Cut-In Detection Using Bounding Boxes for AV Training Data
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Solution Overview
Problem
Existing training data for autonomous vehicles often lacks sufficient representation of challenging driving conditions, particularly cut-ins, making it difficult to effectively train models for handling such scenarios, and manual labeling is costly and inaccurate.
Innovation Solution
Automated detection methods are employed to identify cut-ins within training data by determining bounding boxes and threshold overlaps, enabling the extraction of relevant data for improving vehicle control models, which can be applied in real-time for autonomous or semi-autonomous vehicle operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used to identify cut-ins in training data, then accuracy of cut-in identification can be maintained, but cost and time consumption increase significantly
Solution Approach 1:
The system performs automated cut-in identification using computational algorithms that analyze operational data, sensor data, and map data to detect cut-in events without human intervention. The automated system identifies cut-ins by determining bounding boxes of vehicles, calculating overlap with ego-vehicle lanes, and classifying events based on predefined criteria, thereby eliminating the need for manual labeling while maintaining identification accuracy
Solution Approach 2:
The patent replaces the manual mechanical process of human labeling with an automated computational system. The system uses algorithms to process sensor data, calculate geometric relationships (bounding boxes, overlap areas), and automatically classify cut-in events, substituting human labor with machine-based computational methods that reduce time consumption while preserving accuracy
2Reliability
If comprehensive training data including rare cut-in scenarios is collected, then model performance on challenging conditions improves, but data processing complexity and computational costs increase
Solution Approach 1:
The system extracts only the relevant features and parameters needed for cut-in identification from the comprehensive operational data. By focusing on specific elements such as bounding box coordinates, overlap calculations, and key vehicle parameters, the system reduces processing complexity while maintaining the ability to handle rare and challenging cut-in scenarios effectively
Solution Approach 2:
The patent segments the complex data processing task into distinct modular components: sensor data acquisition, bounding box determination, overlap calculation, cut-in classification, and model training. This segmentation allows each component to be processed independently with optimized algorithms, reducing overall computational complexity while preserving comprehensive analysis of challenging driving conditions
3Productivity
If automated detection methods are used to identify cut-ins, then processing efficiency and productivity improve, but measurement precision may decrease compared to manual labeling
Solution Approach 1:
The system performs preliminary actions by establishing clear geometric criteria and thresholds before automated detection begins. Bounding box parameters, overlap thresholds, and classification rules are predefined based on geometric principles, ensuring that the automated detection process maintains precision while achieving high productivity through consistent, rule-based evaluation of cut-in events
Data Source
AI summary
Example embodiments relate to a method for cut-in identification and classification. An example embodiment includes a obtaining operational data about one or more vehicles; based on the operational data, identifying the presence of one or more cut-ins within the operational data; extracting, from the operational data, cut-in data that depicts one or more of the cut-ins identified within the operational data; and, based on the extracted cut-in data, training a model for controlling an autonomous vehicle. Identifying the presence of a given cut-in includes: determining that at least one vertex of a bounding box surrounding a vehicle was located more than a threshold distance within a lane being navigated by a given vehicle; and determining that the ability of the given vehicle to maintain its course and speed was impeded by the presence of the particular additional vehicle within the lane.