Traffic Control Identification via Machine Learning
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Solution Overview
Problem
Current navigation systems face inefficiencies and safety issues due to limited information in base map data regarding traffic control mechanisms, leading to suboptimal path recommendations and inaccurate ETA predictions.
Innovation Solution
A location management platform utilizes machine learning to generate and identify traffic control mechanisms at junctions by analyzing base map and observation data, training a data model to classify traffic controls, and integrating this information into navigation decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If base map data is used for navigation pathfinding, then navigation functionality is provided, but the path recommendations are suboptimal due to limited traffic control information
Solution Approach 1:
The patent introduces an intermediary data model that mediates between the limited base map data and the navigation pathfinding algorithm. This data model enriches the base map information by inferring traffic control mechanisms (traffic lights, stop signs, yield signs) from vehicle observation data, thereby improving navigation accuracy without requiring direct access to complete traffic control databases.
Solution Approach 2:
The patent replaces the traditional mechanical approach of manually surveying and documenting traffic control mechanisms with an automated information system. Machine learning models process vehicle sensor data (GPS, speed, acceleration) to automatically infer and update traffic control information, substituting manual data collection with automated data-driven inference.
2Measurement precision
If traditional pathfinding algorithms are used with limited base map data, then computation is faster, but ETA predictions are inaccurate
Solution Approach 1:
The patent performs preliminary action by pre-processing vehicle observation data to create the enriched data model before pathfinding execution. Traffic control mechanisms are inferred and stored in advance, so that during actual navigation, the pathfinding algorithm can utilize this pre-computed information without real-time processing delays, improving ETA accuracy while managing computational complexity.
Solution Approach 2:
The system performs self-service by using its own collected vehicle observation data to automatically enrich its base map information. The navigation system processes its own operational data (vehicle positions, speeds, stop patterns) to infer traffic control mechanisms, eliminating the need for external data sources and reducing overall system complexity.
3Reliability
If more traffic control information is collected and processed, then navigation accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The patent applies partial action by selectively inferring only the most critical traffic control mechanisms that have the greatest impact on pathfinding and ETA accuracy. Rather than processing all possible traffic data, the system focuses on key elements (traffic lights, stop signs at major intersections) that provide sufficient navigation reliability while minimizing computational energy consumption.
Data Source
AI summary
A device can receive a data model that has been trained on base map data and summary statistics data associated with a first geographic region. The device can obtain additional base map data associated with a second geographic region and additional summary statistics data for a set of junctions within the second geographic region. The device can determine traffic control mechanisms associated with the set of junctions by providing the additional base map data and the additional summary statistics data as input for the data model. The device can generate, using output of the data model, a base map that includes information indicating whether the set of junctions include traffic control mechanisms. The device can, after generating the base map, perform one or more actions associated with improving vehicle navigation or traffic management.