Waterloo Autonomous Golf Cart Testbed

自动车辆(AVs)提高了安全性、机动性、可达性、能效，并创造了巨大的经济增长机会。控制和运动规划方面的进步对无人机的部署至关重要，但仍然存在重大挑战，特别是在不可预测的、行人密集的环境(如游乐园和机场航站楼)以及在更有可能发生横向和纵向碰撞的复杂、多车辆和高度动态的情况下(如环形交叉口和交叉口)，将自动驾驶汽车用作个人交通工具的复杂性。 我们的团队(拉什加里安·阿扎德，Jeon，Dautenhahn，Fischmeister)正在为行人和道路环境进行安全、符合社会要求和可靠的AV控制和运动规划的尖端研究。我们要求一个全尺寸的反病毒试验台，具体地说，一辆自动高尔夫球车(AGC)，以测试我们新的反病毒算法。所要求的AGC将用于进行必要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要求的运动规划，以及(Iii)环形交叉口和交叉口的安全决策和控制。我们开发了用于环形交叉口驾驶的AV控制器，成功地将模型预测控制和强化学习相结合，在复杂的多车辆交通场景中实现安全运动。我们还在使用控制屏障功能的概念设计具有安全保证的新型数据驱动的AV控制器。我们的早期研究充分利用了虚拟模拟和大规模汽车实验，但我们需要进行全尺寸实验的车辆来验证我们新的AV控制和实际情况下的决策技术。所要求的带有车载传感器和可编程控制器的AGC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反病毒算法的修订版本，并根据实际情况对其进行优化。 所要求的试验台将为在步行区和复杂的道路场景中安全和高效使用自动驾驶汽车提供独特的技术突破机会。反病毒部门对高技能人才的需求强劲且不断增长。由AGC促成的研究将为下一代视听创新者提供一个独特的培训环境，使他们在采用运动规划和控制技术方面拥有实践经验，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、无人驾驶送货和按需移动服务以及自动班车外，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遇。

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.