This project is about an autonomous vehicle, based on a modified toy RC car, that can drive along a “road” without any manual interaction required.
To this end, the car’s remote control is modified so it can be attached to a microcontroller, that receives commands from a Python program running on a laptop. The camera, mounted on the top of the car, streams its view wirelessly to a neural net on the laptop, that decides what steering commands are the most appropriate at every time step/frame.
In this post, I will present how to modify the remote control (soldering and mechanical changes), how to extend the car, and how to stream live video, with low latency, from the Raspberry Pi to a laptop using GStreamer and OpenCV. An upcoming post will show a reliable neural net model for automated steering.
Parsing the structured text files, provided by the Unicode consortium, at each startup is too inefficient, and merely storing the parsed text into a simple integer array wastes too much memory.
A more efficient storage uses a dictionary-like approach, to compress the needed data using a few layers of indirections, while still giving array-like performance with constant (and negligible) overhead.
In the following, I’ll briefly present the solution I found.
Analyzing these heatmaps can point out undesired correlations in the training data, between samples and labels. For example, an image classifiers for train track might rely on objects that are present in each picture (such as , while not being present in pictures of counter examples for horses. This artifact in the collected data set may be subtle, and not noticeable to a human, but would be visible on the heatmap that highlights the critical features in each image that drove the classification.