Vehicle Sign Recognition Using Optimal Frames and Priority Filtering
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Solution Overview
Problem
Existing traffic sign recognition systems face inefficiencies in computational resource usage and sign interpretation due to the need for extensive perception and natural language processing, especially when vehicles are in motion and signs are partially occluded or distant, leading to suboptimal detection and interpretation.
Innovation Solution
A method and system that optimize sign interpretation by determining an optimal location for sign recognition based on road curvature, vehicle speed, light, weather, and sign size, filtering unnecessary computations, and adjusting vehicle speed or lane changes to ensure timely detection of high-priority signs, using sensor data from cameras and processors to execute optimized sign recognition algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sign recognition is performed continuously at high frequency using all sensor data, then detection accuracy is improved, but computational resource consumption increases excessively
Solution Approach 1:
The system performs sign recognition at a reduced frequency rather than continuously, processing only selected frames based on vehicle speed and distance to the sign. This partial action approach maintains adequate detection accuracy while significantly reducing computational resource consumption by avoiding unnecessary processing of every frame.
Solution Approach 2:
The system dynamically adjusts the sign recognition frequency based on multiple parameters including vehicle speed, distance to the sign, and sign priority level. By changing the processing frequency parameter according to these conditions, the system optimizes the balance between detection accuracy and computational resource usage.
2Use of energy by moving object
If sign recognition is performed at reduced frequency to save computational resources, then energy consumption is reduced, but detection accuracy deteriorates
Solution Approach 1:
The system dynamically adjusts the sign recognition frequency based on real-time conditions such as vehicle speed, distance to the sign, and sign priority. This dynamic approach ensures that processing frequency is optimized for each specific situation, maintaining detection accuracy when needed while reducing computational load during less critical moments.
Solution Approach 2:
The system changes the processing frequency parameter based on multiple input parameters including vehicle speed, distance to sign, and sign class priority. This parameter-based adjustment allows the system to maintain adequate detection accuracy by increasing frequency when conditions demand it while reducing consumption during less critical periods.
3Use of energy by moving object
If the vehicle maintains constant speed, then fuel efficiency is improved, but the ability to optimize sign detection timing is reduced
Solution Approach 1:
The system performs preliminary calculations to predict the optimal moment for sign recognition based on current vehicle speed, distance to the sign, and projected trajectory. By determining the optimal detection timing in advance, the system can maintain constant speed for fuel efficiency while still capturing signs at the ideal moment for recognition.
Solution Approach 2:
The system dynamically determines the optimal detection timing based on real-time vehicle conditions and sign characteristics. This allows the vehicle to maintain constant speed for fuel efficiency while the system adapts the recognition timing to optimize detection, rather than requiring constant speed adjustments.
4Reliability
If all detected signs are processed for recognition, then comprehensive monitoring is improved, but computational overhead increases
Solution Approach 1:
The system applies different processing priorities to different signs based on their class and importance. High-priority signs such as stop signs or warning signs receive full processing attention, while lower-priority signs are processed at reduced frequency or with simplified algorithms. This local quality approach ensures comprehensive monitoring of all signs while optimizing computational resource allocation.
Solution Approach 2:
The system performs full recognition processing only on high-priority signs that require immediate attention, while applying reduced-frequency or simplified processing to lower-priority signs. This partial action approach maintains comprehensive monitoring coverage while significantly reducing overall computational overhead by avoiding excessive processing on all signs equally.
Data Source
AI summary
A method for optimizing sign includes receiving, by a controller of a vehicle, sensor data from a plurality of sensors of the vehicle. The method further includes detecting a sign along a road while the vehicle is in motion using the sensor data and, in response to detecting the sign along the road of the vehicle, determining an optimal location of the vehicle relative to the sign to recognize the sign. Further, the method includes recognizing a content of the sign using an optimal frame captured by the camera at the optimal location and, in response to recognizing the content of the sign, filtering the plurality of frames of the video captured by the camera to minimize a use of a computational resources of the controller.