论文标题
自动化车辆的信任感控制与人驾驶员的互动互动
Trust-Aware Control of Automated Vehicles in Car-Following Interactions with Human Drivers
论文作者
论文摘要
信任对于自动化车辆（AV）至关重要，以促进和维持人类主导的交通情况的技术接受。但是，计算信任动态模型描述了AVS与周围人类驾驶员之间的互动关系很少存在。本文旨在通过在与AV的CAR跟随交互中开发人类驱动程序的定量信任动态模型来填补这一空白，并将提出的信任动态模型纳入AV的控制设计中。人类驾驶员的信任水平被建模为计划评估指标，从人类驾驶员的角度来衡量AV计划的明确性，而AV计划的明确性得分已集成到AV的决策过程中。通过提出的方法，信任感知AVS通过优化预定义的计划和与以下人类驾驶员的互动相互作用中的计划来生成可阐明的计划。结果共同表明，与人类AV互动中的Trust-Unaware AV相比，信任感知的AV可以生成更明确的计划，并为人类驾驶员提供更高的信任水平。
Trust is essential for automated vehicles (AVs) to promote and sustain technology acceptance in human-dominated traffic scenarios. However, computational trust dynamic models describing the interactive relationship between the AVs and surrounding human drivers in traffic rarely exist. This paper aims to fill this gap by developing a quantitative trust dynamic model of the human driver in the car-following interaction with the AV and incorporating the proposed trust dynamic model into the AV's control design. The human driver's trust level is modeled as a plan evaluation metric that measures the explicability of the AV's plan from the human driver's perspective, and the explicability score of the AV's plan is integrated into the AV's decision-making process. With the proposed approach, trust-aware AVs generate explicable plans by optimizing both predefined plans and explicability of the plans in the car-following interactions with the following human driver. The results collectively demonstrate that the trust-aware AV can generate more explicable plans and achieve a higher trust level for the human driver compared to trust-unaware AV in human-AV interactions.
