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Summary & Details

BayesianURQ: Bayesian Approach for Unknown Risk Quantification on Highly Automated Driving

高レベル自動運転における未知リスクの定量化のためのベイズ的アプローチ

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Format: PDF

Price: Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880

Publication code: 20266217

Paper/Info type: Proceedings (Autumn)

No.137-26

Pages: 1-6(Total 6 p)

Date of publication: Oct 2026

Publisher: JSAE

Language: Japanese

Event: 2026 JSAE Annual Congress (Autumn)

Detailed Information

Author(J)	1) 横田剛典
Author(E)	1) Masanori Yokota
Affiliation(J)	1) NTTデータオートモビリジェンス研究所
Affiliation(E)	1) NTT Data Automobiligence Research Center
Abstract(J)	高レベル自動運転ではすべてのコンテキストや誘発条件を事前に想定しつくすことが難しく，安全論証において未知のコンテキストや誘発条件によるリスクを適正に推定し許容水準を満たしているかを評価することが求められる．本講演ではベイズ的アプローチによる未知のリスクの評価の方法について具体例を通じて紹介する． Translation
Abstract(E)	In highly automated driving, it is difficult to identify all contexts and triggering conditions in advance. Therefore, in safety arguments, it is necessary to appropriately estimate the risks arising from unknown contexts and triggering conditions and evaluate whether they meet acceptable levels. This presentation will introduce a method for evaluating unknown risks using a Bayesian approach, through concrete examples.

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