Construction Zone Detection With Hierarchical Pixel Labeling
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Solution Overview
Problem
Existing vehicle systems face challenges in accurately detecting and classifying construction zones, leading to potential unsafe or inefficient navigation due to over- or under-application of construction zone labels, which can result in unnecessary vehicle stops or collisions.
Innovation Solution
A vehicle computing system uses two-dimensional sensor data and machine-learned models to integrate construction zone classifications, employing hierarchical labeling techniques to assign accurate labels to image pixels based on confidence factors and risk probabilities, ensuring precise identification of construction zones and other features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If construction zone labels are applied more broadly to ensure safety, then vehicle safety is improved, but false designations increase leading to unnecessary stops or collisions
Solution Approach 1:
The system segments the detection task into multiple hierarchical levels: first identifying construction zone indicators (cones, barrels, workers), then classifying affected road regions, and finally determining drivability. This multi-stage segmentation allows precise localization of construction zones while maintaining comprehensive safety coverage.
Solution Approach 2:
The system applies different detection strategies to different spatial regions: construction zone indicators are detected with high sensitivity across the entire field of view, while drivability assessment is applied locally to specific road segments. This allows the system to be highly sensitive to construction zone presence while maintaining precision in determining which specific areas are affected.
2Device complexity
If traditional sensor systems are used for construction zone detection, then system complexity is reduced, but detection accuracy and classification precision deteriorate
Solution Approach 1:
The system transitions from traditional two-dimensional image analysis to three-dimensional spatial reasoning by detecting construction zone indicators in 3D space and projecting them onto affected road regions. This dimensional transformation enables more accurate classification of construction zones and their impact areas while maintaining compatibility with standard sensor systems.
Solution Approach 2:
The system introduces an intermediate classification layer that bridges raw sensor data and final drivability decisions. Construction zone indicators are first detected, then used to generate affected region masks, which finally inform trajectory planning. This intermediary classification stage significantly improves detection accuracy without requiring complex end-to-end systems.
3Productivity
If construction zones are detected with high precision, then navigation efficiency is improved, but false negatives increase risking unsafe vehicle operation
Solution Approach 1:
The system performs preliminary detection of construction zone indicators (cones, barrels, workers) before final drivability assessment. By identifying these indicators early and generating affected region masks in advance, the system can make faster navigation decisions while maintaining comprehensive safety coverage, thus improving both efficiency and reliability.
Data Source
AI summary
Techniques for detecting and labeling construction zones in an environment are disclosed. Two-dimensional images may be evaluated to identify pixels that may be associated with a construction zone and labeled accordingly. Labels for corresponding pixels in a separate image or non-construction zone labels for the same pixels may be compared to the construction zone pixels and an output image pixel label may be determined based on one or more criteria. An output image can be provided for vehicle control and for other operations, such as top-down segmentation and trajectory determination. Output images and related data may also be used to train a model to perform construction zone detection and labeling.