Siamese Networks for Online Map Validation in Autonomous Driving
IV 2020 Workshop on Online Map Validation, 2020

Abstract
The work addresses map validation in autonomous vehicles by comparing map data against sensor readings using a deep learning classifier based on Siamese Network architecture. The approach reaches an F1 score of 89.1%, whereby misclassified scenes mostly stem from the limited variability in the training data.
Cite
F. Drost, L. Parolini, and S. Schneider, “Siamese Networks for Online Map Validation in Autonomous Driving,” in First Workshop on Online Map Validation and Road Model Creation, IEEE Intelligent Vehicles Symposium (IV), Oct. 2020. doi: 10.1109/iv47402.2020.9304642.
BibTeX
@inproceedings{drost2020,
author = {Drost, Felix and Parolini, Luca and Schneider, Sebastian},
booktitle = {First {Workshop} on {Online} {Map} {Validation} and {Road} {Model} {Creation}, {IEEE} {Intelligent} {Vehicles} {Symposium} ({IV})},
doi = {10.1109/iv47402.2020.9304642},
year = {2020},
month = {oct},
title = {Siamese {Networks} for {Online} {Map} {Validation} in {Autonomous} {Driving}},
}