Traffic Control Feature Detection Using Telemetry CNNs
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Solution Overview
Problem
Conventional on-demand transportation systems face inaccuracies, inefficiencies, and inflexibilities due to reliance on satellite imagery for digital map features, which fails to account for street-level elements like stop signs and traffic signals, leading to outdated information and inability to adapt to changing traffic patterns.
Innovation Solution
The use of a convolutional neural network (CNN) to classify digital image representations of vehicle telemetry data, allowing for accurate, efficient, and flexible determination of traffic control features such as stop signs and lights, by analyzing patterns in real-time vehicle data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If satellite imagery is used to identify map features, then coverage area is large, but measurement precision of traffic control elements deteriorates
Solution Approach 1:
The system segments the map feature identification task by using satellite imagery for general geographic coverage while employing separate street-level image analysis specifically for traffic control elements. This division allows each method to operate in its optimal domain, maintaining both large coverage area and high precision for critical features.
Solution Approach 2:
The system introduces an intermediary layer of street-level imagery captured by mapping vehicles as a mediator between satellite imagery and final map features. This intermediary provides the detailed street-level visibility needed to accurately identify traffic control elements that satellite imagery cannot detect.
2Loss of information
If satellite imagery is used for map features, then update frequency is low, but loss of time for obtaining outdated information worsens
Solution Approach 1:
The system performs preliminary actions by having mapping vehicles capture street-level imagery and identify traffic control features in advance. This allows the system to maintain an updated database of traffic control elements that can be quickly referenced without waiting for frequent satellite passes.
Solution Approach 2:
The system implements continuous collection of street-level imagery through mapping vehicles operating in the field, ensuring that traffic control feature data is continuously updated rather than relying on periodic satellite imagery updates. This continuous action maintains current information without time lags.
3Measurement precision
If mapping vehicles are deployed to collect street-level imagery, then measurement precision of traffic control features improves, but productivity deteriorates due to computational expense
Solution Approach 1:
The system extracts only the essential traffic control feature information from street-level imagery using automated image analysis algorithms. By taking out only the critical data elements (traffic signs, signals, etc.) rather than processing entire images manually, the system achieves high precision while reducing computational overhead.
Solution Approach 2:
The mapping vehicles and image analysis system operate autonomously to identify and catalog traffic control features. The automated processing eliminates the need for manual analysis, allowing the system to service itself in terms of data collection and processing, thereby improving productivity while maintaining precision.
4Device complexity
If conventional systems rely on static imagery, then device complexity is reduced, but adaptability to changing traffic patterns deteriorates
Solution Approach 1:
The system transitions from static satellite imagery to dynamic street-level image capture by mapping vehicles that continuously collect and update traffic control feature data. This dynamic approach allows the system to adapt to changes in traffic patterns, new signage, and temporary controls while maintaining manageable complexity through automated processing.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media for identifying traffic control features based on telemetry patterns within digital image representations of vehicle telemetry information. The disclosed systems can generate a digital image representation based on collected telemetry information to represent the frequency of different speed-location combinations for transportation vehicles passing through a traffic area. The disclosed systems can also apply a convolutional neural network to analyze the digital image representation and generate a predicted classification of a type of traffic control feature that corresponds to the digital image representation of vehicle telemetry information. The disclosed systems further train the convolutional neural network to determine traffic control features based on training data.