Energy Efficient Path Planning for Low Speed Autonomous Electric Vehicle
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This work presents an energy efficient approach for autonomous electric vehicle path planning. When the vehicle is moving at low speed, the rolling resistance losses can be more than the aerodynamic losses, on a flat ground. In particular, for warehouse low-speed electric vehicles, different road-tire frictions may lead to varying rolling resistance which impacts the energy consumption.The minimization of the energy consumed by a vehicle is important in the context where the number of charging stations is limited. The proposed method aims at planning the most energy efficient path by taking into account the rolling resistance and the path length. Unlike most studies reported in the literature, this energy efficient path planner can achieve a good trade-off between preserving battery energy and not extending to much the path length. The preliminary results obtained through extensive simulation indicates that the optimized path planner is effective and robust.
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.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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 it