Autonomous Vehicle Object Detection via Dynamic Memory Reallocation
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately and timely detecting unknown objects due to limitations in computing resources and ever-changing environments, which can lead to unsafe scenarios.
Innovation Solution
The method involves reallocating computing resources between different types of memory devices in an autonomous vehicle when entering a new network coverage zone, allowing for the efficient processing of data related to unknown objects to classify them accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computing resources are allocated to process data from multiple sensor types simultaneously, then object detection accuracy is improved, but processing time increases and may cause delays in critical detection scenarios
Solution Approach 1:
The system dynamically adjusts resource allocation between different sensor data processing tasks based on real-time conditions. When a critical event is detected (such as an unknown object), the system reallocates computing resources away from non-critical tasks like environmental mapping to focus on the urgent detection task, thereby maintaining high accuracy while reducing processing time for critical operations.
Solution Approach 2:
The computing resources are divided into multiple processing pipelines, each dedicated to specific sensor types or detection tasks. This segmentation allows parallel processing of different sensor data streams (LIDAR, camera, radar) simultaneously, improving overall detection accuracy without significantly increasing the processing time for any single task, as each segment operates independently and efficiently.
2Speed
If the autonomous vehicle processes all sensor data locally, then detection speed is improved, but energy consumption increases
Solution Approach 1:
The system performs partial processing of sensor data locally to achieve timely detection, while deferring some less critical processing tasks to cloud-based systems. Critical detection functions are executed locally to maintain speed, while non-critical functions can be processed later with less energy constraint, thus balancing detection speed and energy consumption.
3Measurement precision
If the system maintains a comprehensive database of all possible objects, then detection accuracy is improved, but memory requirements and system complexity increase
Solution Approach 1:
The system pre-processes and filters sensor data to identify only those objects that are relevant to current detection needs, rather than maintaining comprehensive databases of all possible objects. By performing preliminary filtering and classification, the system reduces the complexity of object databases while maintaining detection accuracy for relevant objects.
Data Source
AI summary
Methods, systems, and apparatuses related to autonomous vehicle object detection are described. A method can include receiving, by an autonomous vehicle, an indication that the autonomous vehicle has entered a network coverage zone generated by a base station and performing an operation to reallocate computing resources between a plurality of different types of memory devices associated with the autonomous vehicle in response to receiving the indication. The method can further include capturing, by the autonomous vehicle, data corresponding to an unknown object disposed within a sight line of the autonomous vehicle and performing, using the reallocated computing resources, an operation involving the data corresponding to the unknown object to classify the unknown object.