With growing complexity and criticality of automated driving functions in
road traffic and their operational design domains (ODD), there is increasing
demand for covering significant proportions of development, validation, and
verification in virtual environments and through simulation models.
If, however, simulations are meant not only to augment real-world
experiments, but to replace them, quantitative approaches are required that
measure to what degree and under which preconditions simulation models
adequately represent reality, and thus, using their results accordingly.
Especially in R&D areas related to the safety impact of the "open world", there
is a significant shortage of real-world data to parameterize and/or validate
simulations - especially with respect to the behavior of human traffic
participants, whom automated driving functions will meet in mixed traffic.
We present an approach to systematically acquire data in public traffic by
heterogeneous means, transform it into a unified representation, and use it to
automatically parameterize traffic behavior models for use in data-driven
virtual validation of automated driving functions.

增长的自动驾驶功能的复杂性和重要性，导致了对在虚拟环境和模拟模型中覆盖开发、验证和验证的显著比例的需求。我们提出了一种系统获取公共交通数据的方法，通过异构手段转换为统一表示，并用它来自动参数化交通行为模型，以用于数据驱动的自动驾驶功能的虚拟验证。

系统数据采集与数据驱动仿真方法用于自动驾驶功能的安全测试

An Approach to Systematic Data Acquisition and Data-Driven Simulation  for the Safety Testing of Automated Driving Functions

Autonomous Vehicles (AVs) have the potential to provide numerous societal
benefits, such as decreased road accidents and increased overall transportation
efficiency. However, quantifying the risk associated with AVs is challenging
due to the lack of historical data and the rapidly evolving technology. This
paper presents a data-driven framework for comparing the risk of different AVs'
behaviors in various operational design domains (ODDs), based on counterfactual
simulations of "misbehaving" road users. We introduce the concept of
counterfactual safety margin, which represents the minimum deviation from
normal behavior that could lead to a collision. This concept helps to find the
most critical scenarios but also to assess the frequency and severity of risk
of AVs. We show that the proposed methodology is applicable even when the AV's
behavioral policy is unknown -- through worst- and best-case analyses -- making
the method useful also to external third-party risk assessors. Our experimental
results demonstrate the correlation between the safety margin, the driving
policy quality, and the ODD shedding light on the relative risk associated with
different AV providers. This work contributes to AV safety assessment and aids
in addressing legislative and insurance concerns surrounding this emerging
technology.

自动驾驶车辆的风险评估是一项具有挑战性的任务，本文提出了一种基于数据驱动的框架，通过对 “不当行为” 道路用户的对照模拟来比较不同自动驾驶车辆在不同操作设计领域的风险。引入了反事实安全余量的概念，用于表示最小偏差与正常行为之间可能导致碰撞的关系，展示了该方法在未知行为策略下仍可适用，从而有助于第三方风险评估，并在自动驾驶车辆供应商之间的相对风险方面提供了洞察。

关于自动驾驶车辆风险评分的反事实安全边界视角

A Counterfactual Safety Margin Perspective on the Scoring of Autonomous  Vehicles' Riskiness

We give a first rigorous characterization of Operational Design Domains
(ODDs) for Machine Learning (ML)-based aeronautical products. Unlike in other
application sectors (such as self-driving road vehicles) where ODD development
is scenario-based, our approach is data-centric: we propose the dimensions
along which the parameters that define an ODD can be explicitly captured,
together with a categorization of the data that ML-based applications can
encounter in operation, whilst identifying their system-level relevance and
impact. Specifically, we discuss how those data categories are useful to
determine: the requirements necessary to drive the design of ML Models (MLMs);
the potential effects on MLMs and higher levels of the system hierarchy; the
learning assurance processes that may be needed, and system architectural
considerations. We illustrate the underlying concepts with an example of an
aircraft flight envelope.

研究 ML 技术在航空产品中的应用，提出数据为中心的 ODD 定义方法，解释了这些数据对 ML 模型构建、系统层次影响、学习保证过程及系统架构等的作用。