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SYSTEM AND METHOD FOR PROVIDING DRIVER BEHAVIOR CLASSIFICATION AT INTERSECTIONS AND VALIDATION ON LARGE NATURALISTIC DATA SETS

机译:在交叉点处提供驾驶员行为分类和对大型自然数据集进行验证的系统和方法

摘要

A system and method for predicting whether a vehicle will come to a stop at an intersection is provided. Generally, the system contains a memory; and a processor configured by the memory to perform the steps of: generating a prediction of whether the vehicle will or will not stop at the intersection before a first time based on vehicle data measured during a first time window; and at a second time, the second time being before the first time and approximately equal to a time at which the time window ends, providing an indication that the vehicle will not stop at the intersection before the first time based upon the prediction, wherein generating the prediction comprises using a classification model, the classification model configured to indicate whether the vehicle will or will not stop at the intersection before the first time based on a plurality of input parameters, and wherein the plurality of input parameters are selected from the group consisting of speed, acceleration, and distance to the intersection.
机译:提供了一种用于预测车辆是否将在交叉路口停车的系统和方法。通常,系统包含一个内存。以及由该存储器配置的处理器执行以下步骤:基于在第一时间窗口期间测量的车辆数据,生成对车辆在第一时间之前是否将在交叉路口处停止的预测;在第二时间,第二时间在第一时间之前,并且大约等于时间窗口结束的时间,基于该预测,提供车辆在第一时间之前不会在交叉路口停车的指示,其中,该预测包括使用分类模型,该分类模型被配置为基于多个输入参数来指示车辆是否将在第一时间之前在十字路口停车,并且其中多个输入参数是从包括以下各项的组中选择的:速度,加速度和到交叉路口的距离。

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