著者

Yuki Kitsukawa Tatsuya Minami Yudai Yamazaki Junich Meguro Eijiro Takeuchi Yoshiki Ninomiya Shinpei Kato Masato Edahiro

出版者

Society of Automotive Engineers of Japan, INC

雑誌

International Journal of Automotive Engineering (ISSN:21850984)

巻号頁・発行日

vol.13, no.4, pp.206-213, 2022 (Released:2022-11-22)

参考文献数

18

被引用文献数

2

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ABSTRACT: Ego-vehicle localization is a critical technology in autonomous driving systems, and one of the widely used methods for localization is scan matching between a 3D map and real-time LiDAR scan. This method is known to fail due to factors such as an incorrect initial position and orientation for scan matching. In this paper, we propose a simulator-based localization evaluation framework to verify the robustness of localization. By using a simulator, localization can be evaluated without driving a real vehicle, and can be evaluated by creating disturbances such as traffic jams. Our framework also allows to evaluate the robustness of localization by using multiple particles with random errors of the initial position and orientation for scan matching to simulate dead reckoning errors caused by multiple factors such as road surface conditions and tire diameter. In the evaluation experiments, we confirmed that the robustness of localization can be evaluated by applying this method to factors such as sensor setup, disturbances in the traffic environment, and the amount of 3D features in the environment.

著者

Akihiko Wada Yuya Saito Shohei Fujita Ryusuke Irie Toshiaki Akashi Katsuhiro Sano Shinpei Kato Yutaka Ikenouchi Akifumi Hagiwara Kanako Sato Nobuo Tomizawa Yayoi Hayakawa Junko Kikuta Koji Kamagata Michimasa Suzuki Masaaki Hori Atsushi Nakanishi Shigeki Aoki

出版者

Japanese Society for Magnetic Resonance in Medicine

雑誌

Magnetic Resonance in Medical Sciences (ISSN:13473182)

巻号頁・発行日

pp.mp.2021-0068, (Released:2021-12-10)

参考文献数

32

被引用文献数

5

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Purpose: Myelination-related MR signal changes in white matter are helpful for assessing normal development in infants and children. A rule-based myelination evaluation workflow regarding signal changes on T1-weighted images (T1WIs) and T2-weighted images (T2WIs) has been widely used in radiology. This study aimed to simulate a rule-based workflow using a stacked deep learning model and evaluate age estimation accuracy.Methods: The age estimation system involved two stacked neural networks: a target network-to extract five myelination-related images from the whole brain, and an age estimation network from extracted T1- and T2WIs separately. A dataset was constructed from 119 children aged below 2 years with two MRI systems. A four-fold cross-validation method was adopted. The correlation coefficient (CC), mean absolute error (MAE), and root mean squared error (RMSE) of the corrected chronological age of full-term birth, as well as the mean difference and the upper and lower limits of 95% agreement, were measured. Generalization performance was assessed using datasets acquired from different MR images. Age estimation was performed in Sturge–Weber syndrome (SWS) cases.Results: There was a strong correlation between estimated age and corrected chronological age (MAE: 0.98 months; RMSE: 1.27 months; and CC: 0.99). The mean difference and standard deviation (SD) were −0.15 and 1.26, respectively, and the upper and lower limits of 95% agreement were 2.33 and −2.63 months. Regarding generalization performance, the performance values on the external dataset were MAE of 1.85 months, RMSE of 2.59 months, and CC of 0.93. Among 13 SWS cases, 7 exceeded the limits of 95% agreement, and a proportional bias of age estimation based on myelination acceleration was exhibited below 12 months of age (P = 0.03).Conclusion: Stacked deep learning models automated the rule-based workflow in radiology and achieved highly accurate age estimation in infants and children up to 2 years of age.