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Long-term trajectory classification and prediction of commercial vehicles for the application in advanced driver assistance systems

机译:商用车在高级驾驶员辅助系统中的长期轨迹分类和预测

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摘要

Current development of advanced driver assistance systems (ADAS), e.g., for collision mitigation are increasingly concerned about the protection of other road users. Environment perception provides objects like cars or pedestrians. A prediction of the system vehicle's path is required to decide about the relevance of the objects for a system reaction or to reduce the CAN load in advance. A standard measure for the object criticality is the time-to-collision, where the system vehicle's path is predicted under the assumption of constant acceleration and yaw rate or using lane markings. Lane markings often are not available on urban streets, and the vehicles do not necessarily follow the own lane, e.g., due to parked cars at the road side. This paper proposes an approach that uses maneuver classification based on a combination of the longest-common-subsequence method and a Bayesian classifier. The knowledge obtained about the maneuver in the classification step is used to predict the future trajectory in a parameterizable way. The approach is evaluated in comparison to a prediction with constant acceleration and constant yaw rate using recorded data from more than 20 hours of driving.
机译:例如,用于减轻碰撞的高级驾驶员辅助系统(ADAS)的当前开发越来越关注其他道路使用者的保护。环境感知提供了诸如汽车或行人之类的物体。需要对系统车辆的路径进行预测,以决定对象与系统反应的相关性,或预先减少CAN负载。对象临界的标准度量是碰撞时间,其中系统车辆的路径是在恒定加速度和偏航率或使用车道标记的假设下预测的。在城市街道上通常不提供车道标记,并且例如由于在路边停放的汽车,车辆不一定遵循自己的车道。本文提出了一种基于最长公共子序列方法和贝叶斯分类器的机动分类方法。在分类步骤中获得的有关操纵的知识用于以可参数化的方式预测未来的轨迹。使用来自20多个小时的行驶记录数据,与采用恒定加速度和恒定偏航率的预测进行比较,评估了该方法。

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  • 来源
    《American Control Conference;ACC》|2012年|p.2904- 2909|共6页
  • 会议地点 Montreal(CA)
  • 作者

    Otto, Carola;

  • 作者单位

    Department of Advanced Engineering Daimler Trucks Daimler AG 70327 Stuttgart-Untertuerkheim Germany;

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