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- W4300482099 abstract "In this paper, we present a Model Predictive Control (MPC) framework based on path velocity decomposition paradigm for autonomous driving. The optimization underlying the MPC has a two layer structure wherein first, an appropriate path is computed for the vehicle followed by the computation of optimal forward velocity along it. The very nature of the proposed path velocity decomposition allows for seamless compatibility between the two layers of the optimization. A key feature of the proposed work is that it offloads most of the responsibility of collision avoidance to velocity optimization layer for which computationally efficient formulations can be derived. In particular, we extend our previously developed concept of time scaled collision cone (TSCC) constraints and formulate the forward velocity optimization layer as a convex quadratic programming problem. We perform validation on autonomous driving scenarios wherein proposed MPC repeatedly solves both the optimization layers in receding horizon manner to compute lane change, overtaking and merging maneuvers among multiple dynamic obstacles." @default.
- W4300482099 created "2022-10-03" @default.
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- W4300482099 date "2017-12-13" @default.
- W4300482099 modified "2023-09-26" @default.
- W4300482099 title "Model Predictive Control for Autonomous Driving Based on Time Scaled Collision Cone" @default.
- W4300482099 doi "https://doi.org/10.48550/arxiv.1712.04965" @default.
- W4300482099 hasPublicationYear "2017" @default.
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