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，特别是对于那些目前被传统的手动驾驶汽车剥夺了权利的人群，比如视力障碍人士或老年人。不幸的是，在设计自动驾驶汽车时，尚未充分考虑到自动驾驶技术对这些人群的好处。这种担忧是一个更大问题的一部分，即大多数人根本不相信自动驾驶汽车能满足他们的需求。该项目探索了在人和fav之间共享决策信息的新方法。在现有系统中，自动驾驶汽车以一种用户看不到或无法理解的“黑盒子”方式做出决策，而该项目将开发一个人车协作（HVC）框架，在这个框架中，关于车辆决策过程的有效沟通可以提高用户对自动驾驶汽车的代理感和决策感。通过研究人们对FAV驱动决策的反应和理解，并开发算法和接口，帮助FAV以解决这些问题的方式进行沟通，HVC框架将促进所有用户对FAV的适当信任，同时在改善服务不足人群的可访问性方面提供实质性的好处。HVC框架将使用高保真驾驶模拟器进行设计和评估，该模拟器将测试在关键驾驶事件中共享信息的新交互方法。研究将使用该模拟器和实验平台来操纵驾驶时与决策状态相关的一系列变量。人类数据将收集反应时间、车辆决策解释、基于皮肤电反应和心率的生理测量，以及评估对全自动驾驶汽车信任的调查前后测量。研究结果将为开发和测试HVC配置文件奠定基础，这些配置文件将在驾驶活动中提供个性化的交互和协作。这些配置文件和最终的指导方针代表了项目的关键交付成果，因为它们将从一开始就被设计为提高信任度、可访问性和全面优化全自动驾驶汽车。该奖项反映了美国国家科学基金会的法定使命，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.