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Synthetic versus real: an analysis of critical scenarios for autonomous vehicle testing

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Názov: Synthetic versus real: an analysis of critical scenarios for autonomous vehicle testing
Autori: Song, Qunying, Bensoussan, Avner, Mousavi, Mohammad Reza
Prispievatelia: Lund University, Faculty of Engineering, LTH, Departments at LTH, Department of Computer Science, Software Engineering Research Group, Lunds universitet, Lunds Tekniska Högskola, Institutioner vid LTH, Institutionen för datavetenskap, Programvarusystem, Originator, Lund University, Faculty of Engineering, LTH, LTH Profile areas, LTH Profile Area: AI and Digitalization, Lunds universitet, Lunds Tekniska Högskola, LTH profilområden, LTH profilområde: AI och digitalisering, Originator, Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), ELLIIT: the Linköping-Lund initiative on IT and mobile communication, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), ELLIIT: the Linköping-Lund initiative on IT and mobile communication, Originator, Lund University, Faculty of Engineering, LTH, Competence centers, LTH, NEXTG2COM – a Vinnova Competence Centre in Advanced Digitalisation, Lunds universitet, Lunds Tekniska Högskola, Kompetenscentrum, LTH, NEXTG2COM – ett Vinnova kompetenscenter inom Avancerad Digitalisering, Originator
Zdroj: Automated Software Engineering. 32(2)
Predmety: Engineering and Technology, Electrical Engineering, Electronic Engineering, Information Engineering, Robotics and automation, Teknik, Elektroteknik och elektronik, Robotik och automation
Popis: With the emergence of autonomous vehicles comes the requirement of adequate and rigorous testing, particularly in critical scenarios that are both challenging and potentially hazardous. Generating synthetic simulation-based critical scenarios for testing autonomous vehicles has therefore received considerable interest, yet it is unclear how such scenarios relate to the actual crash or near-crash scenarios in the real world. Consequently, their realism is unknown. In this paper, we define realism as the degree of similarity of synthetic critical scenarios to real-world critical scenarios. We propose a methodology to measure realism using two metrics, namely attribute distribution and Euclidean distance. The methodology extracts various attributes from synthetic and realistic critical scenario datasets and performs a set of statistical tests to compare their distributions and distances. As a proof of concept for our methodology, we compare synthetic collision scenarios from DeepScenario against realistic autonomous vehicle collisions collected by the Department of Motor Vehicles in California, to analyse how well DeepScenario synthetic collision scenarios are aligned with real autonomous vehicle collisions recorded in California. We focus on five key attributes that are extractable from both datasets, and analyse the attribution distribution and distance between scenarios in the two datasets. Further, we derive recommendations to improve the realism of synthetic scenarios based on our analysis. Our study of realism provides a framework that can be replicated and extended for other dataset both concerning real-world and synthetically-generated scenarios.
Prístupová URL adresa: https://doi.org/10.1007/s10515-025-00499-4
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  Data: Synthetic versus real: an analysis of critical scenarios for autonomous vehicle testing
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  Data: With the emergence of autonomous vehicles comes the requirement of adequate and rigorous testing, particularly in critical scenarios that are both challenging and potentially hazardous. Generating synthetic simulation-based critical scenarios for testing autonomous vehicles has therefore received considerable interest, yet it is unclear how such scenarios relate to the actual crash or near-crash scenarios in the real world. Consequently, their realism is unknown. In this paper, we define realism as the degree of similarity of synthetic critical scenarios to real-world critical scenarios. We propose a methodology to measure realism using two metrics, namely attribute distribution and Euclidean distance. The methodology extracts various attributes from synthetic and realistic critical scenario datasets and performs a set of statistical tests to compare their distributions and distances. As a proof of concept for our methodology, we compare synthetic collision scenarios from DeepScenario against realistic autonomous vehicle collisions collected by the Department of Motor Vehicles in California, to analyse how well DeepScenario synthetic collision scenarios are aligned with real autonomous vehicle collisions recorded in California. We focus on five key attributes that are extractable from both datasets, and analyse the attribution distribution and distance between scenarios in the two datasets. Further, we derive recommendations to improve the realism of synthetic scenarios based on our analysis. Our study of realism provides a framework that can be replicated and extended for other dataset both concerning real-world and synthetically-generated scenarios.
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