With the rise of deep learning and intelligent vehicles, the smart assistant
has become an essential in-car component to facilitate driving and provide
extra functionalities. In-car smart assistants should be able to process
general as well as car-related commands and perform corresponding actions,
which eases driving and improves safety. However, in this research field, most
datasets are in major languages, such as English and Chinese. There is a huge
data scarcity issue for low-resource languages, hindering the development of
research and applications for broader communities. Therefore, it is crucial to
have more benchmarks to raise awareness and motivate the research in
low-resource languages. To mitigate this problem, we collect a new dataset,
namely Cantonese In-car Audio-Visual Speech Recognition (CI-AVSR), for in-car
speech recognition in the Cantonese language with video and audio data.
Together with it, we propose Cantonese Audio-Visual Speech Recognition for
In-car Commands as a new challenge for the community to tackle low-resource
speech recognition under in-car scenarios.

研究智能车辆的深度学习与智能化，提供车辆相关信息和增强性能至关重要。然而目前，这个领域的大部分数据集往往只有英语和中文存在，这妨碍了更广泛社群的研究与应用。为了解决这一问题，本文提出了一种应对低资源语言识别问题的方法，即使用视听数据进行广东话语音识别。

Kaggle 竞赛：用于车内指令的粤语视听语音识别

Kaggle Competition: Cantonese Audio-Visual Speech Recognition for In-car Commands

In this paper, we report on the visualization capabilities of an Explainable
AI Planning (XAIP) agent that can support human in the loop decision making.
Imposing transparency and explainability requirements on such agents is
especially important in order to establish trust and common ground with the
end-to-end automated planning system. Visualizing the agent's internal
decision-making processes is a crucial step towards achieving this. This may
include externalizing the "brain" of the agent -- starting from its sensory
inputs, to progressively higher order decisions made by it in order to drive
its planning components. We also show how the planner can bootstrap on the
latest techniques in explainable planning to cast plan visualization as a plan
explanation problem, and thus provide concise model-based visualization of its
plans. We demonstrate these functionalities in the context of the automated
planning components of a smart assistant in an instrumented meeting space.

本文介绍了一种可视化 Explainable AI Planning 代理的能力，该代理可以支持人们做出决策，并展示了如何将规划可视化作为规划解释问题来快速提供其计划的模型可视化。