| No. | 配信 | タイトル・著者（所属） |
|---|---|---|
| 1 | ◯ |
自動運転車両のデジタルツイン試験および認証の標準規格 庄井 美章（ASAM） 自動運転車両の試験および認証においては，実走行とシミュレーションを組み合わせた「デジタルツイン」アプローチが進展している．本研究では，このアプローチの実現に不可欠な標準規格群であるASAM OpenXについて，その目的，導入効果，今後の展望を概説するとともに，標準化手法についても述べる． |
| 2 | ◯ |
ADAS ECUとメータ表示の整合性評価システム 髙野 博幸（日産自動車）・ANHTUAN NGO・DUCPHONG NGUYEN（日産オートモーティブテクノロジーベトナム）・木田 知宏（不動技研工業）・丸田 大介（アクセンチュア）・常門 茂徳（日産自動車） ADAS ECU信号とメータ表示の整合性を自動で評価するHILシステムを開発した．カメラで取得したアイコン画像を画像認識し，ECU信号と同期して比較・判定する．メータ表示の多様複雑な表現を信号化しECU信号と同期比較することで単純化した．車両検証工程に投入し評価を行いテスト工数を削減した． |
| 3 | ◯ |
A Comprehensive Pipeline for Scenario Extraction from Open-Road Data for ADS Validation Marc Perez･Marc Facerias (Applus+ IDIADA) As automated driving systems (ADS) become increasingly complex, traditional testing methods are no longer sufficient to capture the full variability of real-world traffic. This study presents a comprehensive pipeline for extracting scenarios from open-road data, encompassing data acquisition, perception, scenario identification, and the generation of scenario files compliant with ASAM OpenX standards. The pipeline was evaluated across two data-collection campaigns, allowing to validate the proposed approaches and generate preliminary results. These results serve as an initial contribution toward the development of a harmonized European scenario data space, supporting more robust scenario-based ADS validation. |
| 4 | ◯ |
Physically Replicated Weather Conditions to Evaluate Camera, Radar, and Lidar Object Detection in Automotive Scenarios Marc Perez (Applus+ IDIADA) Adverse weather conditions are one of the biggest challenges for large-scale automated driving deployment, due to reduced perception performance. We analyse how rain, fog, and snow affect object detection on cameras, lidars, and radars. We provide experimental results in proving grounds, open road and an enclosed facility with physically replicated controllable rain, fog, and lighting. We report perception and weather metrics, and we discuss some artifacts visible in the point clouds of physically replicated rain, providing actionable insights for the design and operation of future facilities for physically replicating weather conditions. |
| 5 | ◯ |
Integrity of Vehicle Localization for Highly Automated Driving Robin Smit･Emilia Silvas･Saarang Gaggar (Netherlands Organisation for Applied Scientific Research (TNO)) As automated driving advances from partial automation (SAE Level 2) to high automation (Level 4), the reliability and integrity of vehicle localization systems become increasingly critical. Accurate localization is essential for amongst others safe trajectory planning. Establishing robust upper bounds on localization errors is vital to ensure safety. This involves calculating probabilistic error limits that align with predefined risk thresholds. This presentation reviews the current state of integrity monitoring for vehicle localization in automated driving, highlights ongoing challenges and recent advancements, and outlines future research directions to support the safe deployment of highly automated vehicles. |
| 6 | ◯ |
仮想レベル4自動運転移動サービスへのISO 21448（SOTIF）適用に関する検討 大岩 美春・宮崎 義弘・福田 和良（日本自動車研究所） 現在，自動運転レベル4の移動サービスを想定したISO 21448（SOTIF) 適用の公開事例がなく，知見の共有が困難である．そこで本稿では，産業界に実践的知見を提供することを目的に仮想レベル4自動運転移動サービスに対するISO 21448（SOTIF) の適用事例を検討する． |