Semi-Autonomous Perception Annotation System for Autonomous Vehicles
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manual annotation of large datasets for machine learning models in autonomous vehicles is time-consuming, expensive, and inefficient, as it requires labeling every object in every frame of video from vehicle sensors.
Innovation Solution
A semi-automated approach using AI to automatically identify and pre-select potentially interesting regions of data (images or point clouds) for annotation, allowing human reviewers to focus on the most challenging cases, thereby reducing the need for manual selection and annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual annotation is used for all sensor data, then annotation completeness is improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent segments the annotation task by dividing sensor data into Regions of Interest (RoIs) that require human annotation and regions that can be automatically processed. The review selector identifies and selects only the most relevant RoIs for manual annotation, while other regions are handled automatically, thus reducing overall annotation time while maintaining completeness for critical areas.
Solution Approach 2:
Instead of annotating all sensor data manually, the system applies partial manual annotation only to the most challenging and interesting cases identified by the review selector. This partial action approach focuses human effort where it is most needed while using automated methods for the remainder, significantly reducing time consumption while preserving annotation quality for critical regions.
2Manufacturing precision
If manual annotation is used for all sensor data, then annotation quality is improved, but cost increases significantly
Solution Approach 1:
The annotation workload is segmented into high-value RoIs requiring human expertise and lower-value regions handled automatically. The review selector identifies RoIs based on criteria such as object difficulty, ambiguity, and importance, ensuring that human annotators focus only on cases where their expertise provides the most value, thereby reducing costs while maintaining quality for critical annotations.
Solution Approach 2:
The system enables self-service annotation for non-critical regions through automated processing, reducing the need for expensive human labor. The review selector and automated annotation pipeline handle routine cases independently, reserving human annotator resources for challenging cases where quality is paramount, thus optimizing the cost-quality balance.
3Productivity
If automated RoI selection is used, then annotation efficiency is improved, but reliability of selection may worsen
Solution Approach 1:
The review selector incorporates feedback mechanisms where human annotators review and correct automated selections. The system learns from annotator feedback to improve its RoI selection criteria over time, refining its understanding of what constitutes a challenging or interesting case. This feedback loop continuously improves selection accuracy while maintaining high efficiency.
Solution Approach 2:
The review selector performs preliminary filtering and selection of RoIs before human annotation, using pre-established criteria and heuristics to identify challenging cases. This preliminary action reduces the burden on human annotators by pre-processing the data and selecting only the most relevant regions, improving efficiency while maintaining reliability through careful criterion design and iterative refinement.
Data Source
AI summary
A method for selecting one or more Regions of Interest (RoIs) for human annotations includes obtaining sensor data generated by one or more sensors of a vehicle; applying at least one class-agnostic heuristic function to the sensor data to determine a presence and an approximate position of one or more objects in an RoI of the sensor data; selecting one or more RoIs having proposed annotations for the one or more objects for refinement by an annotator; and outputting the one or more selected RoIs.