Scenario-based testing of automated driving functions has become a promising
method to reduce time and cost compared to real-world testing. In
scenario-based testing automated functions are evaluated in a set of
pre-defined scenarios. These scenarios provide information about vehicle
behaviors, environmental conditions, or road characteristics using parameters.
To create realistic scenarios, parameters and parameter dependencies have to be
fitted utilizing real-world data. However, due to the large variety of
intersections and movement constellations found in reality, data may not be
available for certain scenarios. This paper proposes a methodology to
systematically analyze relations between parameters of scenarios. Bayesian
networks are utilized to analyze causal dependencies in order to decrease the
amount of required data and to transfer causal patterns creating unseen
scenarios. Thereby, infrastructural influences on movement patterns are
investigated to generate realistic scenarios on unobserved intersections. For
evaluation, scenarios and underlying parameters are extracted from the inD
dataset. Movement patterns are estimated, transferred and checked against
recorded data from those initially unseen intersections.

通过利用贝叶斯网络分析各个参数的关系，本文提出了一种系统的方法来降低对数据的需求量，并在未观测到的交叉口生成真实的场景，以此评估自动驾驶功能的场景测试。

基于因果关系的驾驶场景转移到未知交叉口

Causality-based Transfer of Driving Scenarios to Unseen Intersections

Ensuring validation for highly automated driving poses significant obstacles
to the widespread adoption of highly automated vehicles. Scenario-based testing
offers a potential solution by reducing the homologation effort required for
these systems. However, a crucial prerequisite, yet unresolved, is the
definition and reduction of the test space to a finite number of scenarios. To
tackle this challenge, we propose an extension to a contrastive learning
approach utilizing graphs to construct a meaningful embedding space. Our
approach demonstrates the continuous mapping of scenes using scene-specific
features and the formation of thematically similar clusters based on the
resulting embeddings. Based on the found clusters, similar scenes could be
identified in the subsequent test process, which can lead to a reduction in
redundant test runs.

通过使用图形来构建一个有意义的嵌入空间，我们的方法展示了场景的连续映射以及基于嵌入结果形成主题相似群集，从而在后续测试过程中识别相似场景，从而减少冗余测试运行。

交通场景相似度：基于图形对比学习的方法

Traffic Scene Similarity: a Graph-based Contrastive Learning Approach

Interaction between the background vehicles (BVs) and automated vehicles
(AVs) in scenario-based testing plays a critical role in evaluating the
intelligence of the AVs. Current testing scenarios typically employ predefined
or scripted BVs, which inadequately reflect the complexity of human-like social
behaviors in real-world driving scenarios, and also lack a systematic metric
for evaluating the comprehensive intelligence of AVs. Therefore, this paper
proposes an evolving scenario generation method that utilizes deep
reinforcement learning (DRL) to create human-like BVs for testing and
intelligence evaluation of AVs. Firstly, a class of driver models with
human-like competitive, cooperative, and mutual driving motivations is
designed. Then, utilizing an improved "level-k" training procedure, the three
distinct driver models acquire game-based interactive driving policies. And
these models are assigned to BVs for generating evolving scenarios in which all
BVs can interact continuously and evolve diverse contents. Next, a framework
including safety, driving efficiency, and interaction utility are presented to
evaluate and quantify the intelligence performance of 3 systems under test
(SUTs), indicating the effectiveness of the evolving scenario for intelligence
testing. Finally, the complexity and fidelity of the proposed evolving testing
scenario are validated. The results demonstrate that the proposed evolving
scenario exhibits the highest level of complexity compared to other baseline
scenarios and has more than 85% similarity to naturalistic driving data. This
highlights the potential of the proposed method to facilitate the development
and evaluation of high-level AVs in a realistic and challenging environment.

该研究旨在利用深度强化学习来创建人类行为类似的背景车辆，从而评估自动驾驶汽车的智能表现，并提出了一个包括安全、驾驶效率和交互效用的框架，用于评价和量化三个系统的智能绩效。

自动驾驶汽车演进测试场景生成方法和智能评估框架

Evolving Testing Scenario Generation Method and Intelligence Evaluation  Framework for Automated Vehicles

Scenario-based testing for the safety validation of highly automated vehicles
is a promising approach that is being examined in research and industry. This
approach heavily relies on data from real-world scenarios to derive the
necessary scenario information for testing. Measurement data should be
collected at a reasonable effort, contain naturalistic behavior of road users
and include all data relevant for a description of the identified scenarios in
sufficient quality. However, the current measurement methods fail to meet at
least one of the requirements. Thus, we propose a novel method to measure data
from an aerial perspective for scenario-based validation fulfilling the
mentioned requirements. Furthermore, we provide a large-scale naturalistic
vehicle trajectory dataset from German highways called highD. We evaluate the
data in terms of quantity, variety and contained scenarios. Our dataset
consists of 16.5 hours of measurements from six locations with 110 000
vehicles, a total driven distance of 45 000 km and 5600 recorded complete lane
changes. The highD dataset is available online at: this http URL

提出了一种从空中视角测量数据以满足安全验证需求的新方法，基于从德国高速公路收集的大规模真实车辆轨迹数据集高 D，从数量、多样性和场景等方面进行评估。