The impact of drivers’ acceleration style on the vehicle energy performance: a real-world case study.
Bibliographic record
Abstract
The present study investigates the impact of different acceleration styles collected from a sample of human drivers on the vehicle's energy performance in real-world trips. The variations in CO 2 emissions according to different acceleration patterns are benchmarked to real-world trips from a driving campaign that involved 20 drivers on the same reference vehicle. The paper builds on a previous work that benchmarked the correlation between CO 2 emissions and acceleration behaviour to the standard homologation Worldwide Harmonized Light Vehicles Test Cycle (WLTC). The current work extends the application to real-world conditions, modifying the acceleration events of real-world trips according to the driver's acceleration attitude and subsequently simulating the energy performance, specifically the CO 2 emissions. The heterogeneity of the driver's acceleration style is characterised by the vehicle-Independent Driving Style metric (IDS), which represents the driver's acceleration aggressiveness. The results confirm a significant impact in CO 2 emissions of the acceleration behaviour, leading to differences above 10% between the most timid (IDS=0.05) and most dynamic styles (IDS=1) when a consistent acceleration style is followed for the whole trip, in contrast with the 5% difference found when benchmarking to the WLTC cycle. The impact is substantially reduced (± 1 CO 2 g/km) when considering the stochasticity of the human acceleration style.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".