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NRI: Addressing Safe Interaction Between Autonomous and Human-Driven Vehicles

NRI：解决自动驾驶和人类驾驶车辆之间的安全交互问题

基本信息

批准号：
2219761
负责人：
Andreas Malikopoulos
金额：
$ 47.58万
依托单位：
University of Delaware
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-10-01 至 2023-10-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2219761&HistoricalAwards=false
关键词：
NRI Addressing Safe Interaction Between

项目摘要

Connected and automated vehicles are robotic systems which exhibit significant levels of computational capability and physical complexity. They have the capacity to make contextually decisions independently, without human intervention, while they interact in a complex environment. The data and shared information through vehicle-to-everything communication are associated with significant technical challenges and gives rise to a new level of complexity in modeling and control. It is expected that connected and automated vehicles will gradually penetrate the market, interact with human-driven vehicles, and contend with vehicle-to-everything communication limitations, e.g., bandwidth, dropouts, errors, and delays. However, different penetration rates of connected and automated vehicles can significantly alter transportation efficiency and safety. This grant will synergistically integrate human-driving behavior with control theory and learning in developing data-driven approaches that will enable a transformative new functionality of connected and automated vehicles to interact with human-driven vehicles safely and efficiently. On the education front and outreach, the research is an excellent catalyst for motivating interest in science, technology, engineering, and mathematics disciplines. The outcome of this research will deliver new methods to address a fundamental gap between optimal trajectory planning of and safe-critical control in connected and automated vehicles. The researched framework is organized at the intersection of three interdependent dimensions, namely, human-driving behavior, control theory, and learning. The human-driving dimension will enhance our understanding on how human drivers will respond to different driving scenarios. The control theory dimension will create knowledge on the appropriate prescription functions that will yield the optimal decisions and planning of connected and automated vehicles with respect to human driving behavior. The learning dimension will create knowledge of how connected and automated vehicles can learn to adapt their decisions and planning in situations where they encounter different behavior from what they already know about human driving. Thus, this dimension will not only improve the robustness of connected and automated vehicles but also their operation range with respect to any different driving behavior that they might encounter. The expected outcome of this research will aim at making connected and automated vehicles to coordinate with human-driven vehicles to improve safety and reduce pollution, energy consumption, and travel delays.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.

联网和自动驾驶车辆是具有高计算能力和物理复杂性的机器人系统。当它们在复杂的环境中交互时，它们有能力独立做出上下文决策，无需人工干预。通过车辆到万物通信的数据和共享信息带来了重大的技术挑战，并使建模和控制的复杂性达到了新的水平。预计联网和自动驾驶汽车将逐渐渗透市场，与人类驾驶的车辆互动，并应对车辆与万物的通信限制，例如带宽、丢包、错误和延迟。然而，联网和自动驾驶车辆的不同渗透率可能会显着改变运输效率和安全性。这笔赠款将协同地将人类驾驶行为与控制理论和学习相结合，开发数据驱动的方法，使联网和自动车辆的变革性新功能能够安全有效地与人类驾驶的车辆交互。在教育方面和推广方面，该研究是激发人们对科学、技术、工程和数学学科兴趣的极好催化剂。这项研究的成果将提供新的方法来解决联网和自动车辆的最佳轨迹规划和安全关键控制之间的根本差距。研究框架是在三个相互依赖的维度的交叉点上组织的，即人类驾驶行为、控制理论和学习。人类驾驶维度将增强我们对人类驾驶员如何应对不同驾驶场景的理解。控制理论维度将创建有关适当处方函数的知识，从而产生有关人类驾驶行为的联网和自动车辆的最佳决策和规划。学习维度将创造有关互联和自动驾驶车辆如何在遇到与人类驾驶已知行为不同的情况下学习调整其决策和规划的知识。因此，这个维度不仅会提高联网和自动驾驶车辆的稳健性，还会提高其针对可能遇到的任何不同驾驶行为的操作范围。这项研究的预期成果将旨在使联网和自动驾驶车辆与人类驾驶车辆协调，以提高安全性并减少污染、能源消耗和出行延误。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力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

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Separation of learning and control for cyber-physical systems

DOI：
10.1016/j.automatica.2023.110912
发表时间：
2021-07
期刊：
Autom.
影响因子：
0
作者：
Andreas A. Malikopoulos
通讯作者：
Andreas A. Malikopoulos

Optimal Weight Adaptation for Model Predictive Control of Connected and Automated Vehicles in Mixed Traffic with Bayesian Optimization

基于贝叶斯优化的混合交通中联网自动驾驶车辆模型预测控制的最优权重自适应

DOI：
发表时间：
2023
期刊：
2023 American Control Conference
影响因子：
0
作者：
Le, V.-A.;Malikopoulos, A.A.
通讯作者：
Malikopoulos, A.A.

Constraint-Driven Optimal Control for Emergent Swarming and Predator Avoidance

紧急蜂群和躲避捕食者的约束驱动最优控制

DOI：
发表时间：
2023
期刊：
2023 American Control Conference
影响因子：
0
作者：
Beaver, L.E.;Malikopoulos, A.A.
通讯作者：
Malikopoulos, A.A.

Re-Routing Strategy of Connected and Automated Vehicles Considering Coordination at Intersections

DOI：
10.23919/acc55779.2023.10156555
发表时间：
2022-10
期刊：
2023 American Control Conference (ACC)
影响因子：
0
作者：
Heeseung Bang;Andreas A. Malikopoulos
通讯作者：
Heeseung Bang;Andreas A. Malikopoulos

A Research and Educational Robotic Testbed for Real-Time Control of Emerging Mobility Systems: From Theory to Scaled Experiments [Applications of Control]

DOI：
10.1109/mcs.2022.3209056
发表时间：
2021-09
期刊：
IEEE Control Systems
影响因子：
0
作者：
Behdad Chalaki;Logan E. Beaver;A. Mahbub;Heeseung Bang;Andreas A. Malikopoulos
通讯作者：
Behdad Chalaki;Logan E. Beaver;A. Mahbub;Heeseung Bang;Andreas A. Malikopoulos