Distributed Meta Learning for Low-Latency C-ITS Traffic Prediction
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Solution Overview
Problem
Distributed ensemble learning for time series prediction in cooperative intelligent transport systems (C-ITS) is limited by high latency due to the need for all base learners to complete their predictions before the meta learner can make a final prediction, which is impractical for low-latency applications like controlling self-driving cars.
Innovation Solution
A system that builds a weighted correlation graph between base learners based on predicted and realized traffic values, using a meta learner to select the most accurate prediction within a time limit while adapting network resources to reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If distributed ensemble learning is used for time series prediction in C-ITS, then prediction accuracy is improved, but latency increases due to the need for all base learners to complete predictions before the meta learner can make a final prediction
Solution Approach 1:
The system performs preliminary actions by pre-establishing correlation graphs and ranking base learners before actual prediction time. The meta learner pre-computes correlation metrics and maintains a ranked list of base learners based on historical performance, so that during real-time prediction, the system can immediately select top-ranked learners without waiting for all learners to complete their predictions.
Solution Approach 2:
The system applies partial action by selecting only the top-k ranked base learners instead of requiring all base learners to complete their predictions. The meta learner aggregates predictions from a partial subset of base learners (those with highest correlation scores) rather than waiting for complete ensemble convergence, thus reducing latency while maintaining acceptable accuracy.
2Reliability
If all base learners must complete predictions before meta learner makes final prediction, then prediction reliability is improved, but system responsiveness deteriorates
Solution Approach 1:
The system implements feedback mechanisms where the meta learner continuously monitors prediction results from base learners and updates correlation graphs accordingly. Historical performance data from previous predictions is fed back to refine the ranking of base learners, allowing the system to learn from past reliability patterns and make informed decisions about which learners to trust most in real-time scenarios.
Solution Approach 2:
The system changes the parameter of base learner selection from a fixed 'all learners must complete' approach to a dynamic ranking-based selection. The meta learner adjusts which base learners are selected for aggregation based on their correlation scores and historical reliability, transforming the rigid requirement into a flexible, adaptive selection process that balances reliability and responsiveness.
Data Source
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AI summary
According to an aspect, there is provided an apparatus for performing the following. The apparatus obtains alternative predicted values of a traffic metric of cooperative intelligent transport system from base learners and corresponding one or more realized values. The apparatus builds a weighted correlation graph describing correlations between the base learners based on the alternative predicted and realized values. Nodes of the weighted correlation graph correspond to the base learners and are associated with first weights defined based on values of a pre-defined error metric defined based on the alternative predicted and realized values. Edges in the weighted correlation graph are associated with second weights defined based on values of a correlation coefficient calculated for arrival times of the alternative predicted values. The apparatus calculates a maximum clique based on the weighted correlation graph and causes adaptation of amount of network resources available to the base learners based thereon.