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的框架内对硬件-软件组件、子系统及系统进行原型开发与验证，需要模块化且灵活开放的生态系统。本研究展示了我们为开发这样一个综合性研究与教育生态系统（名为AutoDRIVE）所做的工作，该生态旨在协同实现面向自主驾驶与智慧城市管理的信息物理解决方案的原型开发、仿真及部署。AutoDRIVE同时提供软件在环与硬件在环测试接口，并包含可公开访问的缩比车辆与基础设施组件。该生态兼容多种开发框架，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被设计为可通过模块化扩展以探索新兴技术，本研究通过展示四个典型部署用例，凸显该生态系统的互补特性与能力：（i）采用概率机器人方法进行地图构建、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）利用车-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。