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Motion planning for urban autonomous driving using Bezier curves and MPC

Abstract : — This paper presents a real-time motion planning scheme for urban autonomous driving that will be deployed as a basis for cooperative maneuvers defined in the European project AutoNet2030. We use a path-velocity decomposition approach to separate the motion planning problem into a path planning problem and a velocity planning problem. The path planner first generates a collision-free piecewise linear path and then uses quintic Bézier curves to smooth the path with C 2 continuity. A derive-free optimization technique Subplex is used to further smooth the curvature of the path in a best-effort basis. The velocity planner generates an optimal velocity profile along the reference path using Model Predictive Control (MPC), taking into account user preferences and cooperative maneuver requirements. Simulation results are presented to validate the approach, with special focus on the flexibility, cooperative-awareness and efficiency of the algorithms.
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Contributor : Fabien Moutarde <>
Submitted on : Tuesday, January 3, 2017 - 5:04:36 PM
Last modification on : Thursday, September 24, 2020 - 5:04:02 PM
Long-term archiving on: : Tuesday, April 4, 2017 - 2:48:41 PM


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Xiangjun Qian, Iñaki Navarro, Arnaud de la Fortelle, Fabien Moutarde. Motion planning for urban autonomous driving using Bezier curves and MPC. 19th IEEE International Conference on Intelligent Transportation Systems (ITSC'2016), Nov 2016, Rio de Janeiro, Brazil. pp.826 - 833, ⟨10.1109/ITSC.2016.7795651⟩. ⟨hal-01425647⟩



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