Map-Sensor Fusion for Fast Traffic Sign Classification
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Solution Overview
Problem
Current traffic sign recognition systems face challenges in accurately detecting and classifying traffic signs, especially in diverse environments with occlusions and varying lighting conditions, leading to potential errors that can impact autonomous vehicle navigation and driver safety.
Innovation Solution
The proposed method involves advanced data fusion using pre-calculated mapping variables from digital map data and real-time sensor data, processed by a system comprising a transceiver, memory, and processor to provide efficient and accurate traffic sign classifications, particularly in complex scenarios like ramps and intersections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is combined with digital map data to increase traffic sign recognition accuracy, then recognition accuracy is improved, but computational cost and processing time increase
Solution Approach 1:
The patent pre-calculates and stores mapping variables (such as road geometry, intersection locations, ramp configurations) in digital map data before runtime. When a traffic sign is detected by sensors, the system only needs to retrieve and compare these pre-computed variables with sensor data, rather than performing complex computations in real-time. This preliminary preparation significantly reduces the computational burden during actual traffic sign recognition while maintaining high accuracy through the integration of both map and sensor data.
2Productivity
If traditional sensor-only methods are used for traffic sign detection, then computational load is reduced, but detection accuracy deteriorates in diverse environments
Solution Approach 1:
The patent merges digital map data containing pre-calculated mapping variables with real-time sensor data to create a hybrid recognition system. The map data provides contextual information about road geometry, intersections, and ramps, while sensor data provides actual visual observations. By combining these two data sources and comparing their respective variables, the system achieves high detection accuracy in diverse environments while maintaining computational efficiency through the use of pre-computed map features.
Data Source
AI summary
System and methods for separating the computation of map feature derivation from the traditional computing pipeline. One or more mapping variables for a sensitive location on a roadway are calculated ahead of time using mapping data derived from previously identified map feature data. When a real time request for processing near the sensitive location is received, the mapping system calculates one or more classification variables for the sensitive location using sensor data included in the request and the previously calculated mapping variables. The classification variables are input into a model configured to output a classification for one or more location features at the sensitive location. The classification is then provided in response to the request.