Path Following Performance of Narrow Tilting Vehicles Equipped With Active Steering
Bibliographic record
Abstract
Narrow Tilting Vehicles offer an opportunity to reduce both traffic congestion and carbon emissions by having a small road footprint, low weight, and a small frontal area. Their narrow width requires that they tilt into corners to maintain stability; this may be achieved by means of an automated tilting system. Automated tilt control systems can be classed as Steering Tilt Control (STC) in which active control of the front wheel steer angle is used to maintain stability, Direct Tilt Control (DTC) in which some form of actuator is used to exert a moment between the tilting part(s) of the vehicle and a non-tilting base, or a combination of the two (SDTC). Combined SDTC systems have the potential to offer improved performance as, unlike STC systems, they are effective at low speeds whilst offering superior transient roll stability to DTC systems. However, alterations to the front wheel steer angle made by STC and SDTC systems may result in unwanted deviations from the driver’s intended path. This paper uses multi-body simulations of a three-wheeled Narrow Tilting Vehicle performing an emergency lane change manoeuvre to show that the path followed by a SDTC equipped vehicle in response to a given series of steer inputs differs significantly from that followed by a DTC equipped vehicle. It is also shown that by using a revised series of steer inputs, a vehicle equipped with SDTC is able to successfully follow a similar path to one equipped with DTC, and that the roll stability of the vehicle is not unduly compromised. Finally, the influence of higher DTC system gains on the SDTC system is considered. It is shown that the result is a small improvement in the vehicle’s path following response at the expense of a small reduction in vehicle roll stability.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".