Traffic Light Detection via Spatial Confidence Scores
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Solution Overview
Problem
Current traffic light detection systems fail to accurately determine the state of traffic lights due to limitations in identifying and combining multiple regions of interest, particularly in scenarios like curves or long distances where spatial relationships between candidates are complex.
Innovation Solution
A system and method for traffic light detection that uses multiple regions of interest, determining confidence scores based on spatial relationships and other factors, allowing for the combination of candidates from different regions to accurately determine the state of traffic lights, incorporating a traffic light detection module that receives images and uses previously stored data to identify candidates and calculate confidence scores using spatial relationships, probabilities, and rule sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple regions of interest are used to detect traffic lights, then detection accuracy is improved, but system complexity increases due to handling overlapping regions and spatial relationships
Solution Approach 1:
The system divides the detection task into multiple independent regions of interest, each processed separately to identify candidate traffic lights. This segmentation allows the system to handle complex scenes by breaking them into manageable parts while maintaining overall detection accuracy through subsequent integration of results from multiple regions.
Solution Approach 2:
The system introduces an intermediary confidence score mechanism that mediates between multiple candidate detections from different regions. By calculating spatial relationship factors and confidence scores, the system resolves conflicts between overlapping regions and determines the most likely traffic light state without requiring complex direct comparisons between all candidates.
2Reliability
If confidence scores based on spatial relationships are calculated, then detection reliability is improved, but computational requirements increase
Solution Approach 1:
The system calculates confidence scores and spatial relationship factors selectively for promising candidates rather than exhaustively analyzing all possible combinations. By applying partial action to the most likely candidates first, the system achieves reliable detection results while minimizing unnecessary computational energy expenditure on low-probability cases.
3Adaptability or versatility
If overlapping regions of interest are permitted, then coverage of complex scenarios is improved, but region management complexity increases
Solution Approach 1:
The system implements a universal confidence score calculation mechanism that handles both overlapping and non-overlapping regions through the same process. This multi-functional approach allows the system to adapt to various scenario complexities without requiring separate management strategies, simplifying region management while maintaining versatility.
Data Source
AI summary
Described herein is a device for traffic light detection. The device comprises a memory and a traffic light detection module. The memory may store information, the information comprising first traffic light data of a first traffic light and second traffic light data of a second traffic light. The traffic light detection module may receive an image comprising a first candidate and a second candidate; determine a first region of interest based, at least in part, on the first traffic light data, the first region of interest comprising the first candidate; determine a second region of interest based, at least in part, on the second traffic light data, the second region of interest comprising the second candidate; and determine a confidence score for a first state of the first candidate, the confidence score based, at least in part, on a spatial relationship factor between the first candidate and the second candidate.