Automated driving has become a very popular topic in the recent years and is becoming more and more of a reality. In this new trend, High Definition (HD) maps play an important role in many ways that will provide a safer and more efficient driving experience, especially in terms of path planning and vehicle localization. Challenges and problems in HD maps data extraction, dataset creation, and data usage prediction are consequent on the developing of HD maps. One of the greatest challenges in automated driving is the ability to acquire, access and query the data pertaining to high resolution 3D objects from multiple heterogeneous sources. Specifically, the information extraction needs to be done by fusing data from both sensors and databases, and with real-time constraints. Existing structures and algorithmic approaches designed for regular maps - or even regular features in HD maps - are not capable to handle the various challenges. We take a further step towards providing an effective learning approach for the recently introduced problem of Predicting Map Data Consumption (PDMC) in the future time instants for a given trip. We propose a novel methodology that integrates multiple sources (road network, traffic, historic trips, HD maps) and, for a given trip, enables prediction of the map data consumption.

Creator

• Zang, Andi

DOI

• https://doi.org/10.21985/n2-wfsx-7a20

Subject

• Computer science

Language

• en

Alternate Identifier

• etdadmin_upload_879487
• http://dissertations.umi.com/northwestern:15935

Keyword

• Data Consumption
• Prediction
• Autonomous Driving
• High Definition Maps
• Machine Learning

Date created

• 2022-01-01

Resource type

• Dissertation

Rights statement

• In Copyright