CPS: TTP Option: Medium: Identifying, Characterizing, and Shaping Multi-Scale Cyber-Human Interactions in Mixed Autonomous/Conventional Vehicle Traffic

CPS：TTP 选项：中：识别、表征和塑造混合自主/传统车辆交通中的多尺度网络人机交互

• 批准号: 1739869
• 负责人: Soyoung Ahn
• 金额: $ 120万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1739869&HistoricalAwards=false
• 关键词: CPS; TTP; Option; Medium; Identifying

The promise of intelligent transportation systems is safety, mobility, and efficiencies well beyond current technologies. Yet, transportation engineers and planners must anticipate future transportation needs for which the enabling technologies are rapidly evolving while the change-over from old to new transportation technologies will take decades. This project seeks to understand the impact of these trends for transportation engineering, offer approaches for effective shared control between autonomous vehicles and drivers, and provide insight into strategies to realize the promise of intelligent transportation systems. The approach to studying traffic uses driving simulators, built on a high-performance computing architecture, that enables high-resolution, physics-based, realistic, interactive simulations, necessary for detailed modeling of vehicles and drivers under changing traffic conditions and various assumptions on autonomy, control, and connectedness, and for monitoring human driving behaviors.This project identifies, characterizes, and shapes human cyber-physical interactions, bound to emerge in future mixed autonomous and conventional vehicle traffic. Its aims are to: (1) better understand human-cyber-physical interactions in mixed traffic, particularly driver trust in vehicle automation; (2) develop analytical and computational methods to assess the impacts on traffic capacity and regime transition for scenarios in which a driver switches from autonomous vehicle mode to conventional vehicle mode (e.g., loss of trust in automation scenarios); and (3) develop engineering solutions to enhance cyber-human interactions and traffic flow. The research embraces a multi-scale analysis approach anchored in human behavior analysis and interface design, modeling of mixed traffic and vehicle control, and system-level analysis of interdependency among human, vehicles, and traffic. Understanding of human cyber-physical interactions through this project will enable better future designs of autonomous vehicles and the ways in which humans interact with these evolving systems safely and dependably.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.

智能交通系统的承诺是安全性，流动性和效率远远超过目前的技术。然而，交通工程师和规划者必须预测未来的交通需求，使能技术正在迅速发展，而从旧的交通技术到新的交通技术的转变将需要几十年的时间。 该项目旨在了解这些趋势对交通工程的影响，为自动驾驶汽车和驾驶员之间的有效共享控制提供方法，并为实现智能交通系统的承诺提供战略见解。 研究交通的方法使用驾驶模拟器，建立在一个高性能的计算架构，使高分辨率，基于物理的，逼真的，交互式的模拟，在不断变化的交通条件下的车辆和驾驶员的详细建模和各种假设的自主性，控制和连通性，并监测人类驾驶行为所必需的。并塑造人类的网络物理交互，必将出现在未来的混合自动驾驶和传统车辆交通中。其目标是：（1）更好地理解混合交通中的人机交互，特别是驾驶员对车辆自动化的信任;（2）开发分析和计算方法，以评估驾驶员从自动驾驶车辆模式切换到传统车辆模式（例如，在自动化场景中失去信任）;以及（3）开发工程解决方案，以增强网络与人类的互动和流量。该研究采用多尺度分析方法，包括人的行为分析和界面设计，混合交通和车辆控制的建模，以及人，车辆和交通之间相互依赖的系统级分析。 通过该项目对人类网络物理交互的理解将有助于更好地设计未来的自动驾驶汽车，以及人类与这些不断发展的系统安全可靠地交互的方式。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

Data-driven analysis for disturbance amplification in car-following behavior of automated vehicles

自动车辆跟车行为干扰放大的数据驱动分析

• DOI: 10.1016/j.trb.2023.05.005
• 发表时间: 2023
• 期刊: Transportation Research Part B: Methodological
• 作者: Zhou, Yang;Zhong, Xinzhi;Chen, Qian;Ahn, Soyoung;Jiang, Jiwan;Jafarsalehi, Ghazaleh
• 通讯作者: Jafarsalehi, Ghazaleh

Modeling Cameras for Autonomous Vehicle and Robot Simulation: An Overview

• DOI: 10.1109/jsen.2021.3118952
• 发表时间: 2021-11
• 期刊: IEEE Sensors Journal
• 影响因子: 4.3
• 作者: A. Elmquist;D. Negrut

SynChrono: A Scalable, Physics-Based Simulation Platform For Testing Groups of Autonomous Vehicles and/or Robots

SynChrono：一个可扩展、基于物理的仿真平台，用于测试自动驾驶车辆和/或机器人组

• DOI: 10.1109/iros45743.2020.9341585
• 发表时间: 2020
• 期刊: IROS
• 作者: Taves, Jay;Elmquist, Asher;Young, Aaron;Serban, Radu;Negrut, Dan
• 通讯作者: Negrut, Dan

Synthetic Image Generation for Robot Simulation: Quantifying the Impact of Model Modifications on Perception

用于机器人仿真的合成图像生成：量化模型修改对感知的影响

• DOI: 10.1109/jsen.2023.3288488
• 发表时间: 2023
• 期刊: IEEE Sensors Journal
• 影响因子: 4.3
• 作者: Elmquist, Asher;Serban, Radu;Negrut, Dan
• 通讯作者: Negrut, Dan

Comparing Subjective Similarity of Automated Driving Styles to Objective Distance-Based Similarity

• DOI: 10.1177/00187208221142126
• 发表时间: 2023-01
• 期刊: Human Factors
• 影响因子: 3.3
• 作者: A. Kamaraj;Joonbum Lee;Joshua E. Domeyer;Shu-Yuan Liu;John D. Lee