CRII:CHS: Addressing Individual Differences in Human-Autonomous Vehicle Interaction in Mixed Transportation Systems

CRII：CHS：解决混合交通系统中人机交互的个体差异

• 批准号: 1850002
• 负责人: Yiqi Zhang
• 金额: $ 17.35万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1850002&HistoricalAwards=false
• 关键词: CRII; CHS; Addressing; Individual; Differences

AbstractAutonomous vehicles will change the way human drivers interact with vehicles dramatically. Before there is 100% adoption of autonomous vehicles (AV), we will have a mixed transportation system in which AVs share the roads with human-driven vehicles (HV). This project investigates how driving styles of autonomous vehicles impact decision-making, trust, and acceptance of AVs both for the human operator and for human drivers in other vehicles. This project will benefit the design of AVs to promote drivers' trust of AVs in mixed traffic conditions, mitigate driver aggressiveness to improve driver safety, address public acceptance of autonomous vehicles, and smooth the transition into a future where autonomous driving is prevalent. It provides researchers and urban policy-makers knowledge and methods to evaluate the effects of AV technology on mixed transportation systems. This research will support the development of graduate-level curricula integrating research and practical design issues in human-vehicle interaction, providing an interdisciplinary education for students from Industrial Engineering, Civil Engineering, and Mechanical Engineering. This project addresses the transitional period of mixed driving systems that are expected to grow in the coming years by targeting three fundamental questions: (1) how to quantify the relationship between individual differences, trust, and decision-making in the interaction between an AV and the human operator; (2) how to investigate driver behavior and performance in the interaction between AV and human-driven vehicles; and (3) how to design driving styles of autonomous vehicles that can improve driver trust and acceptance of AV technology while mitigating the potential for offsetting behavior (e.g. drivers' tendency of additional risk taking when they feel safer). Throughout the project, the effects of individual differences in driving style (aggressive vs conservative), the propensity to trust technology, and age, gender, and driving experience are explored empirically. Corresponding design guidelines of AV driving styles will also be proposed to improve driver safety and promote driver trust of AVs.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.

摘要自动驾驶汽车将极大地改变人类驾驶员与车辆互动的方式。在100%采用自动驾驶汽车（AV）之前，我们将拥有一个混合交通系统，其中AV与人类驾驶的车辆（HV）共享道路。 该项目研究了自动驾驶汽车的驾驶风格如何影响人类操作员和其他车辆中的人类驾驶员对自动驾驶汽车的决策、信任和接受。该项目将有利于自动驾驶汽车的设计，以促进驾驶员在混合交通条件下对自动驾驶汽车的信任，减轻驾驶员的攻击性以提高驾驶员的安全性，解决公众对自动驾驶汽车的接受程度，并顺利过渡到自动驾驶汽车盛行的未来。它为研究人员和城市决策者提供了评估自动驾驶技术对混合交通系统影响的知识和方法。这项研究将支持研究生课程的开发，整合人车互动的研究和实际设计问题，为工业工程，土木工程和机械工程的学生提供跨学科的教育。 本项目针对混合驾驶系统的过渡期，针对三个基本问题：（1）如何量化个体差异、信任和决策之间的关系，在AV和人类驾驶员之间的互动;（2）如何调查AV和人类驾驶车辆之间的互动中驾驶员的行为和表现;（3）如何在AV和人类驾驶车辆之间的互动中进行驾驶员的行为和表现。以及（3）如何设计自动驾驶汽车的驾驶风格，以提高驾驶员对自动驾驶技术的信任和接受度，同时减轻抵消行为的可能性（例如，驾驶员在感到更安全时倾向于承担额外的风险）。 在整个项目中，驾驶风格（积极与保守），信任技术的倾向，年龄，性别和驾驶经验的个体差异的影响进行了实证研究。 此外，还将提出相应的自动驾驶汽车驾驶风格设计指南，以提高驾驶员的安全性，并促进驾驶员对自动驾驶汽车的信任。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。

项目成果

Modeling Driver Takeover Intention in Automated Vehicles With Attention-Based CNN Algorithm

使用基于注意力的 CNN 算法对自动驾驶汽车中的驾驶员接管意图进行建模

• DOI: 10.1177/1071181322661303
• 发表时间: 2022
• 期刊: Proceedings of the Human Factors and Ergonomics Society Annual Meeting
• 作者: Gupta, Shantanu;Mishra, Rohit;Chang, Yu-Hao;Ma, Zheng;Ma, Fenglong;Zhang, Yiqi
• 通讯作者: Zhang, Yiqi

A Computational Cognitive Model of Driver Response Time for Scheduled Freeway Exiting Takeovers in Conditionally Automated Vehicles

• DOI: 10.1177/00187208221143028
• 发表时间: 2022-12
• 期刊: Human Factors
• 影响因子: 3.3
• 作者: Xiaomei Tan;Yiqi Zhang

Investigating the Effects of Automated Driving Styles and Driver’s Driving Styles on Driver Trust, Acceptance, and Take Over Behaviors

研究自动驾驶风格和驾驶员的驾驶风格对驾驶员信任、接受和接管行为的影响

• DOI: 10.1177/1071181320641484
• 发表时间: 2020
• 期刊: Proceedings of the Human Factors and Ergonomics Society Annual Meeting
• 作者: Ma, Zheng;Zhang, Yiqi
• 通讯作者: Zhang, Yiqi

The effects of takeover request lead time on drivers’ situation awareness for manually exiting from freeways: A web-based study on level 3 automated vehicles

接管请求提前时间对驾驶员手动退出高速公路的态势感知的影响：针对 3 级自动驾驶车辆的基于网络的研究

• DOI: 10.1016/j.aap.2022.106593
• 发表时间: 2022
• 期刊: Accident Analysis & Prevention
• 影响因子: 5.9
• 作者: Tan, Xiaomei;Zhang, Yiqi
• 通讯作者: Zhang, Yiqi

Investigating the Effects of Automated Driving Style and Driver Driving Style on Drivers’ Perception of Automated Driving Indicators

研究自动驾驶风格和驾驶员驾驶风格对驾驶员对自动驾驶指标感知的影响

• DOI: 10.1177/1071181322661273
• 发表时间: 2022
• 期刊: Proceedings of the Human Factors and Ergonomics Society Annual Meeting
• 作者: Ma, Zheng;Zhang, Yiqi
• 通讯作者: Zhang, Yiqi