Weather Condition Estimation in Automated Vehicles

Master thesis (2018)

Authors

J.C.H. Wymenga Mechanical Engineering

Contributors

J.F.M. Domhof (mentor)
D. Gavrila (graduation committee member)
J.F.P. Kooij (graduation committee member)
J. Kober (graduation committee member)
Faculty
Mechanical Engineering, Mechanical Engineering
Copyright: © 2018 Jan Wymenga
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Published Date

15-05-2018

Language

English

Reuse Rights

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Faculty

Mechanical Engineering

Abstract

This work presents a multi-sensor approach for weather condition estimation in automated vehicles. Using combined data from weather sensors (barometer, hygrometer, etc) and an in-vehicle camera, a machine learning and computer vision framework is employed to estimate the current weather condition in realtime and in-vehicle. The use of different sensor types is shown to improve robustness and reduce noise. The resulting modular framework allows it to be used with different sensor configurations, and allows changes in sensor configuration with minimal effort. Finally, a proof-of-concept experiment is presented; a dataset is recorded using a test vehicle and used for model evaluation.
The resulting datasets contains 20.000 pairs of video frames and sensor measurements recorded in different weather situations.

Files

Final_report.pdf
(.pdf | 6.81 Mb)