Texas Instruments Corporation

Ziguo Zhong

Liu Hui-che

Vikram Vijayanbabu Appia

Yingmao Li

Tarek Aziz Lahlou

A method for camera radar fusion includes receiving, by the processor, radar object detection data for an object and modeling, by a processor, a three dimensional (3D) physical space kinematic model, including updating 3D coordinates of the object, to generate updated 3D coordinates of the object, in response to receiving the radar object detection data for the object. The method also includes transforming, by the processor, the updated 3D coordinates of the object to updated two dimensional (2D) coordinates of the object, based on a 2D-3D calibrated mapping table and modeling, by the processor, a two dimensional (2D) image plane kinematic model, while modeling the 3D physical space kinematic model, where modeling the 2D image plane kinematic model includes updating coordinates of the object based on the updated 2D coordinates of the object.

System and method for camera radar fusion

An image classification system includes a convolutional neural network, a confidence predictor, and a fusion classifier. The convolutional neural network is configured to assign a plurality of probability values to each pixel of a first image of a scene and a second image of the scene. Each of the probability values corresponds to a different feature that the convolutional neural network is trained to identify. The confidence predictor is configured to assign a confidence value to each pixel of the first image and to each pixel of the second image. The confidence values correspond to a greatest of the probability values generated by the convolutional neural network for each pixel. The fusion classifier is configured to assign, to each pixel of the first image, a feature that corresponds to a higher of the confidence values assigned to the pixel of the first image and the second image.

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Ziguo Zhong