Crowdsensing-Based Personalized Dynamic Route Planning for Smart Vehicles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Current route planning systems report to the driver routes based on expected travel time and distance. However, these systems do not provide individualized routing options. With the current routing systems lacking the provision of individualized routing choices, a routing framework which provides a personalized route option not solely based on time and distance would be a step up. With the expanding sensing and computing capabilities in both vehicles and smart devices along with the promising low-latency of 5G networks, a real-time personalized route planner is achievable. In this article, a route planning framework that utilizes the in-vehicle and smartphone sensors to build a crowdsensed database on road surface quality and the driver's personalized skillfulness in different driving environments is proposed. Such databases are leveraged to provide drivers with routing options based on their personal preferences. This framework is tested and validated through a case study of a real driving scenario in Kingston, Ontario to show the framework capabilities compared to conventional route planning.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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