Fast and real-time end to end control in autonomous racing cars through representation learning

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dc.contributor.author Venkatesh, Praveen
dc.contributor.author Rana, Rwik
dc.contributor.author Palanthandalam-Madapusi
dc.contributor.author Harish J.
dc.date.accessioned 2021-12-24T11:50:53Z
dc.date.available 2021-12-24T11:50:53Z
dc.date.issued 2021-11
dc.identifier.citation Venkatesh, Praveen; Rana, Rwik and Palanthandalam-Madapusi, Harish J., "Fast and real-time end to end control in autonomous racing cars through representation learning", arXiv, Cornell University Library, DOI: arXiv:2111.15343, Nov. 2021 en_US
dc.identifier.uri http://arxiv.org/abs/2111.15343
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/7348
dc.description.abstract The challenges presented in an autonomous racing situation are distinct from those faced in regular autonomous driving and require faster end-to-end algorithms and consideration of a longer horizon in determining optimal current actions keeping in mind upcoming maneuvers and situations. In this paper, we propose an end-to-end method for autonomous racing that takes in as inputs video information from an onboard camera and determines final steering and throttle control actions. We use the following split to construct such a method (1) learning a low dimensional representation of the scene, (2) pre-generating the optimal trajectory for the given scene, and (3) tracking the predicted trajectory using a classical control method. In learning a low-dimensional representation of the scene, we use intermediate representations with a novel unsupervised trajectory planner to generate expert trajectories, and hence utilize them to directly predict race lines from a given front-facing input image. Thus, the proposed algorithm employs the best of two worlds - the robustness of learning-based approaches to perception and the accuracy of optimization-based approaches for trajectory generation in an end-to-end learning-based framework. We deploy and demonstrate our framework on CARLA, a photorealistic simulator for testing self-driving cars in realistic environments.
dc.description.statementofresponsibility by Praveen Venkatesh, Rwik Rana and Harish J. Palanthandalam-Madapusi`
dc.language.iso en_US en_US
dc.publisher Cornell University Library en_US
dc.title Fast and real-time end to end control in autonomous racing cars through representation learning en_US
dc.type Pre-Print en_US
dc.relation.journal arXiv


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