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

基本信息

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

项目摘要

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）的重要且具有挑战性的问题来促进科学的进步，并促进国家繁荣和安全的发展：AVS与人类驱动的车辆的相互作用。当前，缺乏理论，使自动驾驶汽车以安全且受社交的方式与多个周围的车辆互动。这项国家机器人倡议（NRI）项目将通过开发一种新颖的算法框架来满足这一关键需求，以便自动驾驶汽车能够根据当地驾驶文化来预测其他车辆，行为并自定义其运动。该项目通过促进控制工程，机器学习和认知科学领域的知识来为国家利益提供服务。该项目还将在使广泛采用的自动驾驶汽车成为现实方面迈出重要的一步，这有望提高运输系统的效率和安全性。项目结果将通过项目网站，开源仿真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来扩展，包括有关协作自主驾驶，本科研究项目的新课程，并通过实验室旅行向当地社区推广。该项目旨在回答两个基本的研究问题：1）有意义的推理和运动计划的形式性能够创造社会化形式的互动性和2）实体性，以及2）实现的范围，以及2）实现了跨性别的性能，以及2）实现了什么，并实现了2）的范围。驾驶文化？为了回答这些问题，研究团队将追求以下三个目标。首先，项目团队将建立一个贝叶斯游戏模型，以表示车辆互动，并制定机械意图推理和运动计划政策。其次，将开发传递神经网络的消息，以实现可扩展的意图推理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，允许自动驾驶汽车积极优先考虑其各种控制考虑因素，例如对周围车辆的安全和礼貌。在通过与自主车辆制造商和研究机构合作设计的驾驶模拟器中，将通过现实世界中的驾驶场景（例如交叉路口和高速公路）进行验证。该奖项反映了NSF的法定任务，并已通过评估该基金会的知识分子功能和广泛的影响来评估NSF的法定任务。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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)
影响因子：
0
作者：
Prasanth Buddareddygari;Travis Zhang;Yezhou Yang;Yi Ren-
通讯作者：
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
影响因子：
0
作者：
Amatya, Sunny;Ghimire, Mukesh;Ren, Yi;Xu, Zhe;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
通讯作者：
K. Gunasekar;Qiang Qiu;Yezhou Yang

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

Injecting Semantic Concepts into End-to-End Image Captioning

DOI：
10.1109/cvpr52688.2022.01748
发表时间：
2021-12
期刊：
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
影响因子：
0
作者：
Zhiyuan Fang;Jianfeng Wang;Xiaowei Hu;Lin Liang;Zhe Gan;Lijuan Wang;Yezhou Yang;Zicheng Liu
通讯作者：
Zhiyuan Fang;Jianfeng Wang;Xiaowei Hu;Lin Liang;Zhe Gan;Lijuan Wang;Yezhou Yang;Zicheng Liu