より安全な都市に向けて：都市走行シナリオにおける知覚データセットの設計プロセス
Towards Safer Cities: A Design Process for Perception Datasets in Urban Driving Scenarios
都市環境における自動運転の知覚システム向けに、ユーザー生成地図データを活用して既存データセットを分析し、トラック特有のユースケースを特定することで、データセット作成を導く手法を提案した論文。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Alexander Kroth, Cristian Martinez, Steven Peters
分類: cs.RO
原文アブストラクト
Urban environments present significant challenges for the perception systems of automated driving due to their high complexity, characterized by dense traffic, diverse obstacles, or a wide range of possible scenarios. While numerous automotive datasets exist, few focus on urban settings, and the majority are acquired exclusively on passenger cars and overlook other transport modalities such as heavy-duty trucks, busses and trams. In contrast to passenger vehicles, these platforms operate under different geographical and operational constraints; including traversing pedestrian zones or platform-specific tasks such as movement of trailer or winter service. In this work a method is proposed for a guided dataset creation based on an analysis of existing datasets using user-generated map data. Furthermore, representative truck-specific use cases are identified and exemplified to demonstrate the applicability of the proposed methodology and to support targeted acquisition planning.