文章基本信息

标题：Probabilistic Bounds on Vehicle Trajectory Prediction Using Scenario Approach
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作者：Xun Shen ; Xingguo Zhang ; Pongsathorn Raksincharoensak 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：2385-2390
DOI：10.1016/j.ifacol.2020.12.038
语种：English
出版社：Elsevier
摘要：AbstractThe automotive industry concerns about improving road safety. One of the major challenges is to assess road risk and react accordingly in order to avoid accidents. This requires predicting the evolution of the surrounding vehicle trajectories. However, the prediction involves uncertainties from driver operations and ground situations. It is critical to obtain the vehicle trajectory prediction with probabilistic-guarantee bounds. This contribution paper proposes a novel approach to obtain probabilistic ellipsoidal bounds for vehicle trajectory prediction. The vehicle dynamics model adopts a classical bicycle model. The uncertainty of the future trajectory is from the driver’s intend and road condition which can be simplified by setting some parameters of the vehicle dynamics model as a stochastic model. Then, a stochastic optimization problem is formulated to obtain the probabilistic ellipsoidal bounds on the future vehicle trajectories. The proposed approach is validated in a numerical simulation which shows the relationship between the computation complexity and the conservatism of the probabilistic ellipsoidal bounds. The proposed method can be generally used for a physics-based motion method, maneuver-based motion method, and interaction-aware motion method by defining the probability distribution of uncertain variables differently.
关键词：KeywordsVehiclesmotion predictionmotion modelprobabilistic boundscenario approach