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Road surface condition detection with recursive adaptive learning and validation

机译:递归自适应学习和验证的路面状况检测

摘要

A method of determining a road surface condition for a vehicle driving on a road. Probabilities associated with a plurality of road surface conditions based on an image of a capture scene are determined by a first probability module. Probabilities associated with the plurality of road surface conditions based on vehicle operating data are determined by a second probability module. The probabilities determined by the first and second probability modules are input to a data fusion unit for fusing the probabilities and determining a road surface condition. A refined probability is output from the data fusion unit that is a function of the fused first and second probabilities. The refined probability from the data fusion unit is provided to an adaptive learning unit. The adaptive learning unit generates output commands that refine tunable parameters of at least the first probability and second probability modules for determining the respective probabilities.
机译:一种确定在道路上行驶的车辆的路面状况的方法。由第一概率模块确定与基于捕获场景的图像的多个路面状况相关联的概率。由第二概率模块确定与基于车辆运行数据的多个路面状况相关联的概率。由第一和第二概率模块确定的概率被输入到数据融合单元,用于融合概率并确定路面状况。从数据融合单元输出精确的概率,该概率是融合的第一概率和第二概率的函数。来自数据融合单元的精确概率被提供给自适应学习单元。自适应学习单元生成输出命令,该输出命令至少细化第一概率模块和第二概率模块的可调参数以确定各自的概率。

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