Problem
Research questions and friction points this paper is trying to address.
Automated Vehicles
Operational Data
Safety Assessment
In-Service Monitoring and Reporting (ISMR)
Scenario Catalogue
Using Automated Vehicles Operational Data to Confirm Safety and Anticipate Threats
🤖 AI Summary
研究通过收集自动驾驶系统实际运行数据来确认安全性和预测威胁，采用联合国经济委员会的最佳实践和类似领域的监管方法，以解决仅凭传统认证方案无法充分评估的安全问题。
📝 Abstract
European Union (EU) policymakers adopted revolutionary data collection provisions for Automated Driving Systems (ADS) in the recently approved regulation that allows driverless vehicles to be operated on public roads. The framework is inspired by best practices developed at the United Nations Economic Commission for Europe(UNECE) level: the In-Service Monitoring and Reporting (ISMR); and by similar operational data collection regulatory approaches in nuclear energy production and transportation fields. The collection of real-world data will enable the competent safety authorities to gather the information needed to confirm the homologation safety target. Safety-relevant driving scenarios discovered during the real-world operation of a given ADS can also be stored in a scenario catalogue to investigate how other ADS types might have addressed such a traffic conflict. Moreover, lessons learnt deriving from the data collected can be shared among original equipment manufacturers (OEMs) and safety authorities. Ultimately, the ISMR is recognised as a necessary tool to properly tackle the challenges associated with ADS safety assessment given the number of unknowns that might remain undisclosed by leveraging the traditional homologation validation scheme only.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Automated Vehicles
Operational Data
In-Service Monitoring and Reporting (ISMR)
Scenario Catalogue
Safety Assessment
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