AutoDRIVE: A Comprehensive, Flexible and Integrated Digital Twin Ecosystem for Autonomous Driving Research & Education

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