Yamazaki, Yuji, Tamura, Yasumasa, Defago, Xavier, Javanmardi, Ehsan, Tsukada, Manabu, "ToST: Tokyo SUMO traffic scenario ", In: The 26th edition of the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023), Bilbao, Bizkaia, Spain, 2023.Proceedings Article | Abstract | Links | BibTeX
@inproceedings{Yamazaki2023,
title = {ToST: Tokyo SUMO traffic scenario },
author = {Yuji Yamazaki and Yasumasa Tamura and Xavier Defago and Ehsan Javanmardi and Manabu Tsukada},
url = {https://github.com/dfg-lab/ToSTScenario},
doi = {10.1109/ITSC57777.2023.10422517},
year = {2023},
date = {2023-09-24},
urldate = {2023-09-24},
booktitle = {The 26th edition of the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023)},
address = {Bilbao, Bizkaia, Spain},
abstract = {In recent years, research, development, and demonstrations aimed at the societal implementation of autonomous driving have attracted increasing attention. Localization, which involves obtaining information of the surrounding environment from sensor data and estimating the vehicle's position, is necessary for realizing autonomous driving. Localization is commonly performed with 3D LiDAR as a sensor owing to its high measurement accuracy and immunity to ambient light conditions, which allow for precise localization. However, when the surrounding area has distinctive features, localization accuracy may decrease. In this study, we proposed a method based on deep learning to predict the localization accuracy for autonomous driving. The overall localization accuracy can be improved by predicting the accuracy of localization using other sensors, such as GNSS and IMU, or pavement markings in areas with poor accuracy. We created a dataset for predicting the localization accuracy using an open-source autonomous driving simulator. In an experiment, we applied the proposed method to the created dataset. Thresholds were set for errors in the x-direction, y-direction, and distance for localization. Predictions with high accuracy and F-values were obtained. The results indicate that the proposed method can accurately predict the localization accuracy. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In recent years, research, development, and demonstrations aimed at the societal implementation of autonomous driving have attracted increasing attention. Localization, which involves obtaining information of the surrounding environment from sensor data and estimating the vehicle's position, is necessary for realizing autonomous driving. Localization is commonly performed with 3D LiDAR as a sensor owing to its high measurement accuracy and immunity to ambient light conditions, which allow for precise localization. However, when the surrounding area has distinctive features, localization accuracy may decrease. In this study, we proposed a method based on deep learning to predict the localization accuracy for autonomous driving. The overall localization accuracy can be improved by predicting the accuracy of localization using other sensors, such as GNSS and IMU, or pavement markings in areas with poor accuracy. We created a dataset for predicting the localization accuracy using an open-source autonomous driving simulator. In an experiment, we applied the proposed method to the created dataset. Thresholds were set for errors in the x-direction, y-direction, and distance for localization. Predictions with high accuracy and F-values were obtained. The results indicate that the proposed method can accurately predict the localization accuracy.
