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

TL;DR

AutoDRIVE is an integrated digital twin ecosystem designed to facilitate autonomous driving research and education through modular, scalable simulation and hardware-in-the-loop testing, supporting diverse applications and emergent technologies.

Contribution

The paper introduces AutoDRIVE, a comprehensive, flexible platform that combines hardware and software simulation for autonomous driving and smart city applications, with demonstrated use-cases.

Findings

Successful autonomous parking with probabilistic robotics

Behavioral cloning via deep imitation learning

Intersection traversal with vehicle-to-vehicle communication

Abstract

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.