Traffic Light Alarm Logic for Ambiguous Signal Recognition
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Solution Overview
Problem
Existing vehicle exterior environment recognition systems fail to effectively set traffic lights with different or unknown lighting colors as control input targets, leading to inadequate alarm output during braking operations, especially under conditions like intersection angles, weather, and road environments.
Innovation Solution
A driving assistance device with a recognition unit that processes images from a camera to identify traffic lights and an alarm output unit that controls alarms based on the combination of lighting colors of multiple traffic lights, setting a target traffic light for control even if one light's color is unknown, and outputs an alarm when the target light is red and no braking is occurring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all traffic lights with identical lighting colors are set as control input targets, then the alarm output function operates reliably when traffic light colors are consistent, but the driving assistance function fails when traffic lights have different or unknown lighting colors
Solution Approach 1:
The system performs partial recognition by focusing on the traffic light with the highest recognition probability rather than requiring all traffic lights to be clearly recognized. This allows the alarm function to operate even when some traffic lights have unknown or ambiguous colors, achieving reliable assistance without requiring complete certainty about all signals
Solution Approach 2:
The system changes the parameter from binary recognition (red/not red) to probabilistic recognition (recognition probability threshold). By setting a threshold (e.g., 60% or 70% probability), the system can make decisions based on likelihood rather than absolute certainty, improving adaptability to uncertain conditions while maintaining reliability
2Measurement precision
If the system requires clear identification of traffic light colors to output alarms, then false alarms are reduced, but alarm output performance decreases under challenging conditions like intersection angles, weather, and road environments
Solution Approach 1:
The system prepares for uncertain conditions by incorporating a probability threshold mechanism in advance. This cushioning approach allows the system to tolerate ambiguous recognition scenarios without failing completely, ensuring alarm functionality is maintained even when recognition precision is reduced by environmental factors
Solution Approach 2:
The recognition probability serves as an intermediary between the ambiguous visual input and the binary alarm decision. This intermediate parameter allows the system to handle uncertain traffic light colors by translating probabilistic recognition results into reliable alarm decisions based on the threshold criterion
3Measurement precision
If the system sets a high recognition probability threshold for alarm output, then alarm accuracy is improved, but the number of cases where alarms can be output is reduced
Solution Approach 1:
The system optimizes the recognition probability threshold parameter to achieve the best balance between accuracy and output frequency. By adjusting this parameter (e.g., setting it at 60-70% rather than 100%), the system maintains high alarm accuracy while ensuring alarms are output in sufficient numbers to provide effective driving assistance
Data Source
AI summary
A driving assistance device comprises: a recognition unit configured to recognize a lighting color of a traffic light from an image captured by a camera that images an outside world of a vehicle; and an alarm output unit configured to output an alarm to a driver of the vehicle when the lighting color is red and there is no braking operation of the vehicle. When a plurality of traffic lights are recognized from the image by processing of the recognition unit, the alarm output unit controls the output of the alarm based on a combination of lighting colors of the plurality of traffic lights.