Waterloo Autonomous Golf Cart Testbed

滑铁卢自动高尔夫球车测试台

• 批准号: RTI-2021-00103
• 负责人: LashgarianAzad, Nasser
• 金额: $ 10.93万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Research Tools and Instruments
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=718779
• 关键词: Waterloo; Autonomous; Golf; Cart; Testbed

Automated vehicles (AVs) improve safety, mobility, accessibility, energy efficiency and create significant economic growth opportunities. Advances in control and motion-planning are crucial to AV deployment, but major challenges continue to arise, particularly related to the complexities of using AVs as personal transporters in unpredictable, pedestrian-rich environments (e.g., amusement parks and airport terminals) and in complex, multi-vehicle, and highly dynamic situations (e.g., roundabouts and intersections) where lateral and longitudinal collisions are more likely. Our team (Lashgarian Azad, Jeon, Dautenhahn, Fischmeister) is conducting cutting-edge research on safe, socially-compliant, and reliable AV control & motion planning for both pedestrian-rich and road environments. We are requesting a full-scale AV testbed, specifically, an autonomous golf cart (AGC), to test our new AV algorithms. The requested AGC will be used to conduct the required experimental work and data collections for validating and optimizing our methodologies for the following research topics: (i) traffic participant behavior predictions, (ii) socially-compliant motion planning in crowded environments, and (iii) safe decision-making and controls at roundabouts and intersections. We have developed AV controllers for roundabout driving that successfully integrate model predictive control and reinforcement learning to achieve safe motions in complex multi-vehicle traffic scenarios. We are also designing novel data-driven AV controllers with safety assurance using the concept of control barrier functions. Our early-stage research takes full advantage of virtual simulations and scaled car experiments, but we require a vehicle for full-scale experiments to validate our novel AV controls and decision-making techniques for practical situations. The requested AGC instrumented with on-board sensors and a programmable controller is immediately needed to serve as our full-scale testbed. The programmable controller allows quick deployments of revised versions of our AV algorithms and optimizing them for real-life situations. The requested testbed will enable unique opportunities for technological breakthroughs towards the safe and efficient use of AVs in pedestrian zones and complex, on-road scenarios. There are strong and growing demands for highly skilled people in the AV sector. The research enabled by the AGC will provide a unique training environment for the next generation of AV innovators, equipping them with hands-on experience in adopting motion planning and control technologies while advancing their knowledge in learning-based methods, human-machine interactions, and safety assurance techniques. In addition to driverless cars, self-driving delivery & on-demand mobility services, and autonomous shuttles, our research results will impact other industries like construction, mining, farming, and forestry, creating great opportunities to achieve major economic benefits.

自动驾驶汽车（AV）提高了安全性、机动性、可达性和能源效率，并创造了重大的经济增长机会。控制和运动规划方面的进步对AV部署至关重要，但主要挑战仍在不断出现，特别是与在不可预测的、富含二氧化碳的环境中使用AV作为个人运输工具的复杂性有关（例如，游乐园和机场航站楼）和复杂、多车辆和高度动态的情况（例如，环形交叉口和交叉口），在这些地方横向和纵向碰撞更有可能发生。 我们的团队（Lashgarian Azad，Jeon，Dautenhahn，Fischmeister）正在进行安全，社会兼容和可靠的AV控制和运动规划的尖端研究，适用于公路和道路环境。我们正在申请一个全面的AV测试平台，特别是一辆自动高尔夫球车（AGC），以测试我们的新AV算法。所请求的AGC将用于进行所需的实验工作和数据收集，以验证和优化我们针对以下研究主题的方法：（i）交通参与者行为预测，（ii）拥挤环境中符合社会规范的运动规划，以及（iii）安全决策和控制环形交叉口和交叉口。我们开发了用于环形交叉路口驾驶的AV控制器，成功集成了模型预测控制和强化学习，以在复杂的多车辆交通场景中实现安全运动。我们还在设计新型的数据驱动AV控制器，使用控制屏障功能的概念来确保安全性。我们的早期研究充分利用了虚拟仿真和缩放汽车实验，但我们需要一辆汽车进行全尺寸实验，以验证我们的新型AV控制和决策技术在实际情况下的作用。所要求的AGC仪表板上的传感器和可编程控制器是立即需要作为我们的全尺寸试验台。可编程控制器允许快速部署我们的AV算法的修订版本，并针对实际情况进行优化。 所要求的测试台将为技术突破提供独特的机会，以在步行区和复杂的道路场景中安全有效地使用自动驾驶汽车。AV行业对高技能人才的需求强劲且不断增长。 AGC支持的研究将为下一代AV创新者提供独特的培训环境，为他们提供采用运动规划和控制技术的实践经验，同时提高他们在基于学习的方法，人机交互和安全保证技术方面的知识。除了无人驾驶汽车、自动驾驶交付和按需移动服务以及自动驾驶班车之外，我们的研究成果还将影响建筑、采矿、农业和林业等其他行业，为实现重大经济效益创造巨大机会。