance are proposed. The first metric is the recall, i.e. the fraction of correct matches. However, the lanes to which the observations are matched vary widely in length. Therefore, we introduce the path length error (PLE) as a complementary metric. As the name indicates, it considers the length of the incorrect routes. A naive matcher, that simply matches GPS coordinates to the closest lane, is used for benchmarking. Attaining 95% median recall and 3% median PLE, we conclude that our model is high-performing and robust to errors. For comparison, the naive matcher scores 77% median recall and 26% median PLE. Our model is however shown to struggle without reliable vision detections. It would therefore be meaningful to investigate the inclusion of additional vehicle sensors.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/256993
dc.language.isoeng
dc.relation.ispartofseriesMaster's thesis - Department of Mechanics and Maritime Sciences : 2019:52
dc.setspec.uppsokTechnology
dc.subjectInformations- och kommunikationsteknik
dc.subjectTransport
dc.subjectHållbar utveckling
dc.subjectInnovation och entreprenörskap (nyttiggörande)
dc.subjectSannolikhetsteori och statistik
dc.subjectDatavetenskap (datalogi)
dc.subjectSystemvetenskap, informationssystem och informatik
dc.subjectDatorseende och robotik (autonoma system)
dc.subjectFarkostteknik
dc.subjectInformation & Communication Technology
dc.subjectTransport
dc.subjectSustainable Development
dc.subjectInnovation & Entrepreneurship
dc.subjectProbability Theory and Statistics
dc.subjectComputer Science
dc.subjectInformation Systems
dc.subjectComputer Vision and Robotics (Autonomous Systems)
dc.subjectVehicle Engineering
dc.titleLane-Level Map Matching using Hidden Markov Models
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeComputer science – algorithms, languages and logic (MPALG), MSc
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