Traffic Light Detection With Hierarchical Lane Association
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Solution Overview
Problem
Existing systems face challenges in accurately detecting and associating traffic lights and lanes in complex environments, leading to potential hazards and inefficiencies in vehicle navigation due to the resource-intensive processing of voluminous mapping and sensor data.
Innovation Solution
A system utilizing two-dimensional sensor viewpoints and machine-learned models to integrate traffic light and lane classifications, enhancing the accuracy of traffic light and lane associations through hierarchical labeling and fusion of sensor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If voluminous mapping and sensor data are processed to detect and associate traffic lights with lanes, then detection accuracy may improve, but resource consumption increases significantly
Solution Approach 1:
The patent extracts only the essential features needed for traffic light and lane association detection from voluminous mapping and sensor data, rather than processing all available data. This selective extraction approach maintains detection accuracy while significantly reducing computational resource consumption and processing time.
Solution Approach 2:
The detection process is segmented into distinct stages: traffic light detection, lane detection, and association determination. Each stage processes only relevant data for its specific task, avoiding redundant computation and reducing overall resource consumption while maintaining accurate associations between traffic lights and lanes.
2Measurement precision
If complex processing of mapping and sensor data is performed to accurately detect traffic lights and lanes, then detection accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary detection and classification of traffic lights and lanes separately before determining their associations. This preliminary action organizes data in advance, allowing the association determination stage to work with pre-processed, structured information rather than raw voluminous data, thereby reducing processing time while maintaining accuracy.
Solution Approach 2:
Essential spatial and contextual features are extracted from mapping and sensor data in advance, creating a streamlined dataset that contains only the information necessary for accurate traffic light-lane association detection. This extraction eliminates redundant data processing steps and reduces overall computation time.
3Reliability
If traditional detection methods are used to identify traffic light and lane associations, then system complexity remains low, but false designations increase leading to navigation hazards
Solution Approach 1:
The patent introduces an intermediary association determination component that bridges traffic light detection and lane detection results. This intermediary analyzes spatial relationships, contextual information, and detection confidence levels to accurately determine associations, reducing false designations while adding only moderate system complexity through a dedicated intermediate processing layer.
Solution Approach 2:
The system incorporates feedback mechanisms where detection results are continuously evaluated and refined. Confidence scores and validation checks provide feedback loops that correct potential false designations, improving reliability through iterative refinement without requiring excessively complex system architecture.
Data Source
AI summary
Techniques for detecting and labeling traffic lights and/or traffic signs and the traffic lanes with which such lights and signs are associated in an environment are disclosed. Images may be evaluated to identify pixels that may be associated with a light, sign, or lane. Associations between lights and/or signs and lanes in the environment may be determined along with probabilities for the individual pixels that the pixels may be associated with one of the light/sign and lane associations. Those pixels having a sufficient probability of be associated with a light/sign and lane association may be assigned a corresponding label. An output image with such labels can be provided for vehicle control and for other operations, such as top-down segmentation and trajectory determination.