SAX：困難な実世界の運転シナリオで自動運転車への信頼を築く
Sense-Assess-eXplain (SAX): Building Trust in Autonomous Vehicles in Challenging Real-World Driving Scenarios
自動運転システムの保証と規制の障壁を克服するため、環境を頑健に認識し、自己能力を評価し、その解釈と評価の因果的説明を提供するロボットの研究開発を実世界で実証する取り組みを述べた論文。
著者: Matthew Gadd, Daniele De Martini, Letizia Marchegiani, Paul Newman, Lars Kunze
分類: cs.CY, cs.RO
原文アブストラクト
This paper discusses ongoing work in demonstrating research in mobile autonomy in challenging driving scenarios. In our approach, we address fundamental technical issues to overcome critical barriers to assurance and regulation for large-scale deployments of autonomous systems. To this end, we present how we build robots that (1) can robustly sense and interpret their environment using traditional as well as unconventional sensors; (2) can assess their own capabilities; and (3), vitally in the purpose of assurance and trust, can provide causal explanations of their interpretations and assessments. As it is essential that robots are safe and trusted, we design, develop, and demonstrate fundamental technologies in real-world applications to overcome critical barriers which impede the current deployment of robots in economically and socially important areas. Finally, we describe ongoing work in the collection of an unusual, rare, and highly valuable dataset.
