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• W2498889188 abstract "The complex public traffic environment requires an autonomous vehicle to have the ability of planning and executing a sequence of different maneuvers, such as maintaining cruising speed, changing speed to follow a vehicle in front or to lead a vehicle in the rear, and changing a lane when necessary and possible. This paper presents a hierarchical hybrid predictive control framework that integrates the discrete optimization problem of maneuver selection with particle motion-based model predictive trajectory guidance for an autonomous road vehicle. To address the challenge of solving the resulting mixed integer nonlinear programming (MINP) problem efficiently for online implementation, a relaxation method is introduced that transforms the MINP problem into a nonlinear programming problem with improved feasibility for online implementation. The performance of the proposed framework is illustrated via simulations of the autonomous vehicle in highway scenarios." @default.
• W2498889188 created "2016-08-23" @default.
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• W2498889188 date "2016-07-01" @default.
• W2498889188 modified "2023-10-16" @default.
• W2498889188 title "Optimal assigner decisions in a hybrid predictive control of an autonomous vehicle in public traffic" @default.
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• W2498889188 doi "https://doi.org/10.1109/acc.2016.7525450" @default.
• W2498889188 hasPublicationYear "2016" @default.
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