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  • 标题:Optimization of Vehicular Trajectories under Gaussian Noise Disturbances
  • 本地全文:下载
  • 作者:Juan-Bautista Tomas-Gabarron ; Esteban Egea-Lopez ; Joan Garcia-Haro
  • 期刊名称:Future Internet
  • 电子版ISSN:1999-5903
  • 出版年度:2013
  • 卷号:5
  • 期号:1
  • 页码:1-20
  • DOI:10.3390/fi5010001
  • 语种:English
  • 出版社:MDPI Publishing
  • 摘要:Nowadays, research on Vehicular Technology aims at automating every single mechanical element of vehicles, in order to increase passengers’ safety, reduce human driving intervention and provide entertainment services on board. Automatic trajectory tracing for vehicles under especially risky circumstances is a field of research that is currently gaining enormous attention. In this paper, we show some results on how to develop useful policies to execute maneuvers by a vehicle at high speeds with the mathematical optimization of some already established mobility conditions of the car. We also study how the presence of Gaussian noise on measurement sensors while maneuvering can disturb motion and affect the final trajectories. Different performance criteria for the optimization of such maneuvers are presented, and an analysis is shown on how path deviations can be minimized by using trajectory smoothing techniques like the Kalman Filter. We finalize the paper with a discussion on how communications can be used to implement these schemes.
  • 关键词:vehicle safety; optimum control; optimum maneuvering
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