Abstract
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.
Highlights
Deep Learning requires Big Data to learn behaviors from infrequent edge cases or anomalies
There is a need to capture large volumes of high-quality data with minimal supervision, and for inexpensive physical test platforms to validate real-world edge-case performance. This manuscript proposes gamified simulation as a means of collecting bulk data for self-driving and a platform based on commodity hardware for real-world algorithm validation
We aim to develop a simulator based on a game engine capable of generating meaningful data to inform Deep Learning self-driving behavior cloning models capable of real-world operation, with the benefit of being able to crowdsource human control and trusting the resulting input data as being “clean”
Summary
Deep Learning requires Big Data to learn behaviors from infrequent edge cases or anomalies. There is a need to capture large volumes of high-quality data with minimal supervision, and for inexpensive physical test platforms to validate real-world edge-case performance This manuscript proposes gamified simulation as a means of collecting bulk data for self-driving and a platform based on commodity hardware for real-world algorithm validation. Our approach furthers proven techniques to allow the generation of data from unskilled drivers and through the validation of resulting algorithms on lower cost and more widely accessible hardware than is used today This enables large scale, rapid data capture and real-world model validation that may be translated to costlier and higher fidelity test platforms including full-scale vehicles. The result will be improved algorithms capable of responding well to infrequent but impactful edge cases missed by other tools, while a physical test platform will validate model performance in the real-world and capture data for transfer learning (if necessary)
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