Traffic Light Active Learning Using V2I Inconsistency Detection
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Solution Overview
Problem
Collecting and annotating traffic light datasets for machine learning models is expensive and can lead to redundant data, slowing down training and potentially causing overfitting, especially due to varying traffic light configurations and environmental conditions.
Innovation Solution
An active learning pipeline system that utilizes Vehicle-to-Infrastructure (V2I) information as a baseline to validate traffic light detection, identifying inconsistencies for data collection and annotation, focusing on diverse and challenging samples to enhance model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data collection and annotation methods are used for traffic light datasets, then comprehensive coverage of traffic light configurations and environmental conditions can be achieved, but the cost increases and redundant data accumulates
Solution Approach 1:
The system implements feedback by comparing machine learning model predictions with ground truth V2I information to identify inconsistencies. This feedback loop enables selective collection of only those data samples where the model performs poorly, avoiding redundant collection of already-well-perceived traffic light scenarios while maintaining model accuracy.
Solution Approach 2:
The patent changes the approach from collecting all possible traffic light data to selectively collecting data based on model performance parameters. By monitoring prediction consistency with V2I information, the system dynamically identifies which data parameters (specific traffic light configurations and environmental conditions) need supplementation, reducing overall data volume while maintaining comprehensive coverage of challenging cases.
2Reliability
If large volumes of traffic light data are collected for training, then model robustness can be improved, but training time increases and overfitting risk increases
Solution Approach 1:
The system extracts only the essential and most informative data samples for training by filtering out redundant cases. By identifying inconsistencies between model predictions and V2I ground truth, the patent extracts specifically those traffic light scenarios that provide maximum training value, reducing training time while maintaining model robustness through targeted exposure to diverse and challenging cases.
3Adaptability or versatility
If diverse traffic light configurations and environmental conditions are included in the dataset, then model adaptability improves, but data collection complexity and cost increase
Solution Approach 1:
The system performs self-service by automatically identifying which diverse traffic light scenarios need to be added to the training dataset. Through automated comparison of model predictions with V2I information, the patent enables the system to self-determine the specific configurations and environmental conditions that require representation, reducing the complexity of manual data collection planning while ensuring comprehensive adaptability coverage.
Data Source
AI summary
Systems and methods are provided for developing/updating training datasets for traffic light detection/perception models. V2I-based information may indicate a particular traffic light state/state of transition. This information can be compared to a traffic light perception prediction. When the prediction is inconsistent with the V2I-based information, data regarding the condition(s)/traffic light(s)/etc. can be saved and uploaded to a training database to update/refine the training dataset(s) maintained therein. In this way, an existing traffic light perception model can be updated/improved and/or a better traffic light perception model can be developed.