Vehicle Connectivity Engine for Predictive Workload Assignment
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Solution Overview
Problem
Modern vehicles face challenges in executing workloads due to varying connectivity requirements and changing signal conditions, leading to application failures and performance degradation.
Innovation Solution
An intelligent connectivity engine that monitors and predicts the state of multiple connectivity options, assigning workloads for current or future execution based on availability and future predictions, ensuring optimal use of antennas and wired interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple connectivity options (4G, 5G, WiFi) are provided in the vehicle, then connectivity versatility is improved, but system complexity increases and workload management difficulty worsens
Solution Approach 1:
An intelligent connectivity engine is introduced as an intermediary component that manages multiple connectivity options (4G, 5G, WiFi antennas) and workloads. The engine monitors connectivity states, predicts future states, and makes intelligent decisions about workload assignment, thereby reducing the complexity burden on the overall system while maintaining versatility.
Solution Approach 2:
The connectivity engine performs self-monitoring of connectivity states and self-prediction of future states using trained models. It autonomously manages workload assignment without requiring external intervention, enabling the system to adapt to changing connectivity conditions automatically.
2Speed
If workloads are assigned based on current connectivity state only, then response time is improved, but reliability deteriorates due to dead zones and signal loss
Solution Approach 1:
The system performs preliminary actions by training machine learning models on historical connectivity data to predict future connectivity states. These predictions are made in advance before the vehicle actually enters or leaves coverage zones, enabling proactive workload assignment decisions that maintain reliability while responding to current conditions.
Solution Approach 2:
The connectivity engine continuously monitors actual connectivity states and compares them with predicted states. This feedback loop allows the system to refine its predictions and adjust workload assignments dynamically, improving both reliability and response time by learning from past performance.
3Reliability
If connectivity monitoring is performed continuously, then reliability is improved, but energy consumption increases
Solution Approach 1:
Instead of continuous monitoring, the system employs periodic monitoring at strategically determined intervals. The connectivity engine monitors connectivity states at key decision points and uses predictive models to maintain awareness between monitoring intervals, thereby reducing energy consumption while preserving sufficient reliability for workload management.
Data Source
AI summary
A connectivity monitor of a vehicle determines current and/or future states of antennas. A workload monitor of the vehicle receives execution criteria for different workloads to be executed. An intelligent connectivity engine at the vehicle receives the current and/or future states of the antennas and the execution criteria for the respective workloads. Based on the current and/or future states of the antennas and the execution criteria for the respective workloads, the intelligent connectivity engine assigns at least one of the respective workloads for current execution and at least another of the respective workloads for future execution. A client may use an intelligent connectivity service to configure various aspects of the vehicle connectivity. For example, the client can provide workload recommendation code for the intelligent connectivity engine to assign workloads for current or future execution.