The Road to Autonomous Driving
What if a self-driving car could teach itself to drive, just like evolution shapes life? Instead of training with labeled data, this project evolves driving skills using Genetic Algorithms (GA).
Through natural selection, our cars learn to avoid obstacles, optimize speed, and navigate efficiently—all without explicit programming.
The Evolution of an AI Driver
Traditional reinforcement learning often requires reward functions, but our genetic algorithm-based AI finds optimal driving behaviors without predefined rewards. Instead, we use evolutionary selection, crossover, and mutation to improve driving strategies.
Key Features:
- Genetic Algorithm-based Learning: No explicit rules—only evolution.
- Real-time Driving Simulation: Watch cars learn and improve dynamically.
- Elitism & Crossover Techniques: Ensuring top-performing drivers evolve.
How It Works
The system starts with randomly initialized driving strategies. Over multiple generations, the genetic algorithm selects the best-performing cars, allowing them to pass their "genes" (weights of the neural network) to the next generation.
Step 1: Creating the Population
Each car in the simulation has its own neural network controller, initialized with random weights.
class Car {
constructor() {
this.brain = new NeuralNetwork([5, 8, 4]); // Sensor inputs → Hidden layer → Controls
}
}Step 2: Evolution through Natural Selection
The best drivers are chosen based on distance traveled and collision avoidance.
function selectBestCars(cars) {
return cars.sort((a, b) => b.fitness - a.fitness).slice(0, topPerformers);
}Top drivers are selected, then "breeding" (crossover) occurs.
function crossover(parent1, parent2) {
let child = new Car();
child.brain.weights = mixWeights(parent1.brain.weights, parent2.brain.weights);
return child;
}Step 3: Mutation for Exploration
To avoid premature convergence, we introduce random mutations in the neural network.
function mutate(car) {
for (let i = 0; i < car.brain.weights.length; i++) {
if (Math.random() < mutationRate) {
car.brain.weights[i] += (Math.random() - 0.5) * mutationStrength;
}
}
}