Trajectory Penetration Characterization for Efficient Vehicle Selection in HD Map Crowdsourcing
Bibliographic record
Abstract
In this article, we investigate the worker (i.e., vehicle) selection problem in vehicle-based crowdsourcing (VBC), where vehicles in a specific area are recruited by the crowdsourcing platform to collect geographical information in real driving scenarios for autonomous driving. Given a limited recruitment budget, we formulate a cumulative platform utility maximization problem (CMP) to obtain the optimal worker set. The CMP is unsolvable directly as the platform has no prior information of workers at the initial stage (also known as “cold start”) and the cost of collecting all workers' information is prohibitive. To solve the problem, we first conduct a comprehensive data analytics on two real-world vehicle traces and obtain two crucial observations: 1) trajectory of individual vehicle is highly uncertain that it is difficult to make accurate prediction and 2) the overall distribution of vehicular trajectory penetration (measured by collection quantity and coverage) has a diurnal pattern and varies with weekly periodicity. Inspired by the insights, we propose the performance transfer-based online worker selection (POSE) scheme, which works independently from trajectory prediction with two components, i.e., transfer learning-based performance estimation and online worker selection (OWS). Based on the diurnal pattern, the former component collects a short-period trajectory penetration data of vehicles for model fitting, which can output a specific numerical distribution. With the fitting model, we can identify and select vehicles with high trajectory penetration at the initial stage to cope with the “cold start” problem. Then, we map the worker selection problem into a multiarmed bandit problem and develop upper confidence bound-based approach to solve it. Extensive trace-driven simulations are carried out and the results demonstrate the efficiency of POSE in terms of cumulative platform utility.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".