Given the rapid advance in ITS technologies, future mobility is pointing to
vehicular autonomy. However, there is still a long way before full automation,
and human intervention is required. This work sheds light on understanding
human drivers' intervention behavior involved in the operation of autonomous
vehicles (AVs) and utilizes this knowledge to improve the perception of
critical driving scenarios. Experiment environments were implemented where the
virtual reality (VR) and traffic micro-simulation are integrated, and tests
were carried out under typical and diverse traffic scenes. Performance
indicators such as the probability of intervention, accident rates are defined
and used to quantify and compare the risk levels. By offering novel insights
into drivers' intervention behavior, this work will help improve the
performances of the automated control under similar scenarios. Furthermore,
such an integrated and immersive tool for autonomous driving studies will be
valuable for research on human-to-automation trust. To the best knowledge of
the authors, this work is among the pioneer works making efforts into such
types of tools.

该研究通过实施虚拟现实和交通微模拟集成的实验环境，并在典型和多样化的交通场景下进行测试，提出了驾驶员介入行为的新视角，从而改善自动驾驶在类似情景下的表现，并为人与自动化系统之间的信任关系研究提供了有价值的综合与沉浸式工具。

自动驾驶中驾驶员干预行为分析 —— 一种融入 VR 的方法

Analyze Drivers' Intervention Behavior During Autonomous Driving -- A  VR-incorporated Approach

Human drivers can seamlessly adapt their driving decisions across
geographical locations with diverse conditions and rules of the road, e.g.,
left vs. right-hand traffic. In contrast, existing models for autonomous
driving have been thus far only deployed within restricted operational domains,
i.e., without accounting for varying driving behaviors across locations or
model scalability. In this work, we propose AnyD, a single geographically-aware
conditional imitation learning (CIL) model that can efficiently learn from
heterogeneous and globally distributed data with dynamic environmental,
traffic, and social characteristics. Our key insight is to introduce a
high-capacity geo-location-based channel attention mechanism that effectively
adapts to local nuances while also flexibly modeling similarities among regions
in a data-driven manner. By optimizing a contrastive imitation objective, our
proposed approach can efficiently scale across inherently imbalanced data
distributions and location-dependent events. We demonstrate the benefits of our
AnyD agent across multiple datasets, cities, and scalable deployment paradigms,
i.e., centralized, semi-supervised, and distributed agent training.
Specifically, AnyD outperforms CIL baselines by over 14% in open-loop
evaluation and 30% in closed-loop testing on CARLA.

提出了一个地理感知的条件模仿学习模型 AnyD，能够有效地从多样性和全球分布的数据中学习，适应局部细微差异以及在数据驱动的方式下建模区域间的相似性，并在多个数据集、城市和可扩展的部署范式下取得显著性能改进。