Autonomous Vehicle Object Detection via Dynamic Resource Reallocation
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Solution Overview
Problem
Autonomous vehicles face challenges in quickly and accurately detecting and resolving unknown objects, such as defaced signs or debris, due to limitations in computing resources and dynamic environmental conditions.
Innovation Solution
The solution involves reallocating processing resources among multiple memory devices associated with the autonomous vehicle, prioritizing the use of higher-performance memory devices to efficiently process and classify unknown objects in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computing resources are allocated to process unknown objects in real-time, then object detection accuracy improves, but system response time deteriorates due to resource allocation overhead
Solution Approach 1:
The system performs preliminary classification of detected objects using a first machine learning model before allocating substantial computing resources. This preliminary action filters out common objects that don't require intensive processing, reserving resources only for unknown or suspicious objects that need accurate classification, thus maintaining fast response times while improving detection accuracy for critical cases
Solution Approach 2:
The object detection system is segmented into multiple stages: initial detection, preliminary classification using a first machine learning model, and detailed classification using a second machine learning model. This segmentation allows the system to process most objects quickly through the first stage while allocating intensive resources only to cases requiring the second stage, resolving the contradiction between accuracy and response time
2Measurement precision
If multiple machine learning models are deployed for object classification, then classification accuracy improves, but device complexity increases
Solution Approach 1:
The classification system is divided into two sequential models: a first machine learning model for preliminary classification and a second machine learning model for detailed classification. This segmentation reduces overall system complexity by ensuring the more complex second model is only invoked when necessary, rather than deploying a single complex model for all cases
Solution Approach 2:
The system applies partial classification action using the first machine learning model for all detected objects, and only applies the more intensive second classification action to objects that remain unclassified or are identified as unknown. This partial application of complex processing reduces the overall computational burden and system complexity while maintaining high classification accuracy for critical cases
3Productivity
If processing resources are reallocated dynamically, then productivity improves, but system stability deteriorates due to resource reallocation overhead
Solution Approach 1:
The system implements dynamic resource allocation based on the classification needs of detected objects. Processing resources are allocated adaptively: minimal resources for common objects classified by the first model, and additional resources for unknown objects requiring the second model. This dynamic allocation improves processing efficiency while maintaining system stability through controlled, need-based resource transitions
Data Source
AI summary
Methods, systems, and apparatuses related to autonomous vehicle object detection are described. An autonomous vehicle can capture an image corresponding to an unknown object disposed within a sight line of the autonomous vehicle. Processing resources available to a plurality of memory devices associated with the autonomous vehicle can be reallocated in response to capturing the image and an operation involving the image corresponding to the unknown object to classify the unknown object can be performed using the reallocated processing resources.