Smart City Traffic: The Federated Vision Grid

Preface: Urban traffic is a non-linear, chaotic system. A fender-bender on 5th Avenue ripples out to cause gridlock on 12th Street. Static timing plans (fixing lights to 60s green) fail to adapt. This guide builds a Federated Vision Grid, where intersections talk to each other to optimize flow dynamically.

Table of Contents

1. The Vision Node (Object Detection)
2. The Mesh Grid (Intersection-to-Intersection)
3. V2X Integration (Talking to Cars)
4. Privacy & Anonymization
5. SUMO Simulation & Training

1. The Vision Node (Object Detection)

We mount 4 cameras per intersection. We create a "Bird's Eye View" (BEV) transformation to map pixels to GPS coordinates.

Key Metrics tracked:

Queue Length: Meters of backup per lane.
Flow Rate: Vehicles per minute.
Classification: Bus, Truck, Car, Bike, Pedestrian.

2. The Mesh Grid (Intersection-to-Intersection)

Nodes are not islands. Node A needs to know that Node B is full so it doesn't send more cars that way ("Don't Block the Box").

We use Reinforcement Learning (RL). The "Agent" controls the traffic lights. Its "Reward" is maximizing total throughput across the entire grid, not just its own intersection (Global Reward vs Local Reward).

# RL Reward Function simplified
def calculate_reward(throughput, wait_time, ambulance_delay):
    # Penalize waiting, massively penalize blocking emergency vehicles
    reward = (throughput * 1.0) - (wait_time * 2.0) - (ambulance_delay * 100.0)
    return reward

3. V2X Integration (Talking to Cars)

The future is Vehicle-to-Infrastructure (V2I) communication using C-V2X (Cellular V2X) on the 5.9GHz spectrum.

Use Case: SPAT (Signal Phase and Timing)
The traffic light broadcasts "I will turn RED in 4 seconds". The Audi approaching at 50mph receives this. The car calculates it cannot stop safely, so it warns the intersection. The intersection consumes the "Dilemma Zone" logic and extends the yellow light by 2 seconds to prevent a T-bone crash.

4. Privacy & Anonymization

Smart Cities is Surveillance if done wrong. We implement Edge Anonymization.

Face Blurring: Gaussian blur applied to all faces immediately after frame capture.
License Plate Redaction: Black boxes drawn over plates unless a "BOLO" (Be On Look Out) warrant is active.
Data Minimization: Only sending counts (Integer) to the cloud, never images.

5. SUMO Simulation & Training

You cannot train an RL agent on live traffic (you will cause accidents). We use SUMO (Simulation of Urban MObility).

We build a Digital Twin of the city in SUMO, train the agent for 10 million epochs until it learns to create "Green Waves", and then transfer the weights to the physical controllers.

Conclusion: A smart city isn't just sensors; it's a responsive organism. By connecting vision, logic, and vehicles, we reclaim the streets from gridlock.