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The engineering community currently encounters significant challenges in thedevelopment of intelligent transportation algorithms that can be transferredfrom simulation to reality with minimal effort. This can be achieved byrobustifying the algorithms using domain adaptation methods and/or by adoptingcutting-edge tools that help support this objective seamlessly. This workpresents AutoDRIVE, an openly accessible digital twin ecosystem designed tofacilitate synergistic development, simulation and deployment of cyber-physicalsolutions pertaining to autonomous driving technology; and focuses on bridgingthe autonomy-oriented simulation-to-reality (sim2real) gap using the proposedecosystem. In this paper, we extensively explore the modeling and simulationaspects of the ecosystem and substantiate its efficacy by demonstrating thesuccessful transition of two candidate autonomy algorithms from simulation toreality to help support our claims: (i) autonomous parking using probabilisticrobotics approach; (ii) behavioral cloning using deep imitation learning. Theoutcomes of these case studies further strengthen the credibility of AutoDRIVEas an invaluable tool for advancing the state-of-the-art in autonomous drivingtechnology.

Towards Sim2Real Transfer of Autonomy Algorithms using AutoDRIVE Ecosystem