Parallel Object Detection and Behavior Prediction for Low-Latency Driving
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Solution Overview
Problem
Existing machine learning models for autonomous devices, such as autonomous vehicles, face increased latency when detecting objects and predicting their behaviors due to sequential processing, which can compromise safety and reliability.
Innovation Solution
Implementing a system where machine learning models detect objects and predict behaviors simultaneously, utilizing neural networks that process inputs in parallel, thereby reducing latency and enhancing processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models process object detection and behavior prediction sequentially, then processing accuracy is maintained, but latency increases compromising safety and reliability
Solution Approach 1:
The patent segments the machine learning model into multiple parallel processing streams: one stream for object detection and another for behavior prediction. Each stream processes the same input data independently through separate neural network pathways, allowing simultaneous execution without interfering with each other's accuracy requirements.
Solution Approach 2:
The patent transitions from sequential processing (one-dimensional time progression) to parallel processing by adding a spatial dimension to the computation flow. Multiple processing operations occur simultaneously across different computational pathways, effectively converting time-based sequence into space-based parallelism while maintaining processing integrity.
2Productivity
If machine learning models use sequential processing for object detection and behavior prediction, then processing thoroughness is ensured, but processing speed decreases
Solution Approach 1:
The processing pipeline is segmented into independent parallel streams that can execute simultaneously. The object detection stream and behavior prediction stream are divided into separate computational pathways, each handling its specific task without waiting for the other, thereby doubling the effective processing throughput.
Solution Approach 2:
The patent merges the outputs of multiple parallel processing streams into a unified decision-making framework. After parallel execution of detection and prediction tasks, the results are combined and integrated to form comprehensive output that controls vehicle operations, achieving both speed and thoroughness.
3Adaptability or versatility
If multiple machine learning models are used for object detection and behavior prediction, then comprehensive analysis is achieved, but system complexity increases
Solution Approach 1:
The patent implements a universal processing framework where the same input data serves multiple processing functions simultaneously. The parallel streams share common input interfaces and output integration mechanisms, allowing the system to perform detection, prediction, and analysis using a unified architectural pattern rather than separate specialized systems.
Solution Approach 2:
The patent introduces intermediary components that facilitate communication and data exchange between parallel processing streams. These intermediaries manage the coordination of parallel operations, handle data routing between streams, and integrate outputs without requiring complex direct interactions between all model components, thereby reducing overall system complexity.
Data Source
AI summary
In one embodiment, a method is provided. The method includes receiving, at an input of a first machine learning model, first input data representing an environment. The method also includes determining, by the first machine learning model, a set of objects within the environment based on the first input data. The method further includes determining, by a second machine learning model, a set of behaviors for a second set of objects. An input of the second machine learning model is coupled to a set of intermediate layers of the first machine learning model. Determining the set of objects and determining the set of behaviors for the second set of objects is performed at least partially simultaneously.