Abstract
Self-driving vehicle simulation exhibits human-computer interaction. There already exist many commercial autonomous vehicle simulation tools, however, they all require human inputs, which is limited. In this paper, we present our early work as building learning enabled autonomous vehicle simulations, or LEAVS. We started from forking an open-sourced tool AirSim, and legoed it up with our smart sensors, data collection tools, and algorithm platform, to test learning-based algorithm, such as object detection. We report our current platform now can successfully enable many state-of-the-art object recognition algorithm, combined with smart vehicles control to drive independently in thousands of simulations. LEAVS is the platform to perform learning-based simulations for unmanned autonomous vehicles.
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Acknowledgement
This research was supported by the grant from the Tencent Rhino Grant award (11002675), by the grant from the National Science Foundation China (NSFC) (617022501006873), and by the grant from Jiangxi Province Science Foundation for Youths (708237400050).
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Wu, W., Wei, Y., Gao, C., Xu, Z. (2019). Leavs: A Learning-Enabled Autonomous Vehicle Simulation Platform. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2018. Communications in Computer and Information Science, vol 1006. Springer, Singapore. https://doi.org/10.1007/978-981-13-7986-4_9
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DOI: https://doi.org/10.1007/978-981-13-7986-4_9
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