Abstract: The present disclosure relates to determining a first illumination level corresponding to an area based at least on a first illumination detection obtained using a first illumination detector corresponding to a machine. A second illumination level corresponding to the area may be determined based at least on a second illumination detection obtained using a second illumination detector corresponding to the machine. Based at least on the first illumination level and the second illumination level, a scene illumination state of the area may be determined. Based at least on the scene illumination state, one or more lights of the machine may be controlled.

Abstract: The present disclosure relates to determining a first illumination level corresponding to an area based at least on a first illumination detection obtained using a first illumination detector corresponding to a machine. A second illumination level corresponding to the area may be determined based at least on a second illumination detection obtained using a second illumination detector corresponding to the machine. Based at least on the first illumination level and the second illumination level, a scene illumination state of the area may be determined. Based at least on the scene illumination state, one or more lights of the machine may be controlled.

Abstract: In various examples, methods and systems are provided for determining, using a machine learning model, one or more of the following operational domain conditions related to an autonomous and/or semi-autonomous machine: amount of camera blindness, blindness classification, illumination level, path surface condition, visibility distance, scene type classification, and distance to a scene. Once one or more of these conditions are determined, an operational level of the machine may be determined, and the machine may be controlled according to the operational level.