Building a Racing AI from Scratch

A while ago I received an email from my university inviting us to join the ibm global ai racing competition. Now that I'm finished with my exams I am going to give it a try.

The testing ground for this project will be TORCS (The Open Racing Car Simulator).

The Goal

The primary objective is to build an autonomous agent capable of completing a clean lap around the Corkscrew track without crashing, and eventually, optimizing it for competitive lap times.

The plan is to evolve the agent through a structured pipeline:

1. Rule-Based Control (PID): Establishing a solid baseline using Proportional-Integral-Derivative controllers for steering and braking.
2. Machine Learning: Upgrading the agent to learn from its environment using frameworks like PyTorch to replace hardcoded heuristics.
3. Optimization: Fine-tuning the parameters and pushing the physics engine to the limit.

The Tech Stack

This project combines classic simulator architecture with modern local AI tools:

• Simulator: TORCS (running a local server).
• Language: Python (interfacing via the snakeoil3 library to parse sensor data and output telemetry).
• Local AI Assistant: ibm-granite-3.2-8b-instruct. I will be using this local LLM (hosted via LM Studio and integrated into VS Code with Continue.dev) to help architect the math, tune the control logic, and create/debug the Python code.

What to Expect from this Series

I will be documenting the entire process in this series.

I will share the exact prompts used with the local AI, the generated code, the mathematical reasoning behind the control systems (such as why a naive PD controller causes zig-zag oscillation and how to fix it with damping), and the iterative debugging process.

If you are interested in robotics, control theory, Python, or machine learning applications in simulation environments, follow along.

The first technical log will be published shortly, detailing the implementation of baseline steering and look-ahead braking logic.