MRI: Acquisition of a Testbed of Connected Autonomous MicroTransit Vehicles

MRI：获取联网自主微型交通车辆的测试台

• 批准号: 2018879
• 负责人: Ali Karimoddini
• 金额: $ 55万
• 依托单位: North Carolina Agricultural & Technical State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-15 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2018879&HistoricalAwards=false
• 关键词: MRI; Acquisition; Testbed; Connected; Autonomous

This Major Research Instrumentation (MRI) award supports the acquisition of three connected autonomous microtransit vehicles (CAV) to conduct interdisciplinary research leading to the development, implementation, and validation of advanced transportation solutions. Microtransit vehicles can play a key role in meeting the transportation needs in population-dense areas such as downtowns or campus settings and in mobility-disadvantaged areas. The testbed will be featured in a new course and will enhance CAV-related undergraduate and graduate education. It will also be used to enhance the efforts of the Inclusive Engineering Consortium, K-12 activities for underrepresented groups, and outreach to the broader Greensboro community.The testbed provides a multi-user configurable research platform that leverages autonomy and transportation research capabilities at NC A&T State University. The instrumentation enables fundamental research on transportation, communications, networks, and real-time systems. The research projects will enable advances in cooperative control of autonomous vehicles, on-demand mobility services to meet distributed demands, validation and autonomy-enabled algorithms for CAVs, testing and fault diagnosis of networked AVs and resilient Vehicle-to-infrastructure and vehicle to vehicle communication. The CAV testbed provides a catalyst for new regional and national research partnerships with academia, industry, and the national labs to advance research on networks of autonomous vehicles as an important component of public transportation systems.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.

这一重大研究仪器(MRI)奖支持收购三辆联网的自动微型运输车辆(CAV)，以进行跨学科研究，从而开发、实施和验证先进的交通解决方案。微型交通工具在满足市中心或校园等人口密集地区以及行动不便地区的交通需求方面可以发挥关键作用。试验床将在一门新课程中亮相，并将加强与CAV相关的本科生和研究生教育。它还将用于加强包容性工程联盟的努力，为代表不足的群体开展K-12活动，以及向更广泛的格林斯伯勒社区推广。TestBed提供了一个多用户可配置的研究平台，利用了北卡罗来纳州立大学的自主和交通研究能力。该仪器使交通、通信、网络和实时系统的基础研究成为可能。这些研究项目将推动自动驾驶车辆的合作控制、满足分布式需求的按需移动服务、CAV的验证和自主启用的算法、联网AVs的测试和故障诊断以及弹性车辆到基础设施和车辆到车辆的通信。CAV试验台为与学术界、产业界和国家实验室建立新的区域和国家研究伙伴关系提供了催化剂，以推动自动驾驶车辆网络作为公共交通系统的重要组成部分的研究。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

Pedestrian Detection for Autonomous Cars: Inference Fusion of Deep Neural Networks

• DOI: 10.1109/tits.2022.3210186
• 发表时间: 2022-12
• 期刊: IEEE Transactions on Intelligent Transportation Systems
• 影响因子: 8.5
• 作者: M. Islam;vvAbdullah Al Redwan Newaz;A. Karimoddini

A Computationally Effective Pedestrian Detection using Constrained Fusion with Body Parts for Autonomous Driving

• DOI: 10.1109/irc52146.2021.00024
• 发表时间: 2021-11
• 期刊: 2021 Fifth IEEE International Conference on Robotic Computing (IRC)
• 作者: M. Islam;Abdullah Al Redwan Newaz;Renran Tian;A. Homaifar;A. Karimoddini

Pedestrian Detection for Autonomous Cars: Occlusion Handling by Classifying Body Parts

• DOI: 10.1109/smc42975.2020.9282839
• 发表时间: 2020-10
• 期刊: 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
• 作者: M. Islam;Abdullah Al Redwan Newaz;B. Gokaraju;A. Karimoddini

A Pedestrian Detection and Tracking Framework for Autonomous Cars: Efficient Fusion of Camera and LiDAR Data

• DOI: 10.1109/smc52423.2021.9658639
• 发表时间: 2021-08
• 期刊: 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
• 作者: M. Islam;Abdullah Al Redwan Newaz;A. Karimoddini

Connected autonomous vehicles: State of practice

• DOI: 10.1002/asmb.2772
• 发表时间: 2023-05
• 期刊: Applied Stochastic Models in Business and Industry
• 影响因子: 1.4
• 作者: M. Islam;Abdullah Al Redwan Newaz;Li Song;B. Lartey;Shih-Chun Lin;Wei Fan;Ali Hajbabaie;M. Khan;Alireza Partovi;Tienake Phuapaiboon;A. Homaifar;A. Karimoddini