Lane-Level Map Matching using Hidden Markov Models

Typ
Examensarbete för masterexamen
Master Thesis
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Publicerad
2019
Författare
Korsberg, Ellen
Nordén, Eliza
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Sammanfattning
Map 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.
Beskrivning
Ämne/nyckelord
Informations- och kommunikationsteknik, Transport, Hållbar utveckling, Innovation och entreprenörskap (nyttiggörande), Sannolikhetsteori och statistik, Datavetenskap (datalogi), Systemvetenskap, informationssystem och informatik, Datorseende och robotik (autonoma system), Farkostteknik, Information & Communication Technology, Transport, Sustainable Development, Innovation & Entrepreneurship, Probability Theory and Statistics, Computer Science, Information Systems, Computer Vision and Robotics (Autonomous Systems), Vehicle Engineering
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