Multisensor ROI Selection for Low-Latency Feature Tracking
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Solution Overview
Problem
Autonomous vehicles face challenges in processing and transferring large amounts of sensor data due to limited processing resources and data transfer latency, which can hinder timely detection and analysis of features in the environment.
Innovation Solution
The implementation of ROI-enabled sensors that divide the field of view into regions of interest (ROIs) and coordinate with other sensors to selectively capture and analyze data from specific ROIs, reducing the amount of data generated and processed, and utilizing peer-to-peer networks for sensor parameter sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If full-resolution sensor data is obtained from all sensors, then complete environmental information is captured, but data transmission bandwidth and processing load increase significantly
Solution Approach 1:
The field of view of the second sensor is divided into multiple regions of interest (ROIs), allowing selective data acquisition from only relevant segments rather than processing the entire scene. This segmentation enables the system to capture necessary environmental information while significantly reducing the volume of sensor data that needs to be transmitted and processed.
Solution Approach 2:
The system extracts and prioritizes data from specific ROIs that contain features of interest, rather than transmitting complete full-resolution sensor data from all sensors. By taking out only the essential portions of sensor data that contain relevant environmental information, the system reduces bandwidth requirements while maintaining information completeness for critical features.
2Adaptability or versatility
If data is processed centrally through a control system, then coordination between sensors is achieved, but processing delay increases
Solution Approach 1:
The system merges the coordination functions directly into the sensor nodes themselves, allowing sensors to autonomously determine their ROIs based on environmental features detected by other sensors. This combining of detection and coordination capabilities at the sensor level eliminates the need for centralized processing, thereby reducing communication delays while maintaining sensor coordination.
Solution Approach 2:
Each sensor is equipped with the capability to autonomously determine its own ROI based on feature information from other sensors, without requiring centralized control decisions. This self-service approach allows sensors to independently adapt their data acquisition parameters, reducing dependency on centralized processing and minimizing information sharing delays.
3Device complexity
If multiple sensors operate independently, then system complexity is reduced, but feature detection accuracy decreases
Solution Approach 1:
The system introduces a peer-to-peer communication mechanism as an intermediary between sensors, allowing them to share feature information and coordinate ROI selection without requiring complex centralized control infrastructure. This intermediary communication layer enables sensors to work together cooperatively, improving feature detection accuracy through shared information while maintaining relatively simple sensor node architectures.
Solution Approach 2:
Sensors perform preliminary detection of environmental features and share this information with other sensors before final feature analysis. This preliminary action allows other sensors to pre-determine their ROIs based on detected features, enabling coordinated multi-sensor observation that improves detection accuracy without requiring complex real-time coordination during the actual measurement process.
Data Source
AI summary
A system may include a first sensor of a first type and a second sensor of a second different type and having a detector. A field of view of the second sensor may be formed by a plurality of regions of interest (ROIs) defined by the detector. Control circuitry of the system may be configured to perform operations including obtaining, from the first sensor, first sensor data representing an environment, and determining, based on the first sensor data, information associated with a feature of interest within the environment. The operations may also include determining, based on the information, a particular ROI that corresponds to an expected position of the feature at a later time, obtaining a plurality of ROI sensor data from the particular ROI instead of obtaining full-resolution sensor data, and analyzing the plurality of ROI sensor data to determine one or more attributes of the feature.