Hierarchical Object Classification for Autonomous Vehicle Navigation
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Solution Overview
Problem
Conventional autonomous vehicle navigation systems face challenges in accurately differentiating between various types of objects, especially under diverse conditions, which can lead to insufficient collision avoidance due to sudden changes in object speed or trajectory.
Innovation Solution
A computer-implemented pipeline distributes labeling tasks to human operators to assign consistent labels to sensor data, which are then used to train an object classifier system. This system is deployed in autonomous vehicles to control mechanical systems based on the identified object characteristics, utilizing a multi-layered taxonomy to refine labels from general object types to specific ones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If general object detection is used to simplify navigation, then device complexity is reduced, but measurement precision deteriorates leading to insufficient collision avoidance
Solution Approach 1:
The object detection process is segmented into multiple stages: initial general object detection, followed by hierarchical classification into categories (vehicle, pedestrian, animal), and finally sub-category classification (car, truck, SUV for vehicles; walking, running, cycling for pedestrians). This segmentation allows the system to achieve high measurement precision through progressive refinement while managing device complexity through modular processing stages.
2Measurement precision
If object classifier systems are trained with diverse training data to improve accuracy under various conditions, then measurement precision is improved, but loss of time increases due to extensive training requirements
Solution Approach 1:
The system performs preliminary actions by collecting and organizing diverse training data in advance, covering various objects, conditions, and scenarios. The hierarchical classification framework is pre-established with defined taxonomies for different object categories. This preliminary preparation enables the trained model to achieve high measurement precision during deployment without requiring extensive real-time computation or retraining.
3Measurement precision
If hierarchical classification is implemented to improve object differentiation, then measurement precision is improved, but device complexity increases due to multi-layered taxonomy
Solution Approach 1:
The classification system is segmented into hierarchical levels: first-level classification into major categories (vehicle, pedestrian, animal, static object), second-level classification into sub-categories (car, truck, SUV for vehicles; walking, running, cycling for pedestrians), and third-level classification into specific types. Each level processes only relevant data from the previous level, reducing overall system complexity while achieving high measurement precision through progressive differentiation.
Solution Approach 2:
The classification process is made dynamic by adapting the depth and detail of classification based on contextual factors such as object distance, detection confidence, and operational context. For distant or low-confidence detections, the system may perform only first-level classification, while for close, high-confidence detections, full hierarchical classification is applied. This dynamic approach reduces device complexity by avoiding unnecessary classification steps while maintaining measurement precision when needed.
Data Source
AI summary
An autonomous vehicle controlled based upon the output of a trained object classifier is described herein. The object classifier is trained using labeled training data generated by a pipeline configured to assign labels to unlabeled sensor data. The pipeline includes transmitting sensor signal data capturing an object to individual computing devices for indications of an object type, wherein a label is assigned to the object based on the indications and provided to a data store as labeled training data. A learning system receives the labeled training data and generates a trained object classifier (e.g., a neural network) that is deployed in an autonomous vehicle to control operation of a mechanical system based on an output thereof.