CRII: CPS: Bilateral Adaptation Between Models for Human-Perceived Safety/Comfort and Autonomous Driving Controllers

CRII：CPS：人类感知安全/舒适模型与自动驾驶控制器之间的双边适应

• 批准号: 1755771
• 负责人: Yunyi Jia
• 金额: $ 17.5万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-03-15 至 2022-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1755771&HistoricalAwards=false
• 关键词: CRII; CPS; Bilateral; Adaptation; Between

This project addresses perceived safety within the context of autonomous driving. Self-driving cars have great potential to transform our transportation system, but their technical safety does not necessarily indicate how a human will perceive the safety of autonomous driving, especially if the driving style of the passenger is less aggressive than that of the self-driving car. The goal of this research is to change the car's controller when a human shows discomfort with respect to safety. The approach is to measure physical data from passengers in simulators and in real cars, compare these to expected levels of comfort, and update the controllers using novel algorithms. The project's results will improve user acceptance of autonomous vehicles, as well as produce new curriculum and outreach programs related to autonomous driving. The goal of the project is to understand human perceived safety and comfort in autonomous driving and examine and bilaterally adapt the interactions between humans and autonomous vehicles in order to enhance the human perception (i.e., perceived safety and comfort) while simultaneously enhancing the vehicle performance (i.e., technical driving safety and comfort). In particular, the proposed research will (a) understand the factors affecting the human perceived safety and comfort and investigate how to quantify human perceived safety and comfort using physiological signals, (b) investigate general human and autonomous driving models and online bilateral adaptation strategies to improve the driving safety and comfort, and (c) evaluate the research outcomes using both simulated and real autonomous vehicles. The research will contribute to improving the safety and comfort of autonomous driving and is scalable to other human-autonomy interaction contexts as well.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.

该项目解决了自动驾驶背景下的感知安全问题。自动驾驶汽车有很大的潜力来改变我们的交通系统，但它们的技术安全性并不一定表明人类将如何看待自动驾驶的安全性，特别是如果乘客的驾驶风格不如自动驾驶汽车那么激进的话。这项研究的目的是改变汽车的控制器时，人类表现出不舒适的安全方面。该方法是测量模拟器和真实的汽车中乘客的物理数据，将这些数据与预期的舒适度进行比较，并使用新的算法更新控制器。该项目的成果将提高用户对自动驾驶汽车的接受度，并产生与自动驾驶相关的新课程和推广计划。该项目的目标是了解自动驾驶中人类感知的安全性和舒适性，并检查和双向调整人类与自动驾驶车辆之间的交互，以增强人类感知（即，感知的安全性和舒适性）同时增强车辆性能（即，技术驾驶安全性和舒适性）。特别是，拟议的研究将（a）了解影响人类感知的安全性和舒适性的因素，并研究如何使用生理信号量化人类感知的安全性和舒适性，（B）研究一般的人类和自动驾驶模型和在线双边适应策略，以提高驾驶安全性和舒适性，以及（c）使用模拟和真实的自动驾驶车辆评估研究成果。该研究将有助于提高自动驾驶的安全性和舒适性，并可扩展到其他人类自主互动环境。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

Cyber-Human-Physical Heterogeneous Traffic Systems for Enhanced Safety

• DOI: 10.1109/metrocad48866.2020.00024
• 发表时间: 2020-02
• 期刊: 2020 International Conference on Connected and Autonomous Driving (MetroCAD)
• 作者: Yunyi Jia;B. Ayalew

Comfort Improvement for Autonomous Vehicles Using Reinforcement Learning with In-Situ Human Feedback

使用强化学习和现场人类反馈来提高自动驾驶汽车的舒适度

• DOI: 10.4271/2022-01-0807
• 发表时间: 2022
• 期刊: SAE Technical Paper Series
• 作者: Xiang, Jun;Guo, Longxiang
• 通讯作者: Guo, Longxiang

Road Condition Based Adaptive Model Predictive Control for Autonomous Vehicles

基于路况的自动驾驶车辆自适应模型预测控制

• DOI: 10.1115/dscc2018-9095
• 发表时间: 2018
• 期刊: ASME Dynamic Systems and Control Conference (DSCC
• 作者: Wang, Xin;Guo, Longxiang;Jia, Yunyi
• 通讯作者: Jia, Yunyi

Study of Human Comfort in Autonomous Vehicles Using Wearable Sensors

使用可穿戴传感器的自动驾驶汽车的人体舒适度研究

• DOI: 10.1109/tits.2021.3104827
• 发表时间: 2021
• 期刊: IEEE Transactions on Intelligent Transportation Systems
• 影响因子: 8.5
• 作者: Su, Haotian;Jia, Yunyi
• 通讯作者: Jia, Yunyi

Modeling, Learning and Prediction of Longitudinal Behaviors of Human-Driven Vehicles by Incorporating Internal Human DecisionMaking Process using Inverse Model Predictive Control

使用逆模型预测控制结合内部人类决策过程对人类驾驶车辆的纵向行为进行建模、学习和预测

• DOI: 10.1109/iros40897.2019.8968292
• 发表时间: 2019
• 期刊: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS
• 作者: Guo, Longxiang;Jia, Yunyi
• 通讯作者: Jia, Yunyi