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、灵活且开放获取的生态系统。本研究提出我们为开发此类综合性研究与教育生态系统所做的努力——命名为AutoDRIVE，旨在协同地原型设计、仿真并部署与自动驾驶及智慧城市管理相关的信息物理解决方案。AutoDRIVE 兼具软件在环与硬件在环测试接口，配备公开可访问的缩比车辆及基础设施组件。该生态兼容多种开发框架，并支持通过本地及分布式计算实现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，本研究通过展示四种部署用例，重点阐述了该生态的各项互补特性与能力：（i）基于概率机器人方法的自动驾驶泊车（涵盖建图、定位、路径规划与控制）；（ii）结合计算机视觉与深度模仿学习的行为克隆；（iii）利用车-车通信与深度强化学习的路口通行；（iv）依托车-基础设施通信与物联网的智慧城市管理。