Adaptive Computing Scheduling for Edge-Assisted Autonomous Driving
Why this work is in the frame
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Bibliographic record
Abstract
This paper investigates computing resource scheduling for real-time applications in autonomous driving, such as localization and obstacle avoidance. In our considered scenario, autonomous vehicles periodically sense the environment, offload sensor data to an edge server for processing, and receive computing results from the server. Due to mobility and computing latency, a vehicle travels some distance in the duration between the instant of offloading its sensor data and the instant of receiving the computing result. Our objective is finding a scheduling scheme for the edge sever to minimize the above traveled distance of vehicles. The approach is to determine the processing order according to individual vehicle mobility and computing capability of the edge server. We formulate a restless multi-arm bandit (RMAB) problem, design a Whittle index based stochastic scheduling scheme, and determine the index using a deep reinforcement learning (DRL) method. The proposed scheduling scheme avoids the time-consuming policy exploration common in DRL scheduling approaches and makes effectual decisions with low complexity. Extensive simulation results demonstrate that the proposed indexed-based scheme can deliver computing results to the vehicles promptly while adapting to time-variant vehicle mobility.
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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 | 0.001 |
| 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 it