Gm Cruise Holdings LLC

Marc Unzueta Canals

Michael Meyer

Georg Kuschk

Sven Möller

Technologies for detecting, identifying, and tracking objects in a scene based upon images generated by a camera are described. A computing system obtains a perspective image from the camera and generates a feature image based upon the perspective image. The feature image has X×Y pixels, and each pixel in the X×Y pixels has a feature vector that includes values assigned thereto. The feature image is provided as input to a machine learning model, where the machine learning model generates a tensor based upon the feature image, where the tensor includes several bins that include values that are representative of likelihoods that an object exists in the scene at distances from the camera that correspond to the bins. The machine learning model is trained using a Gumbel-softmax algorithm. The computing system generates the image in the overhead view based upon the tensor output by the machine learning model.

Detecting, identifying, and tracking objects based upon a tensor with sparsely computed non-zero values

A method and apparatus for implementing a method are disclosed. The method includes providing point cloud data to a machine learning algorithm, the point cloud data detected in the vicinity of an autonomous vehicle. The method further includes differentiating, via the machine learning algorithm, in the point cloud, data directly representing a location of a first object and data indirectly representing a location of a second object. The method includes transforming the data indirectly representing the location of the second object into data directly representing the location of the second object and generating corrected point cloud data based on the data directly representing the location of the first object and the data directly representing the location of the second object. The method includes outputting the corrected point cloud data to the autonomous vehicle.

Radar point cloud multipath reflection compensation

Technologies described herein relate to learning parameter values of a preprocessing algorithm that is executed in a radar system. The preprocessing algorithm is configured to receive raw radar data as input and is further configured to generate three-dimensional point clouds as output, where the preprocessing algorithm generates the three-dimensional point clouds based upon the raw radar data and the parameter values that are assigned to the preprocessing algorithm. To learn the parameter values, the preprocessing algorithm is converted to auto-differentiated form and is added as an input network layer to a deep neural network (DNN) that is configured to identify objects represented in three-dimensional point clouds. The parameter values are learned jointly with weight matrices of the DNN.

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Munich, Germany

Marc Unzueta Canals