Ecological Adaptive Cruise Control of a plug-in hybrid electric vehicle for urban driving
Bibliographic record
Abstract
Plug-in hybrid electric vehicles (PHEVs) are a promising step toward reaching a fully green passenger vehicle with large pre-chargeable batteries which offer a very good fuel economy. Many researchers are now focussing on making vehicles safer and more efficient by improving vehicles' interaction with their environment and assisting drivers to make better decisions with less errors. Adaptive Cruise Control (ACC) systems are a way to connect vehicles to the most important part of their environment which is the surrounding traffic. It has been shown that using the data from preceding traffic by ACC systems can significantly improve the driving performance. This study presents an ecological ACC (Eco-ACC) system that takes advantage of a radar and traffic light-to-vehicle communications (TL2VC) to predict the future trajectory of its preceding vehicle and employs this information to drive the vehicle with an ecological driving pattern. The problem is formulated as an optimal control problem and the model predictive control (MPC) method is used to solve it. The proposed Eco-ACC is evaluated using a high-fidelity Toyota Prius PHEV model, which shows about 17% improvement in the energy cost compared to a regular car-following ACC.
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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".