Motion Sickness-Oriented Cooperative Control in Mixed Traffic: A Hierarchical MPC Framework With Multi-Objective Optimization
Bibliographic record
Abstract
This study addresses the limitations of existing collaborative control systems for mixed traffic environments where connected and automated vehicles (CAVs) coexist with human-driven vehicles (HDVs), which overemphasize functional safety and energy efficiency loops while neglecting comfort. We propose a hierarchical model predictive control (HMPC) framework incorporating occupants’ motion sickness. The upper layer generates globally optimal speed sequences through dynamic prediction of signal phases, while the lower layer adopts a variable-weight MPC optimization method with a composite cost function integrating travel time, delay, and motion sickness indicators. To address varying CAV penetration rates in mixed traffic, heterogeneous vehicle dynamics models are developed, where CAVs and HDVs employ Cooperative Adaptive Cruise Control (CACC) and Intelligent Driver Model (IDM), respectively. The simulation evaluation results demonstrates that the proposed method achieves significant performance enhancements across diverse CAV penetration rates and traffic saturation scenarios: traffic efficiency is improved by 6.30% and 13.94%, while motion comfort is improved by 51.91% and 25.07%. Field evaluation at the Dongfeng-Huayuan Road intersection in Zhengzhou further confirms these findings, showing 28.97% and 37.87% reductions in travel time and delay, together with 57.81% and 18.18% declines in MSDV and RMS-Jerk, thereby confirming the control strategy’s robustness in real-world perturbed environments.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".