CHS: Small: Improving user trust of autonomous vehicles through human-vehicle collaboration

全自动驾驶汽车（FAV）代表了未来交通运输的一个关键方向，特别是对于目前被传统手动操作车辆剥夺权利的人群，如视力障碍者或老年人。 不幸的是，自动驾驶技术对这些人口的好处在FAV的设计中尚未得到充分考虑。这种担忧是一个更大问题的一部分，在这个问题上，大多数人根本不相信自动驾驶汽车能满足他们的需求。该项目探索了在人与FAV之间共享决策信息的新方法。该项目将开发一个人车协作（HVC）框架，在该框架中，有关车辆决策过程的有效沟通提高了用户对代理和信任FAV的决策的感觉。通过研究人们对FAV驾驶决策的反应和理解，并开发帮助FAV以解决这些问题的方式进行通信的算法和界面，HVC框架将促进所有用户对FAV的适当信任水平，同时为改善服务不足的人群提供实质性的好处。 HVC框架将使用高保真驾驶模拟器进行设计和评估，该模拟器测试在关键驾驶事件期间共享信息的新交互方法。研究将使用这个模拟器和实验平台来操纵与驾驶时的决策状态相关的大量变量。将收集人类数据，包括反应时间、车辆决策的解释、基于皮肤电反应和心率的生理测量，以及用于评估对全自动驾驶车辆信任度的前后调查测量。结果将形成开发和测试HVC配置文件的基础，这些配置文件在驾驶事件期间提供个性化的交互和协作。这些配置文件和由此产生的指导方针代表了该项目的关键成果，因为它们从一开始就旨在提高全自动驾驶汽车的信任度、可达性和整体优化。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Fully autonomous vehicles (FAVs) represent a key future direction for transportation, especially for populations who are currently disenfranchised by traditional, manually operated vehicles, such as those with visual impairments or older adults. Unfortunately, the benefits of self-driving technology for these demographics have yet to be adequately considered in the design of FAVs. This concern is part of a larger problem in which the majority of people simply do not trust that self-driving cars will meet their needs. This project explores new ways to share decision-making information between people and FAVs. Instead of existing systems where the FAV makes the decisions in a "black box" way that users don't see or understand, the project will develop a Human-Vehicle Collaboration (HVC) framework in which effective communication about the vehicle's decision-making process improves users' sense of agency and decisions around trusting the FAV. Through studying people's reactions to and understanding of FAV driving decisions and developing algorithms and interfaces that help FAVs communicate in ways that address them, the HVC framework will promote appropriate levels of trust in FAVs for all users, while providing substantial benefits around improving accessibility for under-served populations. The HVC framework will be designed and evaluated using a high-fidelity driving simulator that tests new interaction methods for sharing information during key driving events. Research will use this simulator and experimental platform to manipulate a host of variables relating to decision states while driving. Human data will be collected on reaction time, interpretation of vehicle decision-making, physiological measures based on galvanic skin response and heart rate, as well as pre-post survey measures for assessing trust in fully-autonomous vehicles. Results will form the foundation for developing and testing HVC profiles that provide individualized interactions and collaborations during driving events. These profiles and the resulting guidelines represent a key deliverable for the project as they will be designed from the outset to improve trust, accessibility, and the overall optimization of fully-autonomous vehicles.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.