AI/ML
Adaptive Traffic Signal Control
Traditional traffic lights operate on fixed timers, which is inefficient. This project uses computer vision to count vehicles and Reinforcement Learning (RL) to decide which lane gets the green light and for how long, adapting to real-time surges in traffic.
Project typeIndependent build
StatusCompleted project
FocusAI/ML
Technology
PythonOpenCVReinforcement LearningTensorFlowSimulation
System approach
- Computer Vision: OpenCV and YOLOv8 for vehicle detection and counting.
- RL Engine: Deep Q-Learning (DQN) agent trained in a simulated environment.
- Simulation: SUMO (Simulation of Urban MObility) for testing and validation.
Challenges
- Training the RL agent to handle edge cases like emergency vehicles.
- Coordinating multiple intersections to prevent bottleneck downstream.
- Processing high-resolution video feeds at low latency.
Solutions
- Integrated a priority-based reward system in the RL algorithm for emergency vehicle detection.
- Implemented a multi-agent RL approach where neighboring signals communicate their flow states.
- Used TensorRT for hardware-accelerated inference on the edge.
Key outcomes
01
Real-time traffic analysis
02
RL-based optimization
03
Reduced wait times