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, systems and methods are disclosed relating to real-time multiview map generation using neural networks. A system can receive sensors images of an environment, such as images from one or more camera, RADAR, LIDAR, and/or ultrasound sensors. The system can process the sensor images using one or more neural networks, such as neural networks implementing attention structures, to detect features in the environment such as lane lines, lane dividers, wait lines, or boundaries. The system can represent the features in various views, including top-down/bird's eye view representations. The system can provide the representations for operations including map generation, map updating, perception, and object detection.

Abstract: Systems and methods are disclosed that relate to object detection and to generating detected object representations. Sensor data corresponding to a scene may be obtained that may represent one or more objects. A tensor may be generated based at least on the sensor data, where the tensor may represent the one or more objects and may include respective predicted 3D characteristics of the one or more objects. The tensor may be represented in 2D space and may be decoded to generate 3D representations of objects using, for example, one or more curve fitting algorithms.

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: Techniques for detecting motion of a vehicle are disclosed. Optical flow techniques are applied to the entirety of the received images from an optical sensor mounted to a vehicle. Motion detection techniques are then imposed on the optical flow output to remove image portions that correspond to objects moving independent from the vehicle and determine the extent, if any, of movement by the vehicle from the remaining image portions. Motion detection can be performed via a machine learning classifier. In some aspects, motion can be detected by extracting the depth of received images in addition to optical flow. In additional or alternative aspects, the optical flow and/or motion detection techniques can be implemented by at least one artificial neural network.

Abstract: Techniques for detecting motion of a vehicle are disclosed. Optical flow techniques are applied to the entirety of the received images from an optical sensor mounted to a vehicle. Motion detection techniques are then imposed on the optical flow output to remove image portions that correspond to objects moving independent from the vehicle and determine the extent, if any, of movement by the vehicle from the remaining image portions. Motion detection can be performed via a machine learning classifier. In some aspects, motion can be detected by extracting the depth of received images in addition to optical flow. In additional or alternative aspects, the optical flow and/or motion detection techniques can be implemented by at least one artificial neural network.