Vehicle Perception ROI Control for Low-Latency Imaging
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Solution Overview
Problem
Autonomous vehicles face a trade-off between accurate perception of their environment and quick reaction time, as high-resolution imaging devices provide extensive data that increases latency and decreases 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, thereby reducing overall system latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution imaging devices are used to provide extensive image data, then measurement precision of the environment is improved, but loss of time increases due to increased data volume and processing latency
Solution Approach 1:
The patent divides the environment into multiple regions of interest (ROIs) with different importance levels. High-resolution imaging is applied only to critical ROIs where precise measurement is essential, while lower resolution is used for less critical areas. This segmentation allows the system to maintain high measurement precision for important regions without processing the entire field of view at maximum resolution, thereby reducing overall data volume and processing latency.
Solution Approach 2:
The patent implements variable resolution imaging where different spatial regions are captured at different resolutions based on their importance to the vehicle's operation. Critical areas such as those containing pedestrians, other vehicles, or road signs receive high-resolution capture, while peripheral or less critical areas receive lower resolution. This local quality approach maintains measurement precision where needed while significantly reducing the total data volume that contributes to system latency.
2Reliability
If high-resolution image data is processed, then reliability of detection is improved, but productivity of the perception system decreases due to increased processing time
Solution Approach 1:
The patent segments the image processing task by identifying and prioritizing specific regions of interest. Detection algorithms are applied at different resolution levels to different segments: high-reliability detection is performed only on critical ROIs using high-resolution data, while less critical areas use lower-resolution data with reduced processing. This segmentation maintains detection reliability for safety-critical elements while improving overall processing speed by reducing the volume of high-resolution data that requires intensive processing.
Solution Approach 2:
The patent applies partial processing to non-critical regions by using lower resolution and simplified detection algorithms for areas that do not require high-confidence detection. Full high-resolution processing with comprehensive detection algorithms is reserved only for critical ROIs. This partial action approach maintains sufficient detection reliability for safety-critical detection while significantly improving productivity by reducing the computational burden on non-critical areas.
3Measurement precision
If the perception system processes all image data at high resolution, then measurement precision is maintained, but use of energy increases due to higher compute resource requirements
Solution Approach 1:
The patent segments the processing workload by resolution level based on spatial importance. High-resolution processing is applied only to segmented critical ROIs where measurement precision is essential for safety, while non-critical areas are processed at lower resolutions. This segmentation dramatically reduces the total number of pixels requiring high-compute processing, thereby reducing energy consumption while maintaining measurement precision for the most important detection targets.
Solution Approach 2:
The patent implements local quality processing where compute resources and processing resolution are dynamically adjusted based on the importance of each spatial region. Critical areas receive high-quality high-resolution processing with full computational resources, while peripheral or less critical areas receive lower-quality lower-resolution processing with reduced computational resources. This local quality approach maintains measurement precision where it matters most while significantly reducing overall energy consumption of the perception system.
Data Source
AI summary
The present disclosure provides a perception system for a vehicle. The perception system includes a number of imaging devices associated with the vehicle and 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. The ROI is used to select images of an environment of the vehicle produced by the imaging devices and the perception filter receives and processes the images produced by the imaging device. The perception system further includes a perception module for receiving the processed images from the perception filter and perceiving the environment of the vehicle based at least in part on the received images.