GREEN LIGHT!

Move carefully...

Red Light, Green Light X

Welcome to the next evolution of the classic children's game. In this AI-powered version, you're not just playing against other players - you're matching wits with our advanced movement prediction system.

Game Rules

Basic Mechanics

🎮

Movement Mechanics

Players can move during green light phase and must freeze during red light.

  • Use WASD or arrow keys for movement
  • Smooth acceleration and deceleration
  • Momentum-based movement system
🤖

AI Detection System

Advanced AI system monitors player movement during red light phase.

  • Real-time movement analysis
  • Pattern recognition for cheating detection
  • Dynamic difficulty adjustment
🎯

Scoring System

Points are awarded based on distance covered and movement precision.

  • Base points for successful completion
  • Speed bonuses for efficient movement
  • Multipliers for consecutive successful rounds

Advanced Strategies

Pro Tips

🔄

Pattern Mixing

Combine different movement patterns to confuse AI prediction system.

  • Alternate between fast and slow movements
  • Use random stop intervals
  • Implement fake-out movements
⚖️

Risk Management

Balance speed and safety to maximize points while avoiding elimination.

  • Calculate risk-reward ratios
  • Optimize stopping positions
  • Time management strategies
👥

Team Tactics

Coordinate with other players to create distractions and opportunities.

  • Synchronized movements
  • Distraction techniques
  • Position trading strategies

Technical Deep Dive

Movement Prediction System

Our AI system uses a neural network to analyze player movement patterns and predict future actions. This allows for dynamic difficulty adjustment and advanced cheating detection.

Movement Prediction Implementation

Neural network-based movement prediction system

Core class setup with neural network initialization

class MovementPredictor {
  private readonly historySize = 100;
  private movementHistory: Movement[] = [];
  private readonly neuralNetwork: NeuralNetwork;

  constructor() {
    this.neuralNetwork = new NeuralNetwork({
      inputNodes: 6,  // x, y, velocity, direction, time, pattern
      hiddenNodes: 12,
      outputNodes: 2  // predicted x, y
    });
  }
}

Movement recording and prediction methods

// Record player movement for pattern analysis
recordMovement(movement: Movement): void {
  this.movementHistory.push(movement);
  if (this.movementHistory.length > this.historySize) {
    this.movementHistory.shift();
  }
  this.updatePredictionModel();
}

// Predict next movement based on historical patterns
predictNextMovement(): PredictedMovement {
  const recentPatterns = this.extractPatterns();
  const features = this.preprocessFeatures(recentPatterns);
  return this.neuralNetwork.predict(features);
}

Pattern extraction and acceleration calculation

// Extract movement patterns for analysis
private extractPatterns(): Pattern[] {
  return this.movementHistory.reduce((patterns, movement, index) => {
    if (index < 2) return patterns;
    
    const pattern = {
      previousMovement: this.movementHistory[index - 1],
      currentMovement: movement,
      timeGap: movement.timestamp - this.movementHistory[index - 1].timestamp,
      acceleration: this.calculateAcceleration(index)
    };
    
    patterns.push(pattern);
    return patterns;
  }, [] as Pattern[]);
}

// Calculate movement acceleration
private calculateAcceleration(index: number): Vector2D {
  const current = this.movementHistory[index];
  const previous = this.movementHistory[index - 1];
  const timeDelta = current.timestamp - previous.timestamp;
  
  return {
    x: (current.velocity.x - previous.velocity.x) / timeDelta,
    y: (current.velocity.y - previous.velocity.y) / timeDelta
  };
}
typescript

Ready to Test Your Skills?

Join the elite players who have mastered the art of movement prediction. Can you outsmart our AI?