Robotaxis as Computing Clusters: A Stochastic Modeling Approach
Bibliographic record
Abstract
In the future, passengers will likely be able to request an autonomous taxi or robotaxi to transport them to their destinations. The operators of a robotaxi fleet may want to operate their vehicles continuously, as these vehicles are often costly to build and operate. However, as the passenger service requests arrive randomly, there will be idling taxis. The fleet operators can utilize the computing resources of idling taxis to execute tasks from external customers. In this paper, we evaluate the task execution performance of a robotaxi fleet. We assume that passenger service has preemptive priority over task execution. Thus an idling taxi executing a task may have to serve a passenger. We study the system’s performance under infinite and finite backlogs of tasks. In the infinite backlog case, there will always be tasks to execute for idling taxis. In this case, we derive the probability distribution of the number of tasks the fleet can serve during a cycle, which is the interval between two consecutive time points when the entire fleet becomes idle. In the finite backlog case, we assume the tasks requiring service arrive at the system according to a Poisson process and derive an approximation for the average task delay. Finally, we present the numerical results for the analysis and the simulation results to show the correctness of the work.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".