Design of SUV Differential Braking Controller Considering the Interactions of Driver and Control System
Bibliographic record
Abstract
This paper presents the design and validation of a differential braking controller for sport utility vehicles (SUVs) using driver-in-the-loop real-time simulations. SUVs are constructed with high ground clearance, which is the main reason for their high rollover rate. A nonlinear 3 degrees-of-freedom (DOF) SUV model with the Dugoff’s tire model is generated to design a differential braking controller. The desired states will be decided using a 2-DOF bicycle model and the automated lane-keeping control results derived from the vehicle velocity and the curvature of the road to negotiate. Actual vehicle states, observed from the nonlinear model, may deviate from the desired ones. A nonlinear robust controller, namely sliding model controller (SMC), is designed to minimize the state error so as to improve the performance measures, e.g., yaw stability. The proposed controller constructed in Labview software is integrated with a virtual SUV developed in CarSim package for co-simulations. The effectiveness of the controller is first investigated using the emulated sine-with-dwell maneuver specified in FMVSS 126. The overall SUV performance depends not only on the control scheme, but on its interaction with the human driver. To investigate the interaction of the driver and the controller, the dynamics of the overall system is simulated using driver-software-in-the-loop real-time simulations (DSIL) under a double-line-change (DLC) maneuver emulated on the DSIL platform in the Multidisciplinary Vehicle Systems Design Laboratory (MVSDL) at the University of Ontario Institute of Technology (UOIT). 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 for enhancing the lateral stability of the SUV. Furthermore, the research discloses important interactions of the driver and the ESC system, and a driver’s training program is highly recommended.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".