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Multi-Object Tracking with Interacting Vehicles and Road Map Information

机译:具有交互车辆和路线图信息的多目标跟踪

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In many applications, tracking of multiple objects is crucial for a perception of the current environment. Most of the present multi-object tracking algorithms assume that objects move independently regarding other dynamic objects as well as the static environment. Since in many traffic situations objects interact with each other and in addition there are restrictions due to drivable areas, the assumption of an independent object motion is not fulfilled. This paper proposes an approach adapting a multi-object tracking system to model interaction between vehicles, and the current road geometry. Therefore, the prediction step of a Labeled Multi-Bernoulli filter is extended to facilitate modeling interaction between objects using the Intelligent Driver Model. Furthermore, to consider road map information, an approximation of a highly precise road map is used. The results show that in scenarios where the assumption of a standard motion model is violated, the tracking system adapted with the proposed method achieves higher accuracy and robustness in its track estimations.
机译:在许多应用中,跟踪多个对象对于感知当前环境至关重要。当前的大多数多对象跟踪算法都假定对象相对于其他动态对象以及静态环境独立移动。由于在许多交通情况下,物体之间会发生相互作用,此外,由于可驾驶区域的限制,因此无法满足独立物体运动的假设。本文提出了一种方法,该方法适用于多目标跟踪系统以对车辆之间的交互以及当前道路的几何形状进行建模。因此,扩展了标记多伯努利滤波器的预测步骤,以利于使用智能驱动程序模型对对象之间的交互进行建模。此外,为了考虑路线图信息,使用了高精度路线图的近似值。结果表明,在违反标准运动模型假设的情况下,采用该方法的跟踪系统在跟踪估计方面具有更高的准确性和鲁棒性。

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