A Wireless-Communication-Based Active Safety System for Articulated Heavy Vehicles
Bibliographic record
Abstract
This paper presents an active trailer steering (ATS) system using a wireless communication link to facilitate exchanging information among vehicle units of articulated heavy vehicles (AHVs). A challenge for developing and implementing the wireless-communication-based ATS system is to address the problems of delay and packet-loss. Embedding wireless communication transceivers on an AHV may introduce delays for data exchange, and the transmitted data may be lost. Many reasons may lead to the delay and packet-loss, e.g., channel fading, noise burst, interference, etc. The proposed ATS system may prevent unstable motion modes of AHVs if the sensor data reach the controllers/actuators in real-time with an acceptable level of delay and packet-loss. In order to ensure the performance of the ATS control, a Kalman-filter-based estimator is introduced. The estimator uses the available dynamic data to estimate the current states of the AHV in case some sensor information is not available due to a delay or an outage in the wireless communication link. To investigate the effect of the time delay due to the wireless communication on the performance of the ATS control, co-simulations are conducted. The wireless network is modeled using TrueTime toolbox, the ATS controller is designed in SimuLink package, and the AHV model is constructed in TruckSim software. Integrating the wireless network modeled in TrueTime, the ATS controller designed in SimuLink, and the AHV model constructed in TruckSim leads to the co-simulation platform. Under the emulated double lane-change test maneuver, the effects of the wireless communication with two schemes on the direction performance of the AHV are examined.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".