Trust-based route planning for automated vehicles
Trust-based route planning for automated vehicles
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DOI：
10.1145/3450267.3450529
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
Shili Sheng;Erfan Pakdamanian;Kyungtae Han;Ziran Wang;John K. Lenneman;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;Lu Feng
最近的几项工作考虑了基于用户配置文件的个性化路线规划，其中没有考虑人类的信任。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。本文提出了第一个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。我们在亚马逊土耳其机器人平台上设计并进行了一项在线用户研究，有100名参与者，以收集用户对自动驾驶汽车的信任数据。我们建立了信任动态和收购决策的数据驱动模型，这些模型被纳入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。我们通过人体实验与22名参与者在驾驶模拟器上评估了由此产生的路线。实验结果表明，参与者采取信任为基础的路线，一般会导致更高的累积POMDP奖励，并报告更多的积极反应，在驾驶后的调查比那些采取基线信任自由路线。
Several recent works consider the personalized route planning based on user profiles, none of which accounts for human trust. We argue that human trust is an important factor to consider when planning routes for automated vehicles. This paper presents the first 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 human's hidden mental state. We designed and conducted an online user study with 100 participants on the Amazon Mechanical Turk platform to collect data of users' trust in automated vehicles. We build data-driven models of trust dynamics and takeover decisions, which are incorporated in the POMDP framework. We compute optimal routes for automated vehicles by solving optimal policies in the POMDP planning. We evaluated 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 resulted in higher cumulative POMDP rewards and reported more positive responses in the after-driving survey than those taking the baseline trust-free route.