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On-line Maneuver Identification in Highway Traffic Using Elastic Template Matching ?

机译:使用弹性模板匹配在高速公路交通中进行在线机动识别

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Early identification and interpretation of traffic maneuvers will become key elements of modern driver assistance systems. Currently, only simple traffic situations can be detected by using adecision tree(DT) procedure in most cases. Even though this approach is straightforward, it still requires an expert to build a different DT for every maneuver. Moreover, for complex driving behaviours such simple logic identification does not work. In the present work we develop a template (or pattern) matching (TM) algorithm for maneuver recognition in highway traffic with a particular focus on acut-inmaneuver. By using maneuver patterns we are able to consider scenarios of arbitrary complexity and make the identification procedure more systematic and with minimal man expert involvement. The key element of the proposed TM algorithm is the settlement of a distance measure. We usedynamic time warping(DTW) method for an optimal non-linear alignment between two sequences under certain restrictions. It enables a correct assignment of the same maneuver but having different dynamics (speed, aggressiveness, etc.), and is robust to data with missing values. We illustrate the proposed method by cut-in detection from real traffic data and compare it to the standard DT identification.
机译:尽早识别和解释交通操纵将成为现代驾驶员辅助系统的关键要素。当前,在大多数情况下,使用决策树(DT)过程只能检测到简单的交通状况。尽管此方法很简单,但仍需要专家为每次操作构建不同的DT。而且,对于复杂的驾驶行为,这种简单的逻辑识别不起作用。在当前的工作中,我们开发了一种模板(或模式)匹配(TM)算法,用于高速公路交通中的机动识别,尤其侧重于切入机动。通过使用机动模式,我们能够考虑任意复杂性的情况,并使识别过程更加系统化,而人工干预最少。提出的TM算法的关键要素是距离度量的确定。我们使用动态时间规整(DTW)方法在一定限制下实现两个序列之间的最佳非线性比对。它可以正确分配相同的操作,但具有不同的动态性(速度,攻击性等),并且对于缺少值的数据具有鲁棒性。我们通过从实际交通数据中进行插入检测来说明所提出的方法,并将其与标准DT识别进行比较。

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