Vehicle Perception ROI Filtering 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 times, as high-resolution imaging devices provide detailed data but increase latency, hindering timely decision-making.
Innovation Solution
A perception system that dynamically adjusts image resolution, region of interest (ROI), and compute resources based on the vehicle's intent and state, using a perception filter to crop and filter image data and allocate resources accordingly, prioritizing critical areas and reducing overall system latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution imaging devices are used to perceive the environment accurately, then measurement precision is improved, but system latency increases and reaction speed deteriorates
Solution Approach 1:
The patent divides the environment into multiple regions of interest (ROIs) with different importance levels. High-resolution imaging resources are segmented and allocated only to critical ROIs such as areas with detected pedestrians, vehicles, or obstacles, while less critical areas receive lower resolution processing. This segmentation allows the system to maintain high measurement precision for important regions while reducing overall computational load and latency.
Solution Approach 2:
The patent implements local quality by applying different resolution levels to different spatial regions. Critical areas requiring high measurement precision receive full high-resolution processing, while non-critical areas use downsampled or lower-resolution data. This approach optimizes the balance between perception accuracy and reaction speed by concentrating computational resources where they are most needed.
2Reliability
If high-resolution image data is processed to improve detection reliability, then reliability is improved, but processing time increases and productivity decreases
Solution Approach 1:
The patent dynamically adjusts the resolution and processing intensity based on real-time driving conditions and detected scene complexity. When high-reliability detection is needed (e.g., pedestrian detection, obstacle identification), the system automatically increases processing resources for those specific regions. During normal cruising with low risk, processing intensity is reduced. This dynamic adaptation maintains high detection reliability when needed while maximizing processing throughput during low-risk periods.
Solution Approach 2:
The patent changes processing parameters such as resolution, frame rate, and computational intensity based on the detected scene context. For example, when a pedestrian is detected, the system increases resolution and processing frequency for that region to ensure high detection reliability. When no critical objects are present, parameters are reduced to maintain high processing throughput. This parameter adaptation resolves the contradiction between reliability and productivity.
Data Source
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.