Vehicle Route Prediction for Timely Geo-Info Delivery
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Solution Overview
Problem
Existing vehicle navigation systems are limited in providing geographically-relevant information as they rely solely on the vehicle's current position, which may not allow sufficient time for the driver to react to important information, and require the driver to enter destination information, making it tedious and distracting.
Innovation Solution
A system that records vehicle route data and generates route prediction models locally, predicts the vehicle route, transmits coordinate data to a backend server, and receives path-relevant information for automatic presentation to the driver without requiring input, using onboard computer systems and wireless networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system provides geographically-relevant information based only on the vehicle's current position, then the system complexity is low, but the driver does not have sufficient time to react to the information before it is too late
Solution Approach 1:
The system performs preliminary actions by predicting the vehicle's future route and pre-fetching geographically-relevant information for upcoming locations before the vehicle reaches them. This allows the driver to receive advance notice of important information (gas stations, road conditions, speed limits) with sufficient time to react, while the system handles the complexity of route prediction and information retrieval automatically in the background
2Loss of information
If the system requires the vehicle operator to enter destination information, then the information provided is more accurate, but the operation becomes tedious and distracting
Solution Approach 1:
The system performs self-service by automatically determining the vehicle's route through prediction algorithms that analyze current position, historical data, and navigation patterns. It autonomously fetches and presents relevant geographic information without requiring the driver to input destination details, thereby maintaining information accuracy while eliminating the tedium and distraction of manual input
Solution Approach 2:
The system uses feedback mechanisms by continuously monitoring the vehicle's current position, comparing it with predicted routes, and adjusting information delivery accordingly. It learns from driver behavior patterns and route choices to improve prediction accuracy over time, ensuring relevant information is provided without requiring explicit driver input
Data Source
AI summary
Path-relevant information is provided by a backend server system to a vehicle without an input or request from the vehicle operator. Program applications, including a web browser application, records vehicle route data in a local memory during vehicle travel. Such vehicle route data may include current GPS coordinates, time of day, day of week, etc. The recorded vehicle route data may then be used to locally generate one or more route prediction models. A predicted vehicle route may be generated from the route prediction models. Once a vehicle route has been predicted, coordinate data corresponding to the predicted route may be transmitted to a backend server. Thereafter, path-relevant information, based on the transmitted coordinate data, may then be received from the backend server, and without the vehicle operator having to provide any input or request.