Vehicle Shadow Updates for Asynchronous State Sync
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Solution Overview
Problem
Existing 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 use of system and network resources.
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 rates 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 processing, but network bandwidth and system resources are wasted due to redundant data transmission
Solution Approach 1:
The system extracts and transmits only the specific subset of vehicle data that is relevant to user interests and application requirements, rather than transmitting complete vehicle snapshots. This extraction approach eliminates redundant data transmission while ensuring that all necessary data for specific purposes is available at the remote storage system.
2Stability of the object's composition
If vehicle data is updated using snapshot frequency, then all vehicle data elements are synchronized at uniform intervals, but data update efficiency decreases because different vehicle elements have varying inherent update frequencies
Solution Approach 1:
The system implements dynamic data update mechanisms where different vehicle data elements are updated at their own optimal frequencies based on their inherent change rates and importance. Critical safety-related data can be updated more frequently, while less critical data updates at lower frequencies, allowing the system to adapt to varying data characteristics rather than enforcing uniform snapshot intervals.
3Device complexity
If the system processes all vehicle data elements at the same rate, then processing logic is simplified, but processing efficiency decreases due to inability to prioritize critical data
Solution Approach 1:
The system applies different processing priorities and logic to different vehicle data elements based on their criticality and importance. Critical data such as safety-related information receives higher processing priority and more rigorous validation, while non-critical data undergoes lighter processing. This localized quality approach optimizes processing efficiency without significantly increasing overall system complexity.
Data Source
AI summary
Systems and methods are disclosed for implementing a vehicle shadow service that includes a vehicle and/or vehicle shadow update mechanism. A vehicle shadow management sub-system of the vehicle shadow service includes or interfaces with an interface configured to receive vehicle state change requests. Received requests are stored in an update queue for a vehicle shadow and a validation processor determines whether the received state change requests are valid before sending a given state change request to a vehicle corresponding to the vehicle shadow. In some embodiments, state change requests may be invalidated due to being rendered moot by subsequently received requests, changes in state of the vehicle, expiration, etc. The corresponding vehicle shadow is then updated when a data stream from the vehicle indicates that the requested state change has in fact been implemented at the vehicle.