PLTO: Path Loss-Aware Task Offloading for Vehicular Cooperative Perception
Bibliographic record
Abstract
Leveraging task offloading in Vehicular Edge Computing (VEC) via V2X can present unique and robust solutions to the challenges associated with cooperative perception in Autonomous Vehicles (AVs). However, making task offloading decisions that account for the risk of communication failure due to path loss, while adhering to the stringent QoS requirements of cooperative perception has been mostly overlooked. In this paper, we propose PLTO, a Path Loss-Aware Task Offloading scheme that accounts for path loss for Line-of-Sight (LOS), Obstructed LoS (OLoS), and Non-LoS (NLoS) propagation in vehicular communications. We formulate the task offloading problem as a 0–1 Integer Linear Program (0–1 ILP) that aims to minimize the path loss and response delay, while sustaining a certain satisfactory level of improved perception and situational awareness demanded by users. We also propose PLTO-Heuristic (PLTO-H), a scheme to solve the task offloading problem using the MTHG heuristic. Extensive simulations show that PLTO yields significant improvements of up to 17%, 10%, and 23% in terms of packet delivery ratio, Received Signal Strength Indicator (RSSI), and average response delay, respectively, compared to a baseline task offloading scheme that does not consider communication efficiency. In addition, PLTO-H achieves a near optimal solution, with a small gap of up to 6%, 5% and 1.2% in terms of packet delivery ratio, RSSI, and satisfaction ratio, respectively.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".