Multi-mode Evasion Assistance Control Method for Intelligent Distributed-drive Electric Vehicle Considering Human Driver’s Reaction
Bibliographic record
Abstract
Abstract Vehicle collision avoidance (CA) has been widely studied to improve road traffic safety. However, most evasion assistance control methods face challenges in effectively coordinating collision avoidance safety and human-machine interaction conflict. This paper introduces a novel multi-mode evasion assistance control (MEAC) method for intelligent distributed-drive electric vehicles. A reference safety area is established considering the vehicle safety and stability requirements, which serves as a guiding principle for evading obstacles. The proposed method includes two control modes: Shared-EAC (S-EAC) and Emergency-EAC (E-EAC). In S-EAC, an integrated human-machine authority allocation mechanism is designed to mitigate conflicts between human drivers and the control system during collision avoidance. The E-EAC mode is tailored for situations where the driver has no collision avoidance behavior and utilizes model predictive control to generate additional yaw moments for collision avoidance. Simulation and experimental results indicate that the proposed method reduces human-machine conflict and assists the driver in safe collision avoidance in the S-EAC mode under various driver conditions. In addition, it enhances the vehicle responsiveness and reduces the extent of emergency steering in the E-EAC mode while improving the safety and stability during the collision avoidance process.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".