Ecological Adaptive Cruise Control With Optimal Lane Selection in Connected Vehicle Environments
Bibliographic record
Abstract
Recent advances in transportation have enabled lane-specific measurements and lane-specific control. This paper makes use of such data to promote energy efficiency of vehicles. In particular, a Multi-Lane Adaptive Cruise Controller (MLACC) is designed which determines the optimal velocity and lane-to-drive in real-time. This cruise controller solves lane-specific optimization problems to compute an instantaneous trip cost for each lane and selects the lane that poses the lowest cost. The optimization tasks incorporate future route data and encompass multiple objectives including safety, energy efficiency and desired velocity tracking. Therefore, they can be treated as distinct Nonlinear Model Predictive Control (NMPC) problems that have to be solved altogether in each sampling time. To handle the computational load of solving multiple NMPCs in real-time, an integration of Newton and Generalized Minimal Residual numerical methods is employed. The proposed MLACC is implemented for a 2013 Toyota Prius and a wide range of simulation studies are performed to examine the controller. Specifically, hardware-in-the-loop experiments are utilized to evaluate the real-time implementability of the controller. In addition, extensive model-in-the-loop simulations are carried out and the results are compared with driver-in-the-loop experiments. Simulation results indicated that speed profiles and lane changes suggested by MLACC yield up to 27% improvement in energy consumption compared to human drivers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".