A data-driven model-based shared control strategy considering drivers’ adaptive behavior in driver-automation interaction
Bibliographic record
Abstract
Shared control scheme improves the driving performance while having an impact on driver behavior, drivers would constantly adapt their steering behavior mechanism in interaction with a shared controller. This paper proposes a novel data-driven model-based shared control strategy which is capable of considering drivers’ adaptive behaviors in driver-automation interaction to improve safety. The Koopman operator theory, which is a pure data-driven modeling technology, is adopted to yield an explicit control-oriented driver-vehicle model for shared controller design. Besides, a weighted online extended dynamic mode decomposition (WOEDMD) algorithm is proposed to update the Koopman driver model online for better capturing the driver’s adaptive behavior in driver-automation interaction, which settles the problem of driver’s potential behavior mechanism variations in practice. Based on the Koopman driver-vehicle model, a model-based shared controller is proposed in the model predictive control (MPC) framework, and the potential fields are incorporated in the optimization objectives to ensure safety. A group of human-in-the-loop experiments are conducted on a driving simulator to demonstrate the effectiveness of the modeling and shared control methods. The results show that the Koopman operator theory can be exploited for modeling the dynamics of the driver-vehicle integrated system, and the drivers’ adaptive behavior can be captured by the WOEDMD algorithm. Moreover, the shared controller considering the driver’s adaptive behavior improves the driving safety in the collision avoidance task.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".