System for neural architecture search for monocular depth estimation and method of using

An in-vehicle model training method comprising:

• receiving an input image;
• performing object detection, using an encoder, on the received input image to identify at least one object, wherein the encoder includes an in-vehicle neural network (NN) model;
• determining a distance to each of the at least one object;
• generating a first heatmap based on the determined distance to each of the at least one object;
• comparing the first heatmap with a second heatmap generated by a trained neural network (NN);
• updating the in-vehicle NN model based on differences between the first heatmap and the second heatmap;
• determining whether a latency of the encoder satisfies a latency specification;
• and
• outputting the in-vehicle NN model in response to the latency satisfying the latency specification and the difference between the first heatmap and the second heatmap satisfying an accuracy specification.

A non-transitory computer readable medium configures to store instructions thereon that, when executed by a processor, cause the processor to:

• receive an input image;
• perform object detection, using an encoder, on the received input image to identify at least one object, wherein the encoder includes an in-vehicle neural network (NN) model;
• determine a distance to each of the at least one object;
• generate a first heatmap based on the determined distance to each of the at least one object;
• compare the first heatmap with a second heatmap generated by a trained neural network (NN);
• update the in-vehicle NN model based on differences between the first heatmap and the second heatmap;
• determine whether a latency of the encoder satisfies a latency specification;
• output the in-vehicle NN model in response to the latency satisfying the latency specification and the difference between the first heatmap and the second heatmap satisfying an accuracy specification.