| No. | 配信 | タイトル・著者（所属） |
|---|---|---|
| 1 | ◯ |
燃料電池ごみ収集車の運行モード分類におけるデータ駆動型弱教師学習手法の提案 鮑 義達・張 翔・方 亦园・楊 イ翔・紙屋 雄史（早稲田大学） 本研究はFCごみ収集車の運行モード分類と統計分析に向け，知見を融合した弱教師深層学習を提案する．学習データの自動生成によりアノテーションコストを75%削減しつつ高精度な分類を実現した．本手法は膨大な実走行CANデータの効率的解析を可能にし，次世代商用車の設計・制御策に寄与する基盤技術になり得ると考える． |
| 2 | ◯ |
自律走行車における「予期機構」の構造 伊藤 昌夫（ニルソフトウェア） これまでの自律走行車と倫理に関する研究から，「予期機構（Anticipation Mechanism, AM）」が，安全性にとって重要であることが明らかになった．また，シナリオベースのテスト負荷を軽減する可能性がある．このAMを，自律走行車に組み込むパターンについて検討した結果を示す． |
| 3 | ◯ |
PhysicsAI: Accelerating Automotive Design with Graph Neural Network-Based CFD and NVH Engineering Son Tong･Marc Brughmans･Andrey Hense･Lester Deleon･Theo Geluk (Siemens Digital Industries Software) PhysicsAI delivers fast physics predictions enabling engineering teams to generate design variations rapidly. PhysicsAI learns physics behavior using Graph Neural Networks (GNNs) trained on mesh geometries and CAD models data. Engineers can explore various design variations, optimize parameters, and accelerate innovation. We present two applications: (1) External aerodynamic drag prediction using CFD simulation data, achieving high accuracy while reducing computation time from hours to minutes; (2) Vibration mode shape recognition and classification for NVH optimization, demonstrating expert-level accuracy on complex automotive structures. Validation from comprehensive automotive datasets will be presented. |
| 4 | ◯ |
Considerations regarding the safety assurance of AI-based Automated Driving Systems Olaf OP DEN CAMP･Jan-Pieter Paardekooper (TNO) In the development of Automated Driving Systems manufacturers and AV-developers make more and more use of AI-based systems. In some cases, even an end-to-end (E2E) AI approach is followed in which no longer a distinction is made between perception, path planning and actuation in the ADS of the vehicle. The paper presents considerations regarding the safety assurance of AI-based systems. The vulnerabilities of AI-based systems and the negative impact of these vulnerabilities on safety assurance will be discussed. It will be shown how the design of AI-based systems can be improved to allow for proper safety assurance. |