Vehicle Perception ROI Scaling for Low-Latency Autonomous Sensing
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Solution Overview
Problem
Autonomous vehicles face a trade-off between accurate perception of their environment and quick reaction time due to the high latency caused by large amounts of image data from high-resolution sensors like cameras and LIDAR, which hinders their ability to dynamically adjust resource allocation and region of interest (ROI) effectively.
Innovation Solution
A perception system that dynamically scales and crops image data based on the vehicle's intent and state, allocating compute resources accordingly to prioritize the region of interest, thereby reducing overall system latency while maintaining high operational quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution imagers are used to perceive the environment accurately, then measurement precision is improved, but processing time increases causing latency
Solution Approach 1:
The patent divides the sensor data processing into segments by identifying and prioritizing regions of interest (ROI) within the full sensor field of view. Only the ROI portions are processed at high resolution, while other areas are processed at lower resolution or skipped entirely, thereby reducing overall processing time while maintaining perception accuracy for critical areas.
Solution Approach 2:
The patent applies different processing qualities to different spatial regions of the sensor data. High-resolution processing is applied locally to identified ROI areas where accurate perception is critical, while lower-resolution processing is applied to non-critical regions, optimizing the balance between accuracy and processing speed.
2Area of stationary object
If the entire sensor field of view is processed, then coverage area is improved, but processing time increases
Solution Approach 1:
The patent extracts and isolates the region of interest (ROI) from the complete sensor field of view. By separating the ROI from the rest of the scene, the system processes only the extracted ROI at high priority, maintaining comprehensive coverage awareness while significantly reducing processing time for critical perception tasks.
Solution Approach 2:
The patent dynamically adjusts the processed field of view based on the autonomous vehicle's current state and intent. The ROI is dynamically determined and adjusted in real-time, allowing the system to expand or contract the processed area according to operational needs, thereby optimizing processing time while maintaining necessary coverage.
3Ease of operation
If compute resources are allocated uniformly across all sensors, then resource distribution is improved, but system responsiveness decreases
Solution Approach 1:
The patent changes the resource allocation parameter from uniform distribution to dynamic prioritization based on vehicle state and intent. Compute resources are reallocated with varying weights to different sensors and processing tasks, with higher resources directed to sensors and ROIs most relevant to current vehicle operations, thereby improving reaction time while maintaining operational simplicity through automated prioritization.
Data Source
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AI summary
The present disclosure provides perception system for a vehicle that includes a plurality of imaging devices for producing images of an environment of the vehicle; a perception filter for receiving the images produced by the imaging devices, wherein the perception filter crops and filters the received images based on an intent of the vehicle and a current state of the vehicle; and a perception module for receiving at least one of the cropped and filtered images from the perception filter and perceiving the environment of the vehicle based on the received at least one of the cropped and filtered images.