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Sequential convex programming MPC for dynamic vehicle collision avoidance

Authors: Bassam Alrifaee; Janis Maczijewski; Dirk Abel;

Sequential convex programming MPC for dynamic vehicle collision avoidance

Abstract

This paper presents a dynamic collision avoidance controller of a connected vehicle group using Model Predictive Control (MPC). The vehicles should follow a predefined reference trajectory while simultaneously avoiding collisions on this trajectory. MPC pursues the tracking of the reference trajectory in the objective function of the optimization problem. In order to avoid collisions, MPC defines avoidance constraints between each pair of vehicles. Due to the collision avoidance constraints, the feasible set of the optimization problem is non-convex. Thus, the resulting optimization problem is non-convex. This paper presents an efficient method to solve this non-convex optimization problem based on sequential convex programming.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
17
Top 10%
Top 10%
Top 10%
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