Planning for Automated Vehicles with Human Trust
Planning for Automated Vehicles with Human Trust
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DOI：
10.1145/3561059
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发表时间：
2021-01
影响因子：
2.3
通讯作者：
Shili Sheng;Erfan Pakdamanian;Kyungtae Han;Ziran Wang;John K. Lenneman;David Parker;Lu Feng
中科院分区：
文献类型：
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作者：
Shili Sheng;Erfan Pakdamanian;Kyungtae Han;Ziran Wang;John K. Lenneman;David Parker;Lu Feng
最近的工作考虑了基于用户配置文件的个性化路线规划，但没有考虑到人类的信任。我们认为，人类的信任是规划自动驾驶汽车的路线时要考虑的一个重要因素。本文提出了一种基于信任的自动车辆路径规划方法。我们形式化的人车交互作为一个部分可观察的马尔可夫决策过程（POMDP）和模型的信任作为一个部分可观察的状态变量的POMDP，代表人类的隐藏的心理状态。我们使用从Amazon Mechanical Turk平台上的100名参与者的在线用户研究中收集的数据，构建了人类信任动态和收购决策的数据驱动模型，并将其纳入POMDP框架。我们计算自动驾驶汽车的最佳路线，通过解决POMDP规划中的最优策略，并通过22名参与者在驾驶模拟器上进行的人体实验来评估所产生的路线。实验结果表明，参与者采取的信任为基础的路线，一般更积极的反应，在驾驶后的调查比那些采取基线（信任自由）路线。此外，我们还分析了多个规划目标之间的权衡（例如，信任、距离、能量消耗）。我们还确定了一组开放的问题和现实世界的部署所提出的方法在自动驾驶汽车的影响。
Recent work has considered personalized route planning based on user profiles, but none of it accounts for human trust. We argue that human trust is an important factor to consider when planning routes for automated vehicles. This article presents a trust-based route-planning approach for automated vehicles. We formalize the human-vehicle interaction as a partially observable Markov decision process (POMDP) and model trust as a partially observable state variable of the POMDP, representing the human’s hidden mental state. We build data-driven models of human trust dynamics and takeover decisions, which are incorporated in the POMDP framework, using data collected from an online user study with 100 participants on the Amazon Mechanical Turk platform. We compute optimal routes for automated vehicles by solving optimal policies in the POMDP planning and evaluate the resulting routes via human subject experiments with 22 participants on a driving simulator. The experimental results show that participants taking the trust-based route generally reported more positive responses in the after-driving survey than those taking the baseline (trust-free) route. In addition, we analyze the trade-offs between multiple planning objectives (e.g., trust, distance, energy consumption) via multi-objective optimization of the POMDP. We also identify a set of open issues and implications for real-world deployment of the proposed approach in automated vehicles.