Development of a Path-following and a Speed Control Driver Model for an Electric Vehicle
Bibliographic record
Abstract
A two-passenger all-wheel-drive urban electric vehicle (AUTO21EV) with four in-wheel motors and an active steering system has been designed and developed at the University of Waterloo. In order to evaluate the handling and performance of such a vehicle in the design stage and analyze the effectiveness of different chassis control systems before implementing them in the real vehicle, the simulation of a large number of different open-loop and closed-loop test maneuvers is necessary. Thus, in the simulation environment, not only is a mathematical vehicle model needed for every test maneuver, but a driver model must also be designed to simulate the closed-loop test maneuvers. The role of the driver model is to calculate the control inputs required to successfully follow a predefined path. Such a driver model can be implemented as an inverse dynamics problem or by a representation of a driver that can look ahead, preview the path, and change the steering wheel angle and acceleration or brake pedal positions accordingly. In this regard, a path-following driver model is developed in this work with an advanced path previewing technique. In addition, a gain scheduling speed control driver model is developed for the AUTO21EV, which adjusts the drive torques of the wheels to minimize the deviation between the desired and actual vehicle speeds.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".