Dynamic actuation map using a neural network fed by visual odometry

Abstract

An apparatus includes an interface and a processor. The interface may be configured to receive pixel data corresponding to an area outside of a vehicle and sensor data from said vehicle. The processor may be configured to generate one or more outputs in response to the sensor data based upon dynamic set-points for the vehicle and one or more inferences made by executing a first trained neural network model. The processor may be configured to process the pixel data arranged as video frames. The trained neural network model may have been trained by processing the pixel data arranged as video frames, performing computer vision operations to detect features in the video frames, determining the dynamic set-points for the vehicle by applying visual odometry operations on the features detected in the video frames, and modifying a plurality of weights of an untrained neural network model based on the dynamic set-points, the sensor data, and a training dataset comprising the dynamic set-points and corresponding actuator values that are representative of one or more desired inferences.