Reducing fuel consumption and tailpipe nitrogen oxides emissions through large-spacing truck platooning
Bibliographic record
Abstract
To enable safe and efficient truck platooning on public roads with hilly terrain, this study develops an innovative controller using real-world truck data from the on-road platooning trials. The designed controller maintains safe spacing while simultaneously saving fuel and minimizing tailpipe nitrogen oxides (NOx) emissions. A two-truck platoon implementing the controller is simulated using validated models based on experimental data. The results show that the developed controller effectively limits spacing errors within a preset safety buffer. Even at time gaps exceeding 2 s, the follower truck achieves up to a 23.2% reduction in NOx emissions and a 6.6% fuel saving under the Alberta Highway 2 driving cycle with varying road grades. These benefits stem from suppressing rapid engine torque fluctuations and minimizing unnecessary decelerations and accelerations. This study highlights the feasibility of large-spacing truck platooning in real-world conditions, ensuring safety while optimizing fuel consumption and emissions.
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 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".