Devlog #1 – Building a Self-Driving Car Simulator in Python

Project Overview

Today I started building a self-driving car simulator in Python using Pygame. The goal of this project is to create an autonomous vehicle that can navigate a track using virtual sensors and eventually learn to drive through evolutionary algorithms and neural networks.

This project is being developed in stages, starting with a simple car and gradually adding intelligence.

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What I Built

MVP 1 – Basic Car Movement

The first milestone was creating a car that could:

• Move forward and backward
• Turn left and right
• Rotate smoothly
• Move using basic trigonometry

The car’s movement is calculated using sine and cosine functions based on its current heading angle.

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MVP 2 – Sensor System

After movement was working, I added five distance sensors.

The sensors act like rays extending from the front of the vehicle:

• Left
• Front-left
• Front
• Front-right
• Right

Each sensor checks for collisions with walls and returns the distance to the nearest obstacle.

This allows the car to “see” its surroundings.

Example sensor output:

[150, 120, 85, 140, 150]

The smaller the number, the closer an obstacle is.

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MVP 3 – First Autonomous Driver

Using the sensor data, I implemented a simple rule-based AI.

The logic is straightforward:

• Drive forward continuously
• If an obstacle appears ahead, turn toward the side with more open space
• Avoid walls detected by side sensors

Although the AI is simple, it can already navigate parts of the track without human input.

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Challenges Faced

One of the biggest issues was setting up Pygame.

Initially, installation failed because I was using Python 3.14, which caused compatibility problems with the version of Pygame being installed.

I switched to pygame-ce and successfully got the simulator running.

I also encountered several coding mistakes:

• Misspelled method names
• Incorrect variable references
• Nested functions due to indentation errors
• Collision detection bugs

Debugging these issues helped me better understand Python classes and object-oriented programming.

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What I Learned

Throughout this session I learned:

• Pygame basics
• Real-time game loops
• Raycasting concepts
• Collision detection
• Trigonometry for movement
• Basic autonomous navigation

I also gained experience reading Python error messages and tracking down bugs.

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Current Status

Completed:

✓ Vehicle movement

✓ Sensor system

✓ Wall collision detection

✓ Basic autonomous driving

In Progress:

□ Better collision model

□ Track checkpoints

□ Fitness scoring

□ Multiple vehicles

Planned:

□ Genetic algorithms

□ Neural networks

□ Procedural track generation

□ Training visualization

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Next Steps

The next major milestone is creating multiple AI-controlled cars and implementing a fitness system.

Instead of manually programming driving behavior, future versions will evolve better drivers over time through genetic algorithms.

The long-term vision is to build a miniature autonomous driving research sandbox where hundreds of vehicles learn and improve through simulation.

This is only the beginning, but seeing the first car successfully avoid walls was a satisfying milestone.