Vehicle Application Latency Evaluation System
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Solution Overview
Problem
Modern vehicles face challenges in executing applications such as lane change, collision avoidance, and platooning due to latency issues in signal propagation and processing, which can lead to outdated data and prevent the use of these applications, especially in dynamic and high-traffic environments.
Innovation Solution
A vehicle application enabling system that includes a memory, initialization module, latency evaluation module, and application enable module, which calculates a weighting factor based on network latency, false positive rate, and deviation to determine a probability threshold for enabling applications, using latency characterization and prediction modules to assess whether current conditions meet the required latency standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If vehicle applications are executed in dynamic and high-traffic environments, then application functionality and productivity are improved, but network latency increases causing outdated data and execution failures
Solution Approach 1:
The system performs preliminary latency evaluation and probability threshold calculation before executing vehicle applications. The latency evaluation module assesses current network conditions and predicts whether latency requirements will be met, enabling the system to prepare and enable applications in advance when conditions are favorable, thus preventing execution failures due to latency.
Solution Approach 2:
The system dynamically adjusts application execution decisions based on real-time latency assessments. The probability threshold is continuously updated based on current network conditions, allowing the system to adapt its application execution strategy to changing environmental conditions, enabling applications when latency is acceptable and disabling them when latency exceeds thresholds.
2Reliability
If latency evaluation with probability thresholds is implemented, then application execution reliability is improved, but system complexity increases due to multiple modules and calculations
Solution Approach 1:
The latency evaluation module serves multiple functions: it assesses current network latency, compares it against requirements, calculates probability thresholds, and provides decisions for application enablement. This multi-functional approach consolidates what could be separate complex modules into a single unified component, reducing overall system complexity while maintaining reliability.
Solution Approach 2:
The system uses historical latency data and confusion matrix statistics that are automatically generated and updated during normal operation. The weighting factor is self-adjusted based on observed false positive rates, eliminating the need for manual calibration and reducing the complexity of system configuration and maintenance.
3Reliability
If strict maximum network latency requirements are enforced, then data accuracy and reliability are improved, but application availability decreases due to frequent latency violations
Solution Approach 1:
The system changes the parameter of latency assessment from a binary pass/fail based on maximum latency to a probabilistic assessment. By calculating the probability that latency will meet requirements based on historical data and current conditions, the system can make more nuanced decisions about application execution, maintaining data accuracy requirements while improving availability through probability-based enablement.
Solution Approach 2:
The system implements feedback through confusion matrix tracking, where actual latency measurements and application execution outcomes are recorded and used to update the weighting factor and probability calculations. This feedback loop allows the system to learn from past performance and continuously improve its latency predictions, balancing data accuracy requirements with application availability.
Data Source
AI summary
A vehicle application enabling system is provided and includes a memory and initialization, latency evaluation, and application enable modules. The initialization module: receives a maximum network latency; sets a percentage of occurrences that the maximum network latency is not satisfied, a maximum false positive rate, and a maximum deviation value; and calculates a weighting factor based on the percentage of occurrences, maximum false positive rate and maximum deviation value. The latency evaluation module implements a latency evaluation algorithm, which includes: comparing one or more latency estimates to the maximum network latency to provide one or more samples; updating confusion matrix statistics based on the one or more samples; updating a probability threshold based on the maximum false positive rate; updating weighted observations based on the weighting factor; and determining a predicted decision based on the probability threshold. The application enable module executes the vehicle application based on the probability threshold.