Bravo
If ARM was an arm, BRN would be its biceps💪!
Well here's a little somethin' that you can't get at home:
WO2021094065A1 METHOD FOR OPERATING A DISTANCE SENSOR OF A VEHICLE IN WHICH A TRANSMISSION SIGNAL IS ADAPTED IN ACCORDANCE WITH HOW AN OBJECT IS CLASSIFIED, COMPUTING DEVICE, AND SENSOR DEVICE
Valeo patent application from November 2019 using a neural network to classify LiDaR signals - how sweet it is.
View attachment 5148
a method for operating a distance sensor (4) of a vehicle (1), in which method a plurality of successive measurement cycles are carried out in an operating mode, wherein, in each measurement cycle, a transmission signal is transmitted, a reception signal (Rx1 to Rx8) is determined on the basis of the transmission signal reflected in a surrounding region (9) of the vehicle (1), the object (8) is classified, and the transmission signal is selected from a plurality of predefined transmission signals in accordance with how the object (8) is classified, wherein the transmission signal is selected in accordance with an assignment rule determined in a learning mode, said assignment rule describing an assignment of the plurality of predefined transmission signals to classes of objects (8), wherein, in each measurement cycle, the object (8) is classified on the basis of the reception signal (Rx1 to Rx8) and the transmission signal is selected in accordance with how the object (8) is classified for subsequent measurement cycles.
[0014] In one embodiment, a method of machine learning is used to determine the assignment rule on the basis of the respective received signals. In particular, so-called deep learning can be used. Provision can also be made for an artificial neural network and/or a generic algorithm to be used in the learning mode. Because the reference measurements for the individual reference objects are carried out with the respective transmission signals, different information is available to the learning algorithm for each individual reference object, as a result of which redundancy can be increased. Furthermore, different object shapes or classes of objects react differently to the different shapes of the transmission signals. This makes it possible to achieve better results in the training because the variance of the transmission signals takes account of the difference in the objects or object classes to be detected. In this way, more significant input data can be supplied to the deep learning algorithm used in practice.
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