The engineering community currently encounters significant challenges in the development of intelligent transportation algorithms that can be transferred from simulation to reality with minimal effort. This can be achieved by robustifying the algorithms using domain adaptation methods and/or by adopting cutting-edge tools that help support this objective seamlessly. This work presents AutoDRIVE, an openly accessible digital twin ecosystem designed to facilitate synergistic development, simulation and deployment of cyber-physical solutions pertaining to autonomous driving technology; and focuses on bridging the autonomy-oriented simulation-to-reality (sim2real) gap using the proposed ecosystem. In this paper, we extensively explore the modeling and simulation aspects of the ecosystem and substantiate its efficacy by demonstrating the successful transition of two candidate autonomy algorithms from simulation to reality to help support our claims: (i) autonomous parking using probabilistic robotics approach; (ii) behavioral cloning using deep imitation learning. The outcomes of these case studies further strengthen the credibility of AutoDRIVE as an invaluable tool for advancing the state-of-the-art in autonomous driving technology.

翻译：工程界目前在开发能够以最小代价从仿真迁移至现实的智能交通算法方面面临重大挑战。通过采用域自适应方法增强算法的鲁棒性，和/或采用能无缝支持该目标的前沿工具，可以实现这一目标。本文介绍了AutoDRIVE——一个开源性数字孪生生态系统，旨在促进自动驾驶技术相关的信息物理系统的协同开发、仿真与部署；并重点探讨如何利用该生态系统弥合面向自主性的仿真到现实（sim2real）鸿沟。我们深入探索了该生态系统的建模与仿真维度，并通过成功实现两个候选自主算法从仿真到现实过渡的案例验证其有效性：(i) 基于概率机器人方法的自主泊车；(ii) 基于深度模仿学习的行为克隆。这些案例研究的结果进一步强化了AutoDRIVE作为推动自动驾驶技术发展前沿的重要工具的可信度。