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Multisensor data fusion for advanced driver assistance systems - the Active Safety Car project

机译:用于高级驾驶员辅助系统的多传感器数据融合-主动安全车项目

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Driver assistance systems support overstrained and affected drivers and become more and more essential for series-production vehicles. Object detection and segmentation is one of the most challenging research topics in this field. In order to warn the driver or automatically break before a potential collision, objects intersecting the path of the host vehicle have to be detected and classified. Most recently developed approaches are based on two dimensional image processing, sometimes in combination with a tracking algorithm associating detections in consecutive frames to one and the same object. Further robustness is achieved by multisensor data fusion, i.e. information by two or more different sensors (e.g. camera and radar data) are fused in order to get a much more reliable result. Another aspect for safety applications is communication between cars, which provides additional sensor locations and thus also requires data fusion technology. Two different approaches for data fusion are proposed and first results are presented.
机译:驾驶员辅助系统为过度劳累和受影响的驾驶员提供了支持,并且对于批量生产的车辆越来越重要。对象检测和分割是该领域最具挑战性的研究主题之一。为了在潜在碰撞之前警告驾驶员或自动停车,必须检测和分类与本车路径相交的物体。最新开发的方法基于二维图像处理,有时与跟踪算法结合使用,该算法将连续帧中的检测与一个和同一对象相关联。通过多传感器数据融合来实现进一步的鲁棒性,即,将两个或更多不同传感器的信息(例如摄像机和雷达数据)融合在一起,以获得更加可靠的结果。安全应用的另一个方面是汽车之间的通信,这提供了额外的传感器位置,因此也需要数据融合技术。提出了两种不同的数据融合方法,并给出了第一个结果。

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