A Gamified Simulator and Physical Platform for Self-Driving Algorithm Training and Validation
Author(s)
Pappas, Georgios; Siegel, Joshua E.; Politopoulos, Konstantinos; Sun, Yongbin
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We identify the need for an easy-to-use self-driving simulator where game mechanics implicitly encourage high-quality data capture and an associated low-cost physical test platform. We design such a simulator incorporating environmental domain randomization to enhance data generalizability and a low-cost physical test platform running the Robotic Operating System. A toolchain comprising a gamified driving simulator and low-cost vehicle platform is novel and facilitates behavior cloning and domain adaptation without specialized knowledge, supporting crowdsourced data generation. This enables small organizations to develop certain robust and resilient self-driving systems. As proof-of-concept, the simulator is used to capture lane-following data from AI-driven and human-operated agents, with these data training line following Convolutional Neural Networks that transfer without domain adaptation to work on the physical platform.
Date issued
2021-05Department
Massachusetts Institute of Technology. Department of Mechanical EngineeringJournal
Electronics
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Electronics 10 (9): 1112 (2021)
Version: Final published version
ISSN
2079-9292