Perspective Vehicle Shadows for Asynchronous Sensor Data Updates
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Solution Overview
Problem
Current remote data storage systems for vehicles, such as cloud-based computing systems, are inflexible and inefficient, particularly in handling varying data update frequencies and providing only full snapshots of vehicle data, which can lead to redundant data transmission and inefficient resource usage.
Innovation Solution
A vehicle shadow service that asynchronously receives and processes disaggregated sensor data from vehicles, allowing for the creation of perspective-based vehicle shadows with customizable configurations, updating based on actual sensor data frequencies and validating data from heterogeneous sources, thereby reducing network overhead and improving data relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full snapshots of vehicle data are transmitted to the remote storage system, then complete vehicle data is available for storage and retrieval, but network bandwidth is wasted due to redundant data transmission and resource usage increases
Solution Approach 1:
The vehicle data is segmented into multiple data streams based on different update frequencies (e.g., high-frequency sensor data vs. low-frequency telemetry data). Each data stream is processed and transmitted independently, allowing the system to send only the necessary portions of data rather than complete snapshots, thereby reducing redundant network transmission while maintaining data completeness.
Solution Approach 2:
The system implements partial updates by transmitting only the changed or relevant portions of vehicle data rather than complete snapshots. The remote storage system receives incremental data updates based on what has actually changed in the vehicle state, reducing network bandwidth consumption while ensuring that complete vehicle data is eventually available for storage and retrieval.
2Measurement precision
If data snapshots are transmitted at high frequency to capture fast-changing vehicle data, then data fidelity is improved, but network overhead and resource usage increase significantly
Solution Approach 1:
Different data streams are assigned different transmission frequencies based on their specific requirements. High-frequency transmission is applied only to fast-changing sensor data that requires high data fidelity, while low-frequency transmission is used for slower-changing vehicle telemetry data. This localized approach to data transmission quality ensures measurement precision for critical data while minimizing network overhead overall.
3Stability of the object's composition
If all vehicle data elements are updated at the same rate based on snapshot frequency, then data consistency is maintained, but efficiency decreases due to unnecessary updates of low-frequency data
Solution Approach 1:
The system dynamically adjusts the update rate for each data element based on its inherent change frequency and importance. Fast-changing sensor data is updated at higher frequencies to maintain consistency, while slower-changing vehicle telemetry data is updated at lower frequencies. This dynamic update strategy maintains data consistency for each data type while significantly improving overall update efficiency by avoiding unnecessary frequent updates of stable data elements.
4Loss of information
If complete vehicle data snapshots are stored remotely, then all vehicle information is accessible for analysis and retrieval, but storage resources are consumed by redundant data
Solution Approach 1:
The system extracts and stores only the essential and non-redundant vehicle data elements in the remote storage system. By processing incoming data streams and identifying unique information that needs to be preserved, the system extracts critical vehicle information for storage while filtering out redundant data. This ensures that all necessary vehicle information remains accessible for analysis and retrieval while optimizing storage capacity utilization.
Data Source
AI summary
Systems and methods are disclosed for implementing perspective-based vehicle shadows. A user of a vehicle shadow service can specify different vehicle shadows for a given vehicle, wherein the different vehicle shadows comprise at least partially different sets of sensor data received from the vehicle and represent the vehicle from different perspectives. For example, a first vehicle shadow may represent the vehicle from the perspective of a first vehicle system, such as the tires, and another vehicle shadow may represent the same vehicle from the perspective of a second vehicle system, such as the engine. Streaming data is provided to the vehicle shadow service and is mapped to the respective perspective-based vehicle shadows, based on user defined configurations for the respective perspective-based vehicle shadows.