Perception Confidence Heuristics for Autonomous Vehicle Data Annotation
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Solution Overview
Problem
Existing methods for annotating sensor data for autonomous vehicle perception algorithms are time-consuming and costly, and conventional approaches to selecting data for annotation do not lead to improved algorithm performance.
Innovation Solution
The system automatically detects sensor data for annotation using heuristics that compare labels and confidence scores from different perception algorithms, allowing only data likely to improve algorithm performance to be annotated and stored or transmitted.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sensor data is randomly sampled for annotation, then annotation coverage is achieved, but annotation efficiency and algorithm improvement are insufficient
Solution Approach 1:
The perception algorithm automatically identifies its own weaknesses by detecting low confidence scores and ambiguous cases, then selectively requests human annotation only for those specific cases. This self-service mechanism eliminates the need for random sampling while ensuring annotation resources are allocated to cases that will most improve algorithm performance.
Solution Approach 2:
The system implements a feedback loop where the perception algorithm's confidence scores and prediction results are continuously evaluated, and this feedback drives the selection of data for human annotation. Cases with low confidence or high ambiguity trigger annotation requests, creating a closed-loop system that progressively improves algorithm performance based on actual performance metrics.
2Reliability
If all sensor data is annotated by human labelers, then complete training data is obtained, but time and cost increase significantly
Solution Approach 1:
Instead of annotating all sensor data (excessive action), the system annotates only the partial subset of data that is most valuable for algorithm improvement - specifically cases with low confidence scores and high ambiguity. This partial action approach maintains sufficient training data quality while dramatically reducing annotation time and cost.
Solution Approach 2:
The system changes the parameter of data selection from random sampling to confidence-score-based filtering. By using confidence scores as the selection parameter, the system identifies and annotates only those specific data points that will provide the most training value, optimizing the balance between training data completeness and annotation resource consumption.
3Adaptability or versatility
If sensor data is selected based on random requirements, then data diversity is achieved, but data quality for algorithm improvement is insufficient
Solution Approach 1:
The system changes the selection parameter from random diversity requirements to confidence score and ambiguity metrics. This parameter change ensures that data quality for algorithm improvement is prioritized, as only cases that the algorithm finds difficult or uncertain are selected for annotation, regardless of whether they meet pre-specified diversity requirements.
Solution Approach 2:
The system replaces manual specification of diversity requirements with an automated mechanism that uses confidence scores and ambiguity detection to identify valuable training cases. This substitution of the selection mechanism ensures that data quality for algorithm improvement is systematically optimized rather than relying on pre-defined diversity categories.
Data Source
AI summary
Various technologies described herein pertain to detecting sensor data to be annotated for autonomous vehicle perception algorithm training. A label identifying a type of an object is assigned to an object at a particular location in an environment based on sensor data generated by a sensor system of an autonomous vehicle for a given time. The label is assigned based on a confidence score assigned to the type of the object by a computer-implemented perception algorithm. The computer-implemented perception algorithm assigns the confidence score to the type of the object based on the sensor data corresponding to the particular location in the environment for the given time. An output of a heuristic is generated based on the label and/or the confidence score, and the output of the heuristic is used to control whether to cause the sensor data for the given time to be annotated.