Merging SPaT and MAP Data for Vehicle Traffic Signal Prediction
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Solution Overview
Problem
Vehicle-to-infrastructure (V2I) communication systems face inefficiencies and inaccuracies due to varying signal quality and optional fields in Signal Phase and Timing (SPaT) messaging, as well as differences in mapping formats used by different content providers, which can lead to incomplete or inaccurate traffic light data management.
Innovation Solution
A method and system for merging SPaT messages by receiving data from both network and direct sources, processing lane and maneuver data to determine matches, and generating a merged traffic light signal group mapping, which includes comparing and selecting data from different sources to create a comprehensive and accurate SPaT message.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If V2I signals are transmitted across different data channels, then data can be delivered to the vehicle from multiple sources, but signal quality varies and data accuracy decreases
Solution Approach 1:
The patent merges MAP data from multiple sources (network source and direct source) into a unified traffic light signal group mapping. The system receives first MAP data from a network source and second MAP data from a direct source, then processes and merges these data streams to create comprehensive lane and maneuver data that leverages the strengths of both sources while compensating for individual weaknesses.
Solution Approach 2:
The system compares lane data and maneuver data from different sources to verify consistency and accuracy. By comparing first lane data with second lane data and first maneuver data with second maneuver data, the system provides feedback validation that ensures data reliability before generating the final traffic light signal group mapping.
2Adaptability or versatility
If optional fields are used in SPaT messaging, then message flexibility increases, but data completeness decreases
Solution Approach 1:
The patent creates a universal merged SPaT message structure that incorporates fields from both network and direct sources. The merged message includes timing position data that estimates traffic signal phase change timing, combining the optional fields from multiple sources into a comprehensive data structure that ensures all necessary information is present regardless of which source provided which fields.
3Adaptability or versatility
If different mapping formats are used by different content providers, then system adaptability increases, but data processing complexity and accuracy decrease
Solution Approach 1:
The patent introduces an intermediary processing layer that standardizes data from different mapping formats. The MAP compare and select circuit receives data in various formats from network and direct sources, processes them through a unified comparison and matching algorithm, and outputs standardized traffic light signal group mappings, thereby mediating between format diversity and processing simplicity.
4Measurement precision
If data from multiple sources is integrated, then data accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary processing of MAP data by pre-determining lane data and maneuver data from both network and direct sources before the actual merging operation. This preliminary organization of data structures and pre-comparison of key fields reduces the computational burden during the final merging and mapping generation, thereby minimizing processing time while maintaining high accuracy.
Data Source
AI summary
Technologies and techniques for merging/resolving MAP data and signal phase and timing (SPaT) messages for traffic light data prediction a vehicle in an intelligent transportation system. SPaT data and/or MAP data may be received in a vehicle from a direct source and a network source, and processed to merge data from the two different platforms. The merging of the data allows a vehicle to generate inferences relating to traffic light data received on direct, low-latency, connections, while taking advantage of the more comprehensive data provided from the network source.