Design and Validation of Differential Braking Controllers for Sport Utility Vehicles Considering the Interactions of Driver and Control System
Bibliographic record
Abstract
This paper introduces the design and validation of a differential braking controller for sport utility vehicles (SUVs) with driver-in-the-loop real-time simulations. SUVs are designed with high ground clearance, which is a main reason for their high rollover rate. A nonlinear 3 degrees-of-freedom (DOF) SUV model is generated to design a differential braking controller. The desired states are determined using a 2-DOF bicycle model and the lane-keeping control results derived from vehicle velocity and road curvature. The actual vehicle states of the 3-DOF model may deviate from the desired ones. A sliding model controller (SMC) is designed to minimize the state error to improve the performance measures, e.g., yaw stability. The SMC controller designed in LabVIEW is integrated with a virtual SUV generated in CarSim for co-simulations. The controller is first examined in the emulated sine-with-dwell maneuver specified in FMVSS 126. The SUV performance depends not only on the control strategy, but also on its interaction with the human driver. To study the interaction of the driver and the controller, the overall system is simulated using driver-software-in-the-loop (DSIL) real-time simulations under a double-line-change (DLC) maneuver. The simulations show that, even equipped with the electronic stability control (ESC) system, the driver still plays an important role in the vehicle dynamics. The simulations demonstrate the effectiveness of the proposed differential braking controller, and the research discloses important interactions of driver and ESC system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 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".