Hierarchical Light Indicator Classifier for Autonomous Vehicles
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately detecting and interpreting light indicators, such as traffic lights, in real-time images, which can lead to uncertain vehicle control and safety issues due to incomplete or inaccurate information about the state of these indicators.
Innovation Solution
An image processing system integrated with autonomous vehicles that captures images, determines the color and shape of light indicators with associated confidence levels, and provides instructions to control the vehicle based on these determinations, using machine-learning training data to classify and interpret the indicators even with partial information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system requires high confidence levels for both color and shape determination before controlling the vehicle, then the accuracy of light indicator interpretation is improved, but the system may fail to respond in time when only partial information is available
Solution Approach 1:
The system applies partial action by allowing vehicle control decisions to be made based on incomplete information. When confidence in color determination exceeds a threshold, the system can control the vehicle using color information alone, even if shape confidence is below the threshold. This enables timely responses while maintaining acceptable accuracy by accepting partial information when sufficient for safe operation.
Solution Approach 2:
The system segments the confidence evaluation into separate assessments for color and shape. Instead of requiring both to meet thresholds simultaneously, the system evaluates each attribute independently and allows control decisions based on whichever attribute (color or shape) meets its confidence threshold, enabling flexible response based on available information quality.
2Reliability
If the system uses a hierarchical classifier to determine light indicator state from multiple attributes, then the reliability of detection is improved, but the device complexity increases
Solution Approach 1:
The hierarchical classifier segments the detection task into multiple independent attribute assessments (color, shape, state) rather than attempting to determine the complete light indicator state in a single complex classification step. Each attribute is evaluated separately with its own confidence level, and results are combined hierarchically to form the final determination, improving reliability through modular assessment.
Solution Approach 2:
The system performs partial classification by determining individual attributes (color, shape) separately rather than requiring complete classification of all attributes simultaneously. The hierarchical structure allows the system to make progress with partial information, determining what can be confidently identified and proceeding with control decisions based on available confident determinations.
3Loss of information
If the system determines both color and shape with confidence levels, then the completeness of information about the light indicator is improved, but the processing time and computational resources increase
Solution Approach 1:
The system applies partial action by determining only those attributes necessary for safe vehicle control. Instead of always completing both color and shape determination with full confidence assessment, the system can proceed with control decisions based on confidently determined attributes alone, reducing processing time while maintaining sufficient information completeness for safe operation.
Solution Approach 2:
The system performs preliminary determination of attributes in a hierarchical order, starting with color determination and then shape determination. This preliminary action approach allows the system to establish basic light indicator state information quickly and make control decisions based on available information, with the option to refine determinations if time and resources permit.
Data Source
AI summary
The present disclosure is directed to an autonomous vehicle having a vehicle control system. The vehicle control system includes an image processing system. The image processing system receives an image that includes a light indicator. The light indicator includes an illuminated component. The image processing system determines a color of the illuminated component of the light indicator and an associated confidence level of the determination of the color of the illuminated component. The image processing system also determines a shape of the illuminated component of the light indicator and an associated confidence level of the determination of the shape of the illuminated component. The determined confidence levels represent an estimated accuracy of the determinations of the shape and color. Additionally, the image processing system provides instructions executable by a computing device to control the autonomous vehicle based on at least one of the determined confidence levels exceeding a threshold value.