ReSeleCT: A New Approach for Continual Learning With Application to Vehicle State Estimation
Bibliographic record
Abstract
In time series and data stream analysis, neural networks (NNs) have demonstrated remarkable capacity in predicting current and future states. However, NNs often suffer from catastrophic forgetting (CF) when adapting to new tasks or data domains. This issue is particularly pressing in vehicle state estimation, given that new data domains are frequently invited to a pre-trained model. While memory-based techniques have been proposed to address CF in continual learning, they are less effective for time series regression problems due to the absence of a suitable subset selection strategy. This paper presents a novel method called ReSeleCT (Representative Selection for Continual learning in Time-series scenarios) for identifying and capturing a coreset of old datasets in memory-based continual learning with application to vehicle velocity estimation. The approach focuses on selecting a representative subset of historical data to retain key information from previous tasks. The framework is applied to estimate a vehicle's longitudinal and lateral velocities using neural networks, incorporating new maneuvers into the previously trained model. Experiments on sensor data from an electric Equinox vehicle demonstrate that ReSeleCT efficiently adapts to new data domains while preserving prediction accuracy on the old dataset, with the added benefit of fast model adaptation without significant computational overhead. Comparison against other algorithms in continual learning shows a superior performance of the proposed method in terms of prediction error while maintaining an acceptable training time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".