NRI: FND: Scalable and Customizable Intent Inference and Motion Planning for Socially-Adept Autonomous Vehicles

NRI：FND：适用于社交自动驾驶车辆的可扩展和可定制的意图推理和运动规划

• 批准号: 1925403
• 负责人: Wenlong Zhang
• 金额: $ 75万
• 依托单位: Arizona State University
• 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: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1925403&HistoricalAwards=false
• 关键词: NRI; FND; Scalable; Customizable; Intent

This project will promote the progress of science, and advance the national prosperity and safety, by tackling an important and challenging problem for autonomous vehicles (AVs): interaction of AVs with human-driven vehicles. Currently, there is a lack of theory that allows an autonomous vehicle to interact with multiple surrounding vehicles in a safe and socially-adept manner. This National Robotics Initiative (NRI) project will address this critical need by developing a novel algorithm framework for an autonomous vehicle to be able to anticipate other vehicles, behavior and customize its motion according to the local driving culture. This project serves the national interests by advancing knowledge in the fields of control engineering, machine learning, and cognitive science. The project will also make an important step in making widely adopted autonomous vehicles a reality, which promises to increase transportation system efficiency and safety. Project results will be disseminated through a project website, open-source simulation software, and public datasets. The impacts of this project will be broadened through various educational activities, including a new class on collaborative autonomous driving, undergraduate research projects, and outreach to the local community through lab tours.This project aims at answering two fundamental research questions: 1) what formalisms of intent inference and motion planning are capable of creating socially-adept motions, and 2) what embodiment of these formalisms can achieve scalability for multi-vehicle interactions and customizability for changing driving cultures? To answer these questions, the research team will pursue the following three objectives. First, the project team will build a Bayesian game model to represent vehicle interactions, and develop mechanistic intent inference and motion planning policies. Second, a message passing neural network will be developed to enable scalable intent inference and motion prediction of multiple surrounding vehicles. Third, a social attention mechanism will be developed that allows an autonomous vehicle to actively prioritize its various control considerations, e.g., safety and courtesy towards the surrounding vehicles. The developed algorithms will be validated with real-world driving scenarios such as intersections and highways in a driving simulator designed through collaboration with autonomous vehicle manufacturers and research institutes.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.

该项目将通过解决自动驾驶汽车（AV）的一个重要而具有挑战性的问题：AV与人类驾驶车辆的交互，促进科学进步，促进国家繁荣和安全。目前，还缺乏允许自动驾驶车辆以安全和社交熟练的方式与多个周围车辆进行交互的理论。这个国家机器人计划（NRI）项目将通过开发一种新的算法框架来解决这一关键需求，使自动驾驶汽车能够预测其他车辆，行为并根据当地驾驶文化定制其运动。该项目通过推进控制工程，机器学习和认知科学领域的知识来服务于国家利益。 该项目还将使广泛采用的自动驾驶汽车成为现实，这有望提高运输系统的效率和安全性。项目成果将通过项目网站、开源模拟软件和公共数据集传播。该项目的影响将通过各种教育活动扩大，包括新的协作自动驾驶课程，本科生研究项目，以及通过实验室图尔斯之旅与当地社区的联系。该项目旨在回答两个基本的研究问题：1）意图推理和动作规划的哪些形式能够创建社交熟练的动作，以及2）这些形式体系的什么实施例可以实现多车辆交互的可扩展性和改变驾驶文化的可定制性？为了回答这些问题，研究小组将追求以下三个目标。首先，项目团队将构建贝叶斯博弈模型来表示车辆交互，并开发机械意图推理和运动规划策略。其次，将开发一个消息传递神经网络，以实现可扩展的意图推断和多个周围车辆的运动预测。第三，将开发一种社会关注机制，允许自动驾驶车辆主动优先考虑其各种控制因素，例如，对周围车辆的安全和礼貌。开发的算法将通过与自动驾驶汽车制造商和研究机构合作设计的驾驶模拟器，在十字路口和高速公路等真实驾驶场景中进行验证。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

Targeted Attack on Deep RL-based Autonomous Driving with Learned Visual Patterns

• DOI: 10.1109/icra46639.2022.9811574
• 发表时间: 2021-09
• 期刊: 2022 International Conference on Robotics and Automation (ICRA)
• 作者: Prasanth Buddareddygari;Travis Zhang;Yezhou Yang;Yi Ren-

When Shall I Estimate Your Intent? Costs and Benefits of Intent Inference in Multi-Agent Interactions

我什么时候才能估计你的意图？

• DOI: 10.23919/acc53348.2022.9867155
• 发表时间: 2022
• 期刊: 2022 American Control Conference (ACC
• 作者: Amatya, Sunny;Ghimire, Mukesh;Ren, Yi;Xu, Zhe;Zhang, Wenlong
• 通讯作者: Zhang, Wenlong

Enabling Courteous Vehicle Interactions through Game-based and Dynamics-aware Intent Inference

通过基于游戏和动态感知的意图推理实现礼貌的车辆交互

• DOI: 10.1109/tiv.2019.2955897
• 发表时间: 2020
• 期刊: IEEE Transactions on Intelligent Vehicles
• 影响因子: 8.2
• 作者: Wang, Yiwei;Ren, Yi;Elliott, Steven;Zhang, Wenlong
• 通讯作者: Zhang, Wenlong

Low to High Dimensional Modality Hallucination Using Aggregated Fields of View

• DOI: 10.1109/lra.2020.2970679
• 发表时间: 2020-01
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: K. Gunasekar;Qiang Qiu;Yezhou Yang

Approximating Discontinuous Nash Equilibrial Values of Two-Player General-Sum Differential Games

• DOI: 10.1109/icra48891.2023.10160219
• 发表时间: 2022-07
• 期刊: 2023 IEEE International Conference on Robotics and Automation (ICRA)
• 作者: Lei Zhang;Mukesh Ghimire;Wenlong Zhang;Zhenni Xu;Yi Ren-