Prototyping and validating hardware-software components, sub-systems and systems within the intelligent transportation system-of-systems framework requires a modular yet flexible and open-access ecosystem. This work presents our attempt towards developing such a comprehensive research and education ecosystem, called AutoDRIVE, for synergistically prototyping, simulating and deploying cyber-physical solutions pertaining to autonomous driving as well as smart city management. AutoDRIVE features both software as well as hardware-in-the-loop testing interfaces with openly accessible scaled vehicle and infrastructure components. The ecosystem is compatible with a variety of development frameworks, and supports both single and multi-agent paradigms through local as well as distributed computing. Most critically, AutoDRIVE is intended to be modularly expandable to explore emergent technologies, and this work highlights various complementary features and capabilities of the proposed ecosystem by demonstrating four such deployment use-cases: (i) autonomous parking using probabilistic robotics approach for mapping, localization, path planning and control; (ii) behavioral cloning using computer vision and deep imitation learning; (iii) intersection traversal using vehicle-to-vehicle communication and deep reinforcement learning; and (iv) smart city management using vehicle-to-infrastructure communication and internet-of-things.

翻译：在智能交通系统的系统之系统框架内对软硬件组件、子系统及系统进行原型设计与验证，需要一种模块化、灵活且开放接入的生态系统。本文介绍了我们为开发此类综合性研究与教育生态系统（称为AutoDRIVE）所做的工作，旨在协同原型设计、仿真并部署涉及自动驾驶及智慧城市管理的网络物理解决方案。AutoDRIVE提供软件在环与硬件在环测试接口，并配备了开放访问的缩比车辆及基础设施组件。该生态系统兼容多种开发框架，通过本地及分布式计算支持单智能体与多智能体范式。尤为关键的是，AutoDRIVE被设计为可模块化扩展以探索新兴技术，本文通过演示四种部署用例，重点阐述了该生态系统各项互补特性与能力：（i）基于概率机器人学方法实现建图、定位、路径规划与控制的自主泊车；（ii）基于计算机视觉与深度模仿学习的行为克隆；（iii）基于车-车通信与深度强化学习的交叉路口通行；（iv）基于车-基础设施通信与物联网的智慧城市管理。