Adaptive Streaming Platform for Autonomous Vehicle Data Prioritization
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Solution Overview
Problem
Conventional HTTP real-time streaming technologies are inadequate for autonomous vehicles due to limited computing resources, storage constraints, and the need for prioritization of data types, making them unsuitable for efficiently streaming diverse and critical vehicle data in varying contexts.
Innovation Solution
An adaptive real-time streaming platform that automates data streaming based on contextual conditions, allowing autonomous vehicles to dynamically adjust the type and format of data transmitted without human intervention, prioritizing essential data and adapting to changes in network conditions and resource availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional HTTP real-time streaming technologies are used for autonomous vehicles, then data can be transmitted over the network, but the system cannot efficiently handle limited computing resources and storage constraints
Solution Approach 1:
The patent segments the data streaming system into multiple components: local processing unit in the vehicle, edge computing nodes, and cloud data centers. This segmentation allows distributed processing that reduces the burden on any single device while maintaining overall streaming efficiency. Critical data is processed locally, less critical data is handled at edge nodes, and historical data is archived in the cloud.
Solution Approach 2:
The patent introduces a hierarchical dimension to the streaming architecture, creating multiple levels of data processing and storage (vehicle-level, fleet-level, cloud-level). This dimensional change allows the system to handle resource constraints by distributing computational and storage loads across different hierarchical levels rather than concentrating them in a single dimension.
2Reliability
If all types of autonomous vehicle data are streamed continuously, then complete data availability is achieved, but network bandwidth and resource usage are excessively consumed
Solution Approach 1:
The patent applies local quality by differentiating data priority levels and applying different streaming strategies to different data types. Critical safety-related data (sensor inputs, control signals) is streamed with high priority and minimal compression, while non-critical data (telemetry, diagnostic information) is streamed with lower priority and higher compression. This allows complete data availability for critical functions while reducing overall resource consumption.
Solution Approach 2:
The patent dynamically changes streaming parameters such as data sampling rate, compression level, and transmission frequency based on vehicle operating conditions, network availability, and data criticality. This allows the system to maintain data availability when needed while reducing resource usage during normal operations or when network resources are constrained.
3Productivity
If data streaming is adapted to varying network conditions, then resource efficiency is improved, but system complexity increases
Solution Approach 1:
The patent implements self-service through automated streaming management that requires no human intervention. The system automatically monitors network conditions, vehicle state, and data priorities, then dynamically adjusts streaming parameters accordingly. This self-service capability improves resource efficiency while keeping the control system relatively simple by using rule-based automation rather than complex manual control interfaces.
4Reliability
If critical data is prioritized in streaming, then operational safety is improved, but non-critical data transmission is reduced
Solution Approach 1:
The patent applies preliminary action by pre-classifying data into priority levels and pre-configuring streaming strategies for each category. Critical safety data is identified and marked in advance with high-priority streaming parameters, while non-critical data is categorized with lower priorities. This preliminary classification ensures that when network resources are available, both critical and non-critical data can be transmitted appropriately without compromising safety.
Data Source
AI summary
Systems and methods provide for adaptive real-time streaming of Autonomous Vehicle (AV) data. In some embodiments, the AV can receive a request from the remote computing system for real-time streaming of a first type of AV data and adaptively streaming a second type of AV data when one or more streaming conditions are satisfied. The first type of AV data and the second type of AV data can be captured as raw data by sensors, actuators, transducers, and other components of the AV. The AV can stream the first type of AV data to the remote computing system in real-time for a first time period. When the AV determines the streaming conditions are satisfied, the AV can automatically determine the second type of AV data to stream to the remote computing system in real-time for a second time period.