Understanding driving scenes and communicating automated vehicle decisions
are key requirements for trustworthy automated driving. In this article, we
introduce the Qualitative Explainable Graph (QXG), which is a unified symbolic
and qualitative representation for scene understanding in urban mobility. The
QXG enables interpreting an automated vehicle's environment using sensor data
and machine learning models. It utilizes spatio-temporal graphs and qualitative
constraints to extract scene semantics from raw sensor inputs, such as LiDAR
and camera data, offering an interpretable scene model. A QXG can be
incrementally constructed in real-time, making it a versatile tool for
in-vehicle explanations across various sensor types. Our research showcases the
potential of QXG, particularly in the context of automated driving, where it
can rationalize decisions by linking the graph with observed actions. These
explanations can serve diverse purposes, from informing passengers and alerting
vulnerable road users to enabling post-hoc analysis of prior behaviors.

自动驾驶中使用的定性可解释图 (QXG) 能够通过传感器数据和机器学习模型解释自动驾驶车辆的环境，利用时空图和定性约束从原始传感器输入中提取场景语义，支持实时增量构建，可用于车内解释和决策合理化。

通过定性场景理解和解释实现可靠的自动驾驶

Towards Trustworthy Automated Driving through Qualitative Scene  Understanding and Explanations

We present the Qualitative Explainable Graph (QXG): a unified symbolic and
qualitative representation for scene understanding in urban mobility. QXG
enables the interpretation of an automated vehicle's environment using sensor
data and machine learning models. It leverages spatio-temporal graphs and
qualitative constraints to extract scene semantics from raw sensor inputs, such
as LiDAR and camera data, offering an intelligible scene model. Crucially, QXG
can be incrementally constructed in real-time, making it a versatile tool for
in-vehicle explanations and real-time decision-making across various sensor
types. Our research showcases the transformative potential of QXG, particularly
in the context of automated driving, where it elucidates decision rationales by
linking the graph with vehicle actions. These explanations serve diverse
purposes, from informing passengers and alerting vulnerable road users (VRUs)
to enabling post-analysis of prior behaviours.

我们提出了定性可解释图（QXG）：一种用于城市移动中场景理解的统一符号和定性表示。QXG 利用时空图和定性约束从原始传感器输入（如 LiDAR 和相机数据）中提取场景语义，为自动车辆的环境进行解读，从而提供了一种可理解的场景模型。QXG 具备实时递增构建的能力，使其成为针对不同类型传感器的车内解释和实时决策的多功能工具。我们的研究展示了 QXG 的转型潜力，特别是在自动驾驶的背景下，通过将图与车辆行为相连接，阐明决策原理。这些解释服务于多种目的，从通知乘客和警示弱势道路使用者（VRUs），到促进先前行为的后续分析。

通过定性场景理解和解释实现可信的自动驾驶

Trustworthy Automated Driving through Qualitative Scene Understanding  and Explanations

The future of automated driving (AD) is rooted in the development of robust,
fair and explainable artificial intelligence methods. Upon request, automated
vehicles must be able to explain their decisions to the driver and the car
passengers, to the pedestrians and other vulnerable road users and potentially
to external auditors in case of accidents. However, nowadays, most explainable
methods still rely on quantitative analysis of the AD scene representations
captured by multiple sensors. This paper proposes a novel representation of AD
scenes, called Qualitative eXplainable Graph (QXG), dedicated to qualitative
spatiotemporal reasoning of long-term scenes. The construction of this graph
exploits the recent Qualitative Constraint Acquisition paradigm. Our
experimental results on NuScenes, an open real-world multi-modal dataset, show
that the qualitative eXplainable graph of an AD scene composed of 40 frames can
be computed in real-time and light in space storage which makes it a
potentially interesting tool for improved and more trustworthy perception and
control processes in AD.

汽车自动驾驶中的鲁棒公平可解释人工智能方法的发展是其未来的基础。本文提出了一种名为 Qualitative eXplainable Graph（QXG）的汽车自动驾驶场景的新表达方法，用于对长期场景进行定性时空推理。实验证明，这种定性可解释图的实时计算和轻量级存储为改进和更可信赖的感知与控制过程提供了潜在的有趣工具。