Urban Growth Prediction Using LiDAR for a Leading System Integrator in South Korea
AI Powered Traffic Monitoring and Analytics
Client
System integrators Smart city operators
Industry
Smart Infrastructure Urban Planning
Solution
LiDAR data analytics Urban growth prediction
Use Case
Traffic Movement Analysis
Spatial Intelligence
Technology
LiDAR
PointPillars
Goal
Develop a LiDAR based AI solution for a leading system integrator in South Korea to process traffic movement data from critical urban junctions. The project focused on enabling accurate detection, classification, and tracking of pedestrians and vehicles to generate actionable insights for urban planning and infrastructure development.
Problem Statement
The client faced the technical challenge of transforming large volumes of raw LiDAR point cloud data into accurate, real time traffic insights. Processing unstructured three-dimensional data while maintaining detection accuracy and low inference latency required a specialized deep learning pipeline capable of identifying and tracking multiple categories of road users at busy urban junctions.
Solution Highlights
Vedya developed and implemented a customized LiDAR based deep learning pipeline tailored to the client’s traffic monitoring environment.
Real Time LiDAR Data Processing
Implemented a multi process pipeline to process incoming LiDAR point cloud data efficiently and support low latency traffic monitoring
Edge Based Data Collection Infrastructure
Configured the data acquisition environment with LiDAR sensors, edge computing infrastructure, Docker, and secure SSH based access for reliable data collection
Advanced Point Cloud Preprocessing
Established a data preparation and annotation workflow using CVAT to convert raw point cloud recordings into structured datasets for model training and validation
3D Deep Learning Model Optimization
Evaluated multiple 3D detection architectures and selected PointPillars for its balance of detection performance and inference speed, achieving approximately 10 ms inference time
Spatial Movement Analysis
Implemented object detection and tracking for pedestrians, two wheelers, cars, buses, and trucks within defined regions of interest, enabling traffic counting and directional movement analysis
GPU Accelerated Model Training
Trained and optimized the model using GPU enabled infrastructure, tuning key parameters such as point cloud range and voxel size to improve model performance
High Accuracy Model and Solution Evaluation
Evaluated the trained model using 600 annotated test samples and validated traffic counting and directional outputs against manually prepared ground truth data. The solution achieved a mean precision of 0.96, mean recall of 0.94, and mean accuracy of 0.90 for directional traffic results
Edge Inference and Deployment
Packaged the solution for deployment in a Docker environment, enabling the trained model to process LiDAR data and generate traffic insights with low latency on the edge computing infrastructure
Conclusion
Vedya successfully addressed the client’s LiDAR data processing and traffic monitoring challenges by developing a customized AI pipeline for real time object detection, tracking, and traffic analysis. By combining optimized deep learning, GPU accelerated training, and edge-based inference, the project demonstrated Vedya’s ability to take an AI model from development through deployment while delivering accurate and timely traffic insights for urban planning applications.