A Hierarchical Model Predictive Control Framework for On-road Formation Control of Autonomous Vehicles

Abstract : This paper presents an approach for formation control of autonomous vehicles traversing along a multi-lane road with obstacles and traffic. A major challenge in this problem is a requirement for integrating individual vehicle behaviors such as lane-keeping and collision avoidance with a global formation maintenance behavior. We propose a hierarchical Model Predictive Control (MPC) approach. The desired formation is modeled as a virtual structure evolving curvilinearly along a centerline, and vehicle configurations are expressed as curvilinear relative longitudinal and lateral offsets from the virtual center. At high-level, the trajectory generation of the virtual center is achieved through an MPC framework, which allows various on-road driving constraints to be considered in the optimization. At low-level, a local MPC controller computes the vehicle inputs in order to track the desired trajectory, taking into account more personalized driving constraints. High-fidelity simulations show that the proposed approach drives vehicles to the desired formation while retains some freedom for individual vehicle behaviors.
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Submitted on : Wednesday, July 20, 2016 - 4:30:40 PM
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  • HAL Id : hal-01298637, version 2

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Xiangjun Qian, Arnaud De La Fortelle, Fabien Moutarde. A Hierarchical Model Predictive Control Framework for On-road Formation Control of Autonomous Vehicles. 2016 IEEE Intelligent Vehicle Symposium, Jun 2016, Göteborg, Sweden. 2016. 〈hal-01298637v2〉

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