Student Team
Pranya Shukla
RoutePilot AI Team · Grade 10 · Kanpur
Amishi Kapoor
RoutePilot AI Team · Grade 8 · Kanpur
Mihika Singh
RoutePilot AI Team · Grade 8 · Kanpur
An AI navigation and geographic intelligence system that uses multi-source map fusion, real-time analytics, and machine learning to provide accurate navigation, optimized routes, corrected map data, and personalized travel assistance.
Outdated or incorrect map information causes delays, wrong routes, and navigation errors for travelers, drivers, and logistics companies.
Travelers and tourists spend significant time researching routes, destinations, and travel plans.
Conflicting data from multiple sources leads to unreliable mapping and location information for businesses, developers, and agencies.
For travelers, drivers, businesses, and developers who face inaccurate mapping data, fragmented geographic information, and inefficient route planning, RoutePilot AI uses multi-source map fusion, real-time analytics, and machine learning to provide accurate navigation and intelligent routing.
Predicts better route options using traffic, road conditions, and location data.
Recommends optimized routes, travel plans, and location options.
Helps users make faster travel and navigation decisions.
Generates corrected map information and personalized itinerary suggestions.
GPS coordinates, traffic information, satellite imagery, map datasets, road status, and user preferences.
Latitude, longitude, traffic level, construction zones, road condition, and current route availability.
Recommended routes, travel plans, corrected map data, and location recommendations.
The best fit because RoutePilot AI analyzes traffic, road conditions, map data, and user preferences to recommend the best route and travel options.
Predicts route efficiency and travel outcomes by combining real-time and historical geographic data.
The project workflow turns scattered location information into reliable, personalized navigation recommendations.
Gather traffic data, GPS locations, road conditions, satellite images, user feedback, and public map datasets.
Combine conflicting map sources into a more unified and trustworthy dataset.
Analyze route conditions, confidence levels, and user priorities.
Return optimized routes, travel plans, and navigation assistance.
Google Maps API and OpenStreetMap can support route data, geographic lookups, and map visualization.
TensorFlow, Python, Google Colab, and Teachable Machine can help prototype classification and recommendation logic.
Faster travel, improved accuracy, better planning, and a more reliable navigation experience.