Autonomous vehicles often make complex decisions via machine learning-based
predictive models applied to collected sensor data. While this combination of
methods provides a foundation for real-time actions, self-driving behavior
primarily remains opaque to end users. In this sense, explainability of
real-time decisions is a crucial and natural requirement for building trust in
autonomous vehicles. Moreover, as autonomous vehicles still cause serious
traffic accidents for various reasons, timely conveyance of upcoming hazards to
road users can help improve scene understanding and prevent potential risks.
Hence, there is also a need to supply autonomous vehicles with user-friendly
interfaces for effective human-machine teaming. Motivated by this problem, we
study the role of explainable AI and human-machine interface jointly in
building trust in vehicle autonomy. We first present a broad context of the
explanatory human-machine systems with the "3W1H" (what, whom, when, how)
approach. Based on these findings, we present a situation awareness framework
for calibrating users' trust in self-driving behavior. Finally, we perform an
experiment on our framework, conduct a user study on it, and validate the
empirical findings with hypothesis testing.

自动驾驶汽车中人工智能的可解释性和人机界面对于建立信任起到重要作用，因此本研究探讨了可解释性人机系统的作用，提出了一种评估用户对自动驾驶行为的信任的情境感知框架，并通过实验证明了相关的经验结果。

将解释引入自动驾驶车辆的人机界面以提升信任和情景感知

Incorporating Explanations into Human-Machine Interfaces for Trust and  Situation Awareness in Autonomous Vehicles

In complex industrial and chemical process control rooms, effective
decision-making is crucial for safety and effi- ciency. The experiments in this
paper evaluate the impact and applications of an AI-based decision support
system integrated into an improved human-machine interface, using dynamic
influ- ence diagrams, a hidden Markov model, and deep reinforcement learning.
The enhanced support system aims to reduce operator workload, improve
situational awareness, and provide different intervention strategies to the
operator adapted to the current state of both the system and human performance.
Such a system can be particularly useful in cases of information overload when
many alarms and inputs are presented all within the same time window, or for
junior operators during training. A comprehensive cross-data analysis was
conducted, involving 47 participants and a diverse range of data sources such
as smartwatch metrics, eye- tracking data, process logs, and responses from
questionnaires. The results indicate interesting insights regarding the effec-
tiveness of the approach in aiding decision-making, decreasing perceived
workload, and increasing situational awareness for the scenarios considered.
Additionally, the results provide valuable insights to compare differences
between styles of information gathering when using the system by individual
participants. These findings are particularly relevant when predicting the
overall performance of the individual participant and their capacity to
successfully handle a plant upset and the alarms connected to it using process
and human-machine interaction logs in real-time. These predictions enable the
development of more effective intervention strategies.

评估了基于人工智能决策支持系统与改进的人机界面的影响和应用，利用动态影响图、隐藏马尔可夫模型和深度强化学习，在复杂工业和化学过程控制室中减轻操作员负担、提高情景感知以及提供不同干预策略的功能。

在控制室中分析操作员状态和 AI 增强决策支持的影响：一种人为参与的专项强化学习框架的干预策略

Analyzing Operator States and the Impact of AI-Enhanced Decision Support  in Control Rooms: A Human-in-the-Loop Specialized Reinforcement Learning  Framework for Intervention Strategies

This paper aims to develop a new human-machine interface to improve
rehabilitation performance from the perspective of both the user (patient) and
the machine (robot) by introducing the co-adaption techniques via model-based
reinforcement learning. Previous studies focus more on robot assistance, i.e.,
to improve the control strategy so as to fulfill the objective of
Assist-As-Needed. In this study, we treat the full process of robot-assisted
rehabilitation as a co-adaptive or mutual learning process and emphasize the
adaptation of the user to the machine. To this end, we proposed a Co-adaptive
MDPs (CaMDPs) model to quantify the learning rates based on cooperative
multi-agent reinforcement learning (MARL) in the high abstraction layer of the
systems. We proposed several approaches to cooperatively adjust the Policy
Improvement among the two agents in the framework of Policy Iteration. Based on
the proposed co-adaptive MDPs, the simulation study indicates the
non-stationary problem can be mitigated using various proposed Policy
Improvement approaches.

本文提出了一种基于模型的强化学习的方法，即协同适应 MDPs 模型，可用于人机界面和康复系统中，可以提高用户（患者）和机器（机器人助手）的表现效果，并强调了用户与机器之间的协同适应过程。