Vehicle Perception ROI Control for Low-Latency Scene Detection
Find Innovative SolutionsGenerate Solutions
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 large amounts of data that increase latency and decrease reaction time.
Innovation Solution
A perception system that dynamically adjusts the resolution and region of interest (ROI) based on the vehicle's intent and state, using a perception filter to select and process image data from imaging devices, and allocates compute resources accordingly to prioritize important areas, 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 the volume of information increases causing latency to increase and reaction time to decrease
Solution Approach 1:
The patent divides the environment into multiple regions of interest (ROIs) with different resolution requirements. High-resolution imaging is applied only to critical ROIs where accurate perception is essential, while lower resolution is used for non-critical areas. This segmentation allows the system to maintain perception accuracy for important elements while reducing overall data volume and processing latency.
Solution Approach 2:
The patent implements local quality by assigning different resolution levels to different spatial regions based on their importance to vehicle operation. Critical areas such as pedestrians, vehicles, and obstacles receive high-resolution processing, while background or less critical areas receive lower resolution processing. This approach optimizes the balance between measurement precision and processing speed by concentrating computational resources where they are most needed.
2Reliability
If high-resolution image data is processed to enable precise detection, then reliability is improved, but the volume of information increases causing system latency to increase
Solution Approach 1:
The patent dynamically adjusts the resolution and processing level of different image regions based on real-time vehicle state, intent, and detected objects. As the vehicle operates, the system continuously re-evaluates which regions require high-resolution processing and adapts accordingly. This dynamic adjustment ensures detection reliability is maintained for critical elements while minimizing processing latency by reducing resolution for non-critical regions.
Solution Approach 2:
The patent changes the resolution parameter of image processing based on the importance of different regions. By dynamically modifying this parameter, the system can switch between high-resolution processing for reliable detection of critical objects and lower-resolution processing for non-critical areas, thereby balancing detection reliability with system latency requirements.
3Measurement precision
If compute resources are allocated to process all image data at high resolution, then perception accuracy is maintained, but processing time increases reducing reaction speed
Solution Approach 1:
The patent applies partial action by processing only the necessary portions of image data at high resolution rather than the entire scene. The system identifies critical regions of interest and applies high-resolution processing only to those areas, while using lower resolution for the remainder of the image data. This approach maintains perception accuracy for important elements while significantly reducing overall processing time and improving reaction speed.
Data Source
AI summary
The present disclosure provides a perception system for a vehicle. The perception system includes a perception filter for determining a region of interest (“ROI”) for the vehicle based on an intent of the vehicle and a current state of the vehicle; and a perception module for perceiving an environment of the vehicle based on the ROI; wherein the vehicle is caused to take appropriate action based on the perceived environment, the current state of the vehicle, and the intent of the vehicle.