Traffic Light State Classification Using Configuration-Specific AI
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately identifying and interpreting the state of traffic lights due to variations in configuration, orientation, and bulb patterns, which affects their ability to make reliable control decisions.
Innovation Solution
The method involves capturing an image of the environment, cropping it to focus on a traffic light region, and using a machine learning classifier tailored to the specific configuration of the traffic light, such as orientation and bulb pattern, to generate accurate predictions of its state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single machine learning classifier is used for all traffic lights, then the system complexity is low, but the measurement precision and reliability of traffic light state identification deteriorate due to variations in configuration, orientation, and bulb patterns
Solution Approach 1:
The patent segments the machine learning classifier system into multiple specialized classifiers, each trained to handle specific traffic light configurations, orientations, and bulb patterns. This segmentation allows each classifier to specialize in particular conditions, thereby improving measurement precision for each specific case while managing overall system complexity through modular organization.
Solution Approach 2:
The patent applies local quality by assigning different machine learning classifiers to different local conditions (specific configurations, orientations, and bulb patterns). Each classifier is optimized for its specific local condition, ensuring high accuracy for that particular scenario. The system selects the appropriate classifier based on the detected traffic light characteristics, thereby achieving high measurement precision across diverse conditions without requiring a single overly complex universal classifier.
2Reliability
If multiple specialized machine learning classifiers are used for different traffic light configurations, then the measurement precision and reliability improve, but the device complexity and computational overhead increase
Solution Approach 1:
The patent implements preliminary action by pre-training multiple machine learning classifiers on specific traffic light configurations, orientations, and bulb patterns during the system setup phase. This preliminary preparation allows the system to quickly select and apply the appropriate pre-trained classifier during operation, improving reliability without adding significant computational overhead during real-time processing. The classifiers are ready in advance for their specific conditions.
Solution Approach 2:
The patent uses copying by creating multiple instances of machine learning classifiers, each copied and trained for a specific traffic light configuration. Instead of developing one complex universal classifier, the system creates copies of the classifier architecture, each specialized for particular conditions. This approach improves reliability through specialization while managing complexity through replication of a proven base architecture.
3Measurement precision
If image cropping is performed to focus on the traffic light region, then the measurement precision improves by reducing background interference, but the processing time increases due to additional image processing steps
Solution Approach 1:
The patent extracts the traffic light region from the full image through cropping, isolating the area of interest and removing distracting background elements. This extraction improves measurement precision by focusing the machine learning classifier's attention solely on the traffic light, eliminating false positives and improving detection accuracy. The time penalty is offset by the subsequent efficiency gains from using specialized classifiers on smaller, pre-processed images.
Data Source
AI summary
Systems and methods for determining a current state, of at least one traffic light, for use in controlling an autonomous vehicle are described herein. Implementations determine, based on a pose instance of the autonomous vehicle and a stored mapping of an environment of the autonomous vehicle, a region of the environment including the traffic light and a configuration that is assigned to the traffic light. Further, those implementations process an image capturing the region, using a machine learning classifier, to generate predicted output associated with multiple candidate states of the traffic light, and determine a current state of the traffic light based on the predicted output. Processing the image using the machine learning classifier can be based on the configuration of the traffic light.