Evaluating the Viability of Computational Offloading for Vehicles Under Adverse Network Conditions
Bibliographic record
Abstract
The safe and efficient operation of automated vehicles requires processing massive amounts of sensor data. However, the computational capabilities of vehicles are often limited. Recent results point to computational offloading as a promising solution for transferring raw sensor data to be processed elsewhere. This alleviates vehicles from performing costly computations while increasing their perception of complex environments. The work in this paper evaluates the resilience of such solutions, specifically focusing on adverse network conditions, which are often overlooked when evaluating computational offloading. To emulate adverse network conditions, we use synthetic network interference that includes, e.g., packet loss, throughput rate limiting, packet corruption, and RF attenuation. We conducted experiments with a real vehicle on a test track, where object detection was offloaded to an edge server. An optical camera, one of the most common perception sensors, was mounted on the vehicle to scan the environment. The experimental results indicate that network conditions can significantly impact the object detection performance. Packet loss and packet corruption proved to be especially impactful on the accuracy of detections. During the scenario of 5% packet corruption, the median value of false detections reached as high as 20%. The results emphasize the need for resilience and robustness to poor network conditions when designing computational offloading strategies.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".