This application is written in Python and is called sdc-console-python, where sdc stands for Self Driving Car.

It is designed as a console application that allows you to simulate individual modules of the sensor system of a self driving car for different models of vehicle behavior (i.e. when it doesn't move, when it moves at a constant speed, or, for example, when it changes the angle of its movement).

This is done by entering the required commands through the console.

At the moment, several modules have been written:

• Kalman filter and Real-Time Locating System,
• Fuzzy controller,
• A neural network for handwritten digit recognition.

Kalman filter allows you to reduce measurement error and more accurately estimate the true value of the measured parameter based on previously measured values.

In the original version, the Kalman filter generated empirical data for a GPS receiver, processed this data, drew a graph of the dependence of measurements and calculations and output the calculation results to the console.

It should be noted that the Kalman filter was guided by the constant location of the car, and also process variance was a constant parameter.

Later, I divided the tasks that the Kalman filter performed between different modules and made it so that the process variance parameter changes its value with each iteration depending on the calculated aposteri error at the previous iteration ("the larger the error value at the previous iteration, the larger step we have to take in the current iteration to reduce this error").

This allowed the Kalman filter to be used to add models with constant speed and random acceleration, and to add a gyroscope.

One of the ideas on how to teach a car to independently calculate the speed of its movement may be the following algorithm:

1. Set the starting and ending points of the path,
2. Estimate the location of the car at the current time and mark it on the map,
3. Using the map, calculate the distance that the car will drive in a straight line without turning,
4. Since almost any driver tries to slow down when cornering, it makes sense to teach a car the same (i.e. teach a car to think as a human being in this situation). You can copy this thinking model by using a fuzzy controller.
   In a general sense, fuzzy controller allows the car to adjust its speed according to the idea that if the pivot point is far away, then you can drive at a high speed, and if it's close, then the speed should be slowed down.
5. Determine the boundaries of such terms of the linguistic variables distance and speed as "close distance", "far distance", "low speed" and "high speed" in numerical values,
6. Build rulebase in the following way: "if the distance is close then speed should be low" and "if distance is far then speed should be high" and so on,
7. Apply the fuzzy inference algorithm (the most used is the Mamdani algorithm).
   Thus, the task of the fuzzy controller is to take the distance to the next pivot point and return the speed corresponding to that distance.

A huge amount of information required for self driving car control system is visual (for example, road markings, road signs and traffic signals).
Moreover, this kind of information cannot be obtained using the so-called traditional measurement methods using radar, lidar, GPS, gyroscope and accelerometer. A well-established solution for computer vision is currently neural networks.
Now I only added a neural network algorithm for recognizing handwritten digits (MNIST) to this app.

In order to run this app, you need to enter:

cd console 
python console.py

Then you just need to write some commands.

Now I will briefly talk about the commands you can use in this application. Commands are entered as follows:

app module --operation_mode

For example, if you need to simulate the GPS module, provided that the car is moving at a constant speed, then you need to enter sdc gps --v.

If you need to simulate the operation of a gyroscope under the condition that the angle at which the car is moving is unchanged, then you should enter sdc gyro --const, etc.

More information on commands you can be found by typing sdc or sdc -commands.

Service modules are called as follows:

app -service_module

For example, sdc -commands or sdc -help.