基于dempster-shafer理论的高阶传感器数据融合体系结构中目标存在概率融合

M. Aeberhard, S. Paul, N. Kaempchen, T. Bertram
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引用次数: 45

摘要

未来的驾驶辅助系统需要更加强大和可靠,因为这些系统将对日益复杂的情况做出反应。这需要提高环境感知传感器和算法的性能,以检测其他相关的交通参与者和障碍物。物体的存在概率已被证明是确定物体质量的一个有用的度量。提出了一种基于Dempster-Shafer证据理论的高阶传感器数据融合的存在概率融合方法。该方法能够在融合过程中考虑传感器的可靠性。针对冗余和部分重叠的传感器配置,评估了存在概率融合算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Object existence probability fusion using dempster-shafer theory in a high-level sensor data fusion architecture
Future driver assistance systems need to be more robust and reliable because these systems will react to increasingly complex situations. This requires increased performance in environment perception sensors and algorithms for detecting other relevant traffic participants and obstacles. An object's existence probability has proven to be a useful measure for determining the quality of an object. This paper presents a novel method for the fusion of the existence probability based on Dempster-Shafer evidence theory in the framework of a highlevel sensor data fusion architecture. The proposed method is able to take into consideration sensor reliability in the fusion process. The existence probability fusion algorithm is evaluated for redundant and partially overlapping sensor configurations.
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