MRI: Acquisition of Connected Autonomous Vehicles (CAV) Infrastructure to Support Cooperative Human-Robot Driving and Pedestrian Safety

MRI：收购联网自动驾驶车辆 (CAV) 基础设施以支持人机协作驾驶和行人安全

• 批准号: 2216489
• 负责人: Brendan Morris
• 金额: $ 37.73万
• 依托单位: University of Nevada Las Vegas
• 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 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2216489&HistoricalAwards=false
• 关键词: MRI; Acquisition; Connected; Autonomous; Vehicles

This project funds the creation of the University of Nevada, Las Vegas (UNLV) Mixed Driving (MixeD) “living laboratory” through the purchase and deployment of advanced environmental sensors and vehicle-to-everything (V2X) communication equipment. The equipment will augment UNLV’s basic automated vehicle demonstration platform and equip at least three intersections adjacent to campus with high-resolution cameras, radars, and lidars and V2X communication for collaborative vehicle-infrastructure sensing and safety. The equipment will enable new research in connected and autonomous vehicles (CAVs), with emphasis on artificial intelligence (AI) that can support the transition from human drivers to fully self-driving cars, and provides a real-world testbed for the research.While significant advances have been made in environmental sensing from a vehicle or infrastructure, much less work has focused on their cooperation and collaboration. This project will consider how V2X connectivity can enable sharing of information between vehicles and infrastructure and how to develop AI algorithms that take advantage of the strengths of each – e.g., high resolution measurements from a vehicle with the high availability and behavior diversity observable from infrastructure. The research will consider i) collaborative vehicle-infrastructure sensing and control, ii) on-road behavior modeling for better understanding of human actions for less conservative and more natural CAV operation in mixed traffic situations, iii) computer vision-based AI algorithm development for continual adaption, and iv) real-time, continuous characterization of safety at intersections through trajectory analysis. The MixeD project will promote collaboration between future automated vehicles and humans and enable safer and more natural interactions. The research will explicitly consider human behavior and intentions rather than rules-of-the-road to ensure safety to improve the safety of CAVs around pedestrians and other vulnerable road users. As a Minority- and Hispanic-Serving Institution, project will provide UNLV’s diverse student population with technical skills and unique learning and training experience in the growing areas of self-driving cars, AI, and computer vision. Full project details, including publications, datasets, and code, are available at https://MixeD.sites.unlv.edu. The website will be maintained for at least five years.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.

该项目通过购买和部署先进的环境传感器和车联网（V2X）通信设备，资助内华达大学拉斯维加斯分校（UNLV）混合驾驶（Mixed）“生活实验室”的创建。该设备将增强UNLV的基本自动车辆演示平台，并为校园附近的至少三个十字路口配备高分辨率摄像头、雷达、激光雷达和V2X通信，以实现协同车辆基础设施传感和安全。该设备将为联网和自动驾驶汽车（cav）的新研究提供支持，重点是人工智能（AI），可以支持从人类驾驶员到完全自动驾驶汽车的过渡，并为研究提供现实世界的测试平台。虽然在车辆或基础设施的环境传感方面取得了重大进展，但关注它们之间的合作和协作的工作要少得多。该项目将考虑V2X连接如何实现车辆和基础设施之间的信息共享，以及如何开发利用两者优势的人工智能算法——例如，从车辆上进行高分辨率测量，同时从基础设施上观察到高可用性和行为多样性。该研究将考虑i)协同车辆-基础设施感知和控制，ii)道路行为建模，以便更好地理解混合交通情况下更保守、更自然的自动驾驶汽车操作的人类行为，iii)基于计算机视觉的人工智能算法开发，以实现持续适应，iv)通过轨迹分析实时、连续地表征十字路口的安全性。mix项目将促进未来自动驾驶汽车与人类之间的协作，实现更安全、更自然的互动。该研究将明确考虑人的行为和意图，而不是道路规则，以确保安全，以提高自动驾驶汽车在行人和其他弱势道路使用者周围的安全性。作为一个少数族裔和西班牙裔服务机构，该项目将为UNLV的多元化学生群体提供技术技能和独特的学习和培训经验，这些领域包括自动驾驶汽车、人工智能和计算机视觉。完整的项目细节，包括出版物、数据集和代码，可在https://MixeD.sites.unlv.edu上获得。该网站将至少维持5年。该奖项反映了美国国家科学基金会的法定使命，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。