IRES Track 1: International Research Experiences in Learning based Connected and Autonomous Vehicles (CAVs) with Real-World Implementations

IRES 轨道 1：基于学习的联网自动驾驶汽车 (CAV) 的国际研究经验及其实际实施

• 批准号: 2246347
• 负责人: Seta Bogosyan
• 金额: $ 30万
• 依托单位: CUNY City College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2246347&HistoricalAwards=false
• 关键词: IRES; Track; International; Research; Experiences

Future societies will depend more and more on artificial intelligence (AI) and networked systems, and, in turn, on autonomous vehicles (AVs) and connected AVs (CAVs) for many services and operations; i.e. transportation, urban logistics, factory automation, smart farming/agriculture and disaster management, to name a few. AVs and CAVs have strong potential to increase performance, safety, and efficiency, and as a result, to contribute to societal well-being and enhance economic growth. On the other hand, autonomous systems still fail to provide generalizable responses to vehicle, sensor, road, and environment related uncertainties, and need human intervention. Reinforcement learning (RL), an emerging branch of machine learning (ML) and control, has a lot to promise for autonomy with its capacity to address unpredictable changes in the system and environment. However, the field still has many research gaps and also suffers from the lack of practical research evaluation. Most AI based AV research is performed in simulations, on simple platforms and for simplified cases that are far from reflecting real-life uncertainties and convincing responses to changing road conditions, especially at high speeds. This 3-year project will support selected undergraduate and graduate students from US universities to tackle the open challenges of fully autonomous vehicles within a cohort experience at Istanbul Technical University (ITU) under the mentorship of subject-matter experts from ITU, from KTH, and a US based autonomous bus company (ADASTEC Corp). Eight US students (4 graduate, 4 undergraduate) will be funded each year for a 10-week on-site, hands-on research experience at ITU, with each student being in charge of his/her own level-appropriate project using ML/RL for one or more of the vehicle autonomy layers; namely, for perception, localization, motion planning, and trajectory tracking and associated practical tests on actual vehicles (with safety drivers). The algorithm tests will be performed around the ITU Campus within real-world scenarios. Each year, a different student cohort will be selected for this unique research and professional development opportunity, thereby contributing to the US leadership in the future of vehicle technologies with a well-prepared workforce. Special recruitment efforts are planned for broadening participation and recruitment of students from underrepresented communities.The adaptive optimality offered by RL provides increased performance and efficiency when compared with classical control approaches that cannot handle unstructured dynamics, often resulting in safe but highly conservative, low-performance solutions. Similarly, because of its adaptability to changes in the environment and problem dynamics, RL offers increased safety when compared with rule-based, heuristic approaches often practiced in industries to address the needs of autonomous vehicles and platforms. The student research projects for each layer of vehicle autonomy will use RL based designs to address uncertainties and disturbances faced in real-life, which have often been ignored in lab based AV research. In our perception/localization projects, novel algorithms will be developed to estimate sensing uncertainties to improve the performance of RL based motion planning and dynamic object tracking. The trajectory tracking algorithms will be based on Zero-Sum Games (ZSG) and online, model-free RL based control algorithms to address disturbances and slip/slide effects, which is also a significant research contribution. At the end of each research cycle, the modularly developed algorithms will be integrated on an actual vehicle and tested individually and in integration, first on the ITU vehicle-in-the-loop (VIL) system, then on indoor RaceCars and finally, at the ITU Living Lab environment for shuttle service scenarios.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.

未来社会将越来越依赖人工智能（AI）和网络系统，而自动驾驶汽车（AVs）和联网自动驾驶汽车（cav）将提供许多服务和运营；例如交通运输、城市物流、工厂自动化、智能农业和灾害管理等。自动驾驶汽车和自动驾驶汽车在提高性能、安全性和效率方面具有巨大的潜力，从而为社会福祉做出贡献，促进经济增长。另一方面，自动驾驶系统仍然无法对车辆、传感器、道路和环境相关的不确定性提供通用响应，需要人为干预。强化学习（RL）是机器学习（ML）和控制的一个新兴分支，它有能力应对系统和环境中不可预测的变化，因此在自主方面有很多希望。然而，该领域仍有许多研究空白，也缺乏实际的研究评价。大多数基于人工智能的自动驾驶研究都是在模拟、简单的平台和简化的情况下进行的，远远不能反映现实生活中的不确定性，也不能对不断变化的路况做出令人信服的反应，尤其是在高速行驶时。这个为期三年的项目将支持来自美国大学的部分本科生和研究生，在伊斯坦布尔技术大学（ITU）的队列体验中，在国际电联、KTH和美国自动驾驶巴士公司（ADASTEC Corp）的主题专家的指导下，解决全自动驾驶汽车的公开挑战。每年将资助8名美国学生（4名研究生，4名本科生）在国际电联进行为期10周的现场实践研究体验，每位学生将在一个或多个车辆自动驾驶层使用ML/RL负责他/她自己的适合级别的项目；即，用于感知，定位，运动规划和轨迹跟踪以及相关的实际车辆（安全驾驶员）的实际测试。算法测试将在国际电联园区周围的真实场景中进行。每年，将选择不同的学生群体参加这一独特的研究和专业发展机会，从而为美国在未来汽车技术领域的领导地位做出贡献。计划进行特别征聘工作，以扩大来自代表性不足社区的学生的参与和征聘。与无法处理非结构化动态的经典控制方法相比，RL提供的自适应最优性提供了更高的性能和效率，通常导致安全但高度保守的低性能解决方案。同样，由于其对环境变化和问题动态的适应性，与基于规则的启发式方法相比，强化学习提供了更高的安全性，这些方法通常用于满足自动驾驶车辆和平台的需求。每一层车辆自动驾驶的学生研究项目将使用基于强化学习的设计来解决现实生活中面临的不确定性和干扰，这些在基于实验室的自动驾驶研究中经常被忽视。在我们的感知/定位项目中，将开发新的算法来估计感知不确定性，以提高基于强化学习的运动规划和动态目标跟踪的性能。轨迹跟踪算法将基于零和游戏（ZSG）和基于在线、无模型RL的控制算法来解决干扰和滑动/滑动效应，这也是一个重要的研究贡献。在每个研究周期结束时，模块化开发的算法将集成到实际车辆上，并首先在国际电联环内车辆（VIL）系统上进行单独和综合测试，然后在室内赛车上进行测试，最后在国际电联生活实验室环境中进行穿梭服务场景测试。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。