Nvidia Corporation

Tommi Koivisto

Pekka Janis

David Nister

Minwoo Park

Sangmin Oh

Zhaoting Ye

Yilin Yang

Daniel Herrera Castro

Bala Siva Sashank Jujjavarapu

Timo Roman

Tero Kuosmanen

Nizar Assaf

Kari Hamalainen

Sriya Sarathy

William Zhang

Colin Tracey

Esa Tapani Tiirola

Timo Erkki Lunttila

Kari Juhani Hooli

Mikko J Rinne

Jari Olavi Lindholm

Tuomas Tapio Hakuli

Bala Siva Jujjavarapu

Pekka Jänis

Henri Koskinen

In various examples, a deep neural network (DNN) is trained to accurately predict, in deployment, distances to objects and obstacles using image data alone. The DNN may be trained with ground truth data that is generated and encoded using sensor data from any number of depth predicting sensors, such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. Camera adaptation algorithms may be used in various embodiments to adapt the DNN for use with image data generated by cameras with varying parameters—such as varying fields of view. In some examples, a post-processing safety bounds operation may be executed on the predictions of the DNN to ensure that the predictions fall within a safety-permissible range.

Distance to obstacle detection in autonomous machine applications

In various examples, detected object data representative of locations of detected objects in a field of view may be determined. One or more clusters of the detected objects may be generated based at least in part on the locations and features of the cluster may be determined for use as inputs to a machine learning model(s). A confidence score, computed by the machine learning model(s) based at least in part on the inputs, may be received, where the confidence score may be representative of a probability that the cluster corresponds to an object depicted at least partially in the field of view. Further examples provide approaches for determining ground truth data for training object detectors, such as for determining coverage values for ground truth objects using associated shapes, and for determining soft coverage values for ground truth objects.

Object detection and detection confidence suitable for autonomous driving

A neural network includes at least a first network layer that includes a first set of filters and a second network layer that includes a second set of filters. Notably, a filter was removed from the first network layer. A bias associated with a different filter included in the second set of filters compensates for a different bias associated with the filter that was removed from the first network layer.

Pruning convolutional neural networks

In various examples, a deep neural network (DNN) is trained to accurately predict, in deployment, distances to objects and obstacles using image data alone. The DNN may be trained with ground truth data that is generated and encoded using sensor data from any number of depth predicting sensors, such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. Camera adaptation algorithms may be used in various embodiments to adapt the DNN for use with image data generated by cameras with varying parameters&#x2014;such as varying fields of view. In some examples, a post-processing safety bounds operation may be executed on the predictions of the DNN to ensure that the predictions fall within a safety-permissible range.

In various examples, a deep neural network (DNN) is trained&#x2014;using image data alone&#x2014;to accurately predict distances to objects, obstacles, and/or a detected free-space boundary. The DNN may be trained with ground truth data that is generated using sensor data representative of motion of an ego-vehicle and/or sensor data from any number of depth predicting sensors&#x2014;such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. The DNN may be trained using two or more loss functions each corresponding to a particular portion of the environment that depth is predicted for, such that&#x2014;in deployment&#x2014;more accurate depth estimates for objects, obstacles, and/or the detected free-space boundary are computed by the DNN. In some embodiments, a sampling algorithm may be used to sample depth values corresponding to an input resolution of the DNN from a predicted depth map of the DNN at an output resolution of the DNN.

A communications system has a cellular structure including a base station that is located within a cell of the cellular structure and provides an elevation beamforming transmission based on a set of elevation precoding matrix indicator offsets in an elevation codebook. The communications system also includes user equipment that is located within the cell and coupled to the base station to receive the set of elevation precoding matrix indicator offsets and a set of reference signals to provide channel quality and inter-cell interference measurements, wherein a selected channel quality indicator is based on an increase in channel quality with respect to inter-cell interference at the user equipment and corresponds to one of the set of elevation precoding matrix indicator offsets. A method of operating a communications system having a cellular structure is also provided.

Soft codebook subset restriction for elevation beamforming

A communications system has a cellular structure and the communications system includes a base station that is located within a cell of the cellular structure and employs a Kronecker product of azimuth and elevation precoding vectors for beamforming. Additionally, the communications system includes user equipment that is located within the cell and coupled to the base station to receive a reference channel state information process employing a reference precoding vector for use in a non-reference channel state information process to derive a compensated channel quality indication. A method of operating a communications system is also included.

Channel quality indication compensation for kronecker precoding

Methods for operating a small cell in a discontinued reception (DRX) mode include maintaining the small cell in a discontinuous transmission (DTX) mode during a first time period having a plurality of first time slots. The methods include transmitting common reference signals in a predetermined number of second time slots prior to the first time slots and in a predetermined number of third time slots following commencement of the first time slots. The methods include discontinuing transmission of the common reference signals and common channel signals if mobile devices are in a discontinuous reception mode during the first time period. The methods include discontinuing transmission of the common reference signals during a predetermined number of fourth time slots following commencement of the first time period if there is no dedicated common transmission to the mobile devices.

Enhanced discontinued transmission and reception methods

Disclosed is a method of providing channel state information for a desired downlink channel of a wireless communication system. In a configuration phase, the method comprises receiving on a signaling channel configuration information comprising an identifier of an interference source and an association which associates the identifier with at least one resource element not used for transmission on the desired downlink channel. In an estimation phase, the method comprises estimating channel state information for an expected transmission on the desired downlink channel accounting for an incoming interference transmission from the identified interference source as observed from the at least one resource element. In a reporting phase, the method comprises reporting the channel state information.

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Helsinki, Finland

Tommi Koivisto