Autonomous Vehicle Sensor Data Selection for Targeted Annotation
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Solution Overview
Problem
Conventional methods for selecting sensor data for annotation in autonomous vehicles are inefficient, leading to suboptimal training of computer-implemented perception algorithms, as they often rely on random sampling or categorization, which may not prioritize data that improves algorithm performance.
Innovation Solution
Implementing a heuristic system that automatically detects and prioritizes sensor data for annotation based on confidence scores and label assignments from multiple perception algorithms, allowing only data likely to enhance algorithm performance to be annotated and stored or transmitted.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If random sampling or categorization methods are used to select sensor data for annotation, then the annotation process can be simplified and executed, but the training effectiveness of perception algorithms deteriorates
Solution Approach 1:
The system uses the perception algorithm's own confidence scores to automatically identify which sensor data frames need annotation. The algorithm serves itself by evaluating its own performance uncertainty and selecting frames where it is least confident, eliminating the need for external random sampling or categorization methods while ensuring training effectiveness.
Solution Approach 2:
The system changes the selection criterion from random or categorical parameters to the confidence score parameter generated by the perception algorithm. By monitoring confidence scores and selecting frames below a threshold, the system transforms the selection process into a performance-driven automated decision based on algorithm output parameters.
2Reliability
If all sensor data is annotated, then the perception algorithm can be maximally trained, but the time and cost resources are excessively consumed
Solution Approach 1:
The system extracts only the specific subset of sensor data frames that need annotation based on confidence score thresholds. Instead of annotating all frames, it extracts and selects only those frames where the perception algorithm exhibits uncertainty, separating the annotation-worthy data from the rest and significantly reducing annotation time and cost.
Solution Approach 2:
The system applies partial annotation action by annotating only the necessary portion of sensor data rather than all data. By using confidence score thresholds to identify critical frames, it performs exactly enough annotation to improve algorithm performance without excessive annotation of already-well-understood scenes.
3Productivity
If high-confidence sensor data is selected for annotation, then the annotation process is efficient, but the algorithm performance improvement is limited
Solution Approach 1:
The system inverts the conventional approach by not selecting high-confidence data for annotation, but rather selecting low-confidence data. Instead of annotating what the algorithm already understands well, it annotates what the algorithm struggles with, turning the confidence metric upside down to identify training needs.
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.