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Probabilistic modeling of sensor properties in generic fusion systems for modern driver assistance systems

机译:现代驾驶员辅助系统通用融合系统中传感器性能的概率模型

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Modern driver assistance and safety systems need a reliable and precise description of the environment. Fusing the measurement data of two or more sensors can improve the performance of the perception system. A generic fusion system which is independent of the attached sensors could be reused in multiple fusion systems and sensor combinations. This could be very helpful because sensor data fusion is a demanding and complex task. In this contribution, we present the algorithmic basics for a generic fusion system, detailed ways on how to model sensor specific properties and which benefits we can achieve by using these models.
机译:现代驾驶员援助和安全系统需要对环境的可靠和精确描述。融合两个或多个传感器的测量数据可以提高感知系统的性能。独立于所附传感器的通用融合系统可以在多个融合系统和传感器组合中重复使用。这可能是非常有帮助的,因为传感器数据融合是一个苛刻和复杂的任务。在这一贡献中,我们为通用融合系统提供了算法基础知识,详细方法是如何通过使用这些模型来实现传感器特定属性的详细方法,以及我们可以实现的好处。

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