AV Sensor Latency Estimation for Resource Prioritization
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Solution Overview
Problem
Autonomous vehicles face challenges in processing data from environmental sensors and cameras in real-time due to high resource demands, leading to increased latency and potential safety concerns, especially when the workload exceeds hardware capabilities.
Innovation Solution
A method is developed to estimate the tolerable frame processing latency for autonomous vehicles using simulations, allowing for prioritization of hardware resources to ensure safe operation by determining the minimum camera frame processing rate required for safe driving and allocating resources accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hardware resources are improved and neural networks are optimized to process all sensor data with high accuracy and quickly, then processing accuracy and real-time performance are improved, but resource consumption increases substantially
Solution Approach 1:
The patent applies local quality by differentiating processing priorities among different sensors and data streams. Critical safety-related sensors (e.g., LIDAR, cameras for collision detection) receive high-priority processing resources to maintain high accuracy, while non-critical sensors process at lower priorities. This selective allocation ensures processing accuracy for essential functions while reducing overall resource consumption.
Solution Approach 2:
The patent segments the data processing workflow into multiple priority levels and queues. Sensor data is classified into different processing streams based on safety criticality, with separate handling for immediate safety concerns versus general navigation data. This segmentation allows the system to maintain high processing accuracy for safety-critical segments while reducing resource allocation for non-critical segments.
2Reliability
If processing rate is increased to maintain safe operating conditions, then safety is improved, but hardware resource demands increase
Solution Approach 1:
The patent implements dynamic resource allocation that adjusts processing rates based on real-time driving conditions and safety requirements. During normal operation, processing occurs at standard rates. When safety-critical situations are detected (e.g., obstacles, hazardous conditions), the system dynamically increases processing rate for relevant sensors to maintain safety, then returns to normal operation. This dynamic approach ensures safety when needed while avoiding excessive resource consumption during routine operation.
Solution Approach 2:
The patent changes processing parameters such as frame rates, resolution, and processing depth based on safety requirements and environmental conditions. For example, camera frame rates may be reduced during stable driving conditions but increased when obstacles are detected. This parameter adjustment maintains safety standards while reducing overall hardware resource demands during non-critical periods.
3Reliability
If all sensor data is processed at high priority to ensure safety, then safety is maintained, but processing latency increases for non-critical data
Solution Approach 1:
The patent maintains continuous processing of safety-critical data streams at high priority without interruption, ensuring uninterrupted safety monitoring. Simultaneously, non-critical data streams are processed continuously but at lower priorities, allowing them to wait in queues when resources are constrained. This continuous multi-level processing ensures safety functions never stall while preventing excessive latency accumulation in non-critical streams.
Solution Approach 2:
The patent introduces intermediary processing queues and buffer management mechanisms that mediate between high-priority safety data and lower-priority non-critical data. When processing resources are available, non-critical data is processed; when resources are constrained, safety data is processed first. These intermediaries prevent safety data from being blocked by non-critical data, maintaining safety response times while allowing non-critical data to experience controlled latency.
Data Source
AI summary
An estimation model utilizes simulations of an autonomous vehicle and objects detected near the automated vehicle to develop estimates of tolerable frame processing latency to develop real world frame processing latency estimates for similar driving conditions. An estimation model can a minimum tolerable latency for processing the frames of image data of an object detection camera on an autonomous vehicle using the object state data of the objects detected near the autonomous vehicle. An autonomous vehicle system process can determine if the processing latency of a sensor is greater than the modeled tolerable latency for that sensor, then a safety check is failed and an alert is sent. An autonomous vehicle system process can determine if the processing latency of a sensor is greater than the modeled tolerable latency for that sensor, then the hardware resources are prioritized to the processing for that sensor. An autonomous vehicle system process can determine if the processing latency of a sensor is greater than the modeled tolerable latency for that sensor, hardware performance may be increased.