Abstract: Autonomous vehicles including sensors and electronic control units (ECUs) that communicate via ring networks are disclosed. A system includes an ECU of an autonomous vehicle that includes an ECU network interface. The system includes a plurality of sensors disposed on the autonomous vehicle, and each of the plurality of sensors includes a respective sensor network interface. The system includes a plurality of network links communicatively coupling the plurality of sensors to the ECU network interface of the ECU via the respective sensor network interface in a ring configuration.

Abstract: A method of training a heterogeneous convolutional neural network (HCNN) system includes identifying batch sizes for a first task and a second task, defining images for a first batch, a second batch, and a batch x for the first task, defining images for a first batch, a second batch, and a batch y for the second task, training the HCNN using the first batch for the first task, training the HCNN using the first batch for the second task, training the HCNN using the second batch for the first task, training the HCNN using the second batch for the second task. The sequential training continues for each of the batches and each of the tasks until the end of an epoch. When the epoch is complete, the images for each batch and each task are reshuffled.

Abstract: A heterogeneous convolutional neural network (HCNN) system includes a visual reception system generating an input image. A feature extraction layer (FEL) portion of convolutional neural networks includes multiple convolution, pooling and activation layers stacked together. The FEL includes multiple stacked layers, a first set of layers learning to represent data in a simple form including horizontal and vertical lines and blobs of colors. Following layers capture more complex shapes such as circles, rectangles, and triangles. Subsequent layers pick up complex feature combinations to form a representation including wheels, faces and grids. The FEL portion outputs data to each of: a first sub-network which performs a first task of object detection, classification, and localization for classes of objects in the input image to create a detected object table; and a second sub-network which performs a second task of defining a pixel level segmentation to create a segmentation data set.

Abstract: A method of training a heterogeneous convolutional neural network (HCNN) system includes identifying batch sizes for a first task and a second task, defining images for a first batch, a second batch, and a batch x for the first task, defining images for a first batch, a second batch, and a batch y for the second task, training the HCNN using the first batch for the first task, training the HCNN using the first batch for the second task, training the HCNN using the second batch for the first task, training the HCNN using the second batch for the second task. The sequential training continues for each of the batches and each of the tasks until the end of an epoch. When the epoch is complete, the images for each batch and each task are reshuffled.

Abstract: A heterogeneous convolutional neural network (HCNN) system includes a visual reception system generating an input image. A feature extraction layer (FEL) portion of convolutional neural networks includes multiple convolution, pooling and activation layers stacked together. The FEL includes multiple stacked layers, a first set of layers learning to represent data in a simple form including horizontal and vertical lines and blobs of colors. Following layers capture more complex shapes such as circles, rectangles, and triangles. Subsequent layers pick up complex feature combinations to form a representation including wheels, faces and grids. The FEL portion outputs data to each of: a first sub-network which performs a first task of object detection, classification, and localization for classes of objects in the input image to create a detected object table; and a second sub-network which performs a second task of defining a pixel level segmentation to create a segmentation data set.