文章基本信息

标题：Stochastic Model Predictive Velocity Control for Automobiles Considering Uncertainty of Nearby Vehicles
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作者：Kentaro Hashimoto ; Yoshihide Mizushima ; Koji Shibata 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：15596-15601
DOI：10.1016/j.ifacol.2020.12.2493
语种：English
出版社：Elsevier
摘要：AbstractIn this paper, we present vehicle velocity control based on stochastic model predictive control applied to an actual automobile near other vehicles with uncertain motion. Modern external sensors can measure the pose and velocity of transportation participants, whose motion can be predicted utilizing a model; however, observation and process noise results in uncertainty. Thus, near other vehicles, the velocity of the ego vehicle should be reduced to account for the variance of nearby vehicle motion while suppressing the reduction of speed. In this paper, we utilize stochastic model predictive control to reduce the expectation of relative velocity with respect to nearby vehicles during passing. We evaluate the proposed control using numerical simulation and an experiment with an automobile developed for self-driving and equipped with GNSS, radar, and LiDAR. The results show that the vehicle velocity is automatically reduced based on the expectation of relative velocity calculated from its probabilistic distribution.
关键词：KeywordsSelf-driving vehicleAutomobileStochastic model predictive controlVelocity controlKalman filterRelative velocityExpectationRisk prediction