A simplified dynamic model with driver’s NMS characteristic for human-vehicle shared control of autonomous vehicle
Bibliographic record
Abstract
Since the large-scale application of fully autonomous vehicles is difficult to be commercialized in the short term, human-vehicle shared control (HVSC) is a promising technique. To implement the control authority allocation and observe the driver characteristic, it is essential to develop an efficient HVSC dynamic model with the driver’s neuromuscular characteristic (NMS). To further our previous research, a simplified HVSC dynamic model is proposed in this paper. This model simplifies the non-critical NMS parameters such as muscle spindle feedback, which has no significant feedback effect while retaining essential NMS characteristics such as stretch reflection and intrinsic properties. The model consists of a model predictive controller (MPC) coupled with a driver NMS model and a 2 DOF vehicle model. The stability is proved by Lyapunov stability theory. Moreover, a field experiment was conducted for validation of the model. The V-Box is utilized to measure the vehicle’s state signals, such as steering wheel angle and pedal stroke. Subsequently, the adaptive genetic algorithm (AGA) is employed to identify the model parameters based on the experimental results. The comparison between the experiment and the model output shows that the proposed model can accurately represent the driver’s NMS characteristics and vehicle dynamic parameters. This paper will serve as a theoretical basis for the control authority allocation for L3 class autonomous vehicles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".