Autonomous Vehicle Sensor Batching for Latency Optimization
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Solution Overview
Problem
Autonomous vehicles face inefficiencies in processing sensor system outputs due to asynchronous image generation and idle periods for object classifier systems, leading to delayed reaction times to unexpected events on the road.
Innovation Solution
Implementing a batch generator system that batches sensor system outputs to minimize exit delays and optimize computing resource utilization by determining the optimal batch size and timing based on processing times and arrival times of sensor outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If sensor system outputs are processed individually as they arrive, then real-time processing is achieved, but computing resources are underutilized due to frequent idle periods
Solution Approach 1:
The system performs preliminary actions by pre-processing sensor outputs (such as generating feature representations or embeddings) before batching them for classification. This allows the batch processing system to efficiently handle multiple outputs simultaneously while maintaining real-time performance, as the preprocessing steps can be executed independently for each sensor output as it arrives.
Solution Approach 2:
The patent introduces an intermediary batching mechanism that sits between sensor output generation and object classification. This batching layer aggregates multiple sensor outputs into batches, optimizes batch sizes based on system capacity and latency requirements, and manages the flow to the classifier system, thereby balancing real-time processing with efficient resource utilization.
2Speed
If multiple object classifier systems are assigned to different sensor systems, then real-time processing is enabled, but computing resources are wasted due to idle periods
Solution Approach 1:
The patent merges multiple sensor outputs into unified batches that are processed by a shared object classification system. Instead of having separate classifier systems for each sensor, the system combines outputs from multiple sensors (cameras, lidar, radar) into batches, allowing a single classifier system to serve multiple sensors efficiently, thereby eliminating idle periods and reducing overall computing resource requirements.
Solution Approach 2:
The object classification system is designed to be universal and multi-functional, capable of processing batches containing sensor outputs from different sensor types and different sensors. This universal batch processing approach allows one classifier system to handle all sensor inputs, maximizing its utilization and eliminating the need for dedicated classifier systems for each sensor.
3Productivity
If batch size is increased to improve classifier efficiency, then computing resource utilization improves, but processing delay increases
Solution Approach 1:
The system dynamically adjusts batch sizes based on real-time conditions such as sensor output arrival rates, classifier processing capacity, and latency requirements. Rather than using fixed batch sizes, the batching mechanism adapts its parameters to optimize the balance between classifier efficiency and processing delay, ensuring high resource utilization without excessive waiting times.
Solution Approach 2:
The patent employs parameter changes by adjusting batch size, batch timeout, and aggregation thresholds based on system load and performance metrics. These parameter adjustments allow the system to optimize classifier efficiency under different operating conditions while maintaining acceptable processing delays, effectively resolving the contradiction between batch efficiency and real-time performance.
Data Source
AI summary
An autonomous vehicle is described herein. The autonomous vehicle includes several sensor systems that asynchronously generate sensor system outputs. A batch generator system, executed by a processor, identifies batches of sensor system outputs to provide to an object classifier system, wherein the batches of sensor system outputs are identified based upon timing estimates, wherein the timing estimates include first timing estimates and second timing estimates. The first timing estimates include estimates of amounts of time needed by the object classifier system to complete processing of batches of different sizes. The second timing estimates includes estimates of when sensor system outputs are expected to be received from the several sensor systems.