Google has developed an AI algorithm to refine route suggestions on Google Maps, personalizing it based on user data and behavior, allegedly improving the accuracy on an average by 16-24 percent.
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Personalized Route Suggestions through AI
- The AI model comprises 360 million parameters, using real-time data from Maps users to influence factors including travel time, road conditions, tolls, and personal preferences to suggest routes.
- This technology is grounded on "inverse reinforcement learning" (IRL), specifically a new IRL algorithm - "Receding Horizon Inverse Planning (RHIP)".
The Power of RHIP and AI in Maps
- Google and Deepmind jointly worked to develop RHIP, using complex stochastic models in immediate vicinity areas, but switching to simpler deterministic methods for distant areas for power conservation.
- The AI improves route suggestions for both driving and two-wheeled vehicles by learning from Maps users' movements and behaviors over time.
- Google states that this is the largest application of inverse reinforcement learning for route planning to date.
Implementation and User Testing
- Google has applied the algorithm to Maps data globally, but extensive user testing is needed to confirm if the technique consistently produces better routes.
- Previous attempts at using AI systems for route planning on a large scale have often failed due to the complexity of road networks.
(source)
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