Bridging the gap between infrastructure based V2I and decentralized V2V communication: Keynote at the 8th Workshop on User Mobility and Vehicular Networks (ON-MOVE), 39th IEEE Conference on Local Computer Networks (LCN), Edmonton, 8-11 September 2014
Bibliographic record
Abstract
Extensive research activities and field operation tests on V2I and V2V communication have been carried out for more than a decade. However, there are still some unsolved issues for successful and sustainable deployment of cooperative systems based on vehicular communication. Open challenges for a deployment of cooperative applications with high reliability and user acceptance include: minimal performance requirements on positioning accuracy and wireless performance to ensure interoperability, congestion control and adaptive data aggregation for reliable communications, life cycle management and security & privacy issues to ensure the user acceptance and protect the investments. Advanced cooperative applications require a deployment of vehicles equipped with V2V communication at high penetration rate. However, presently only applications which do not require time-critical communication and high penetration rate can be deployed based on cellular communication (3G/4G). But how can we bridge the penetration rate gap and introduce also time-critical applications step-by-step? One promising solution might be selective infrastructure support: Roadside units with 802.11p technology, initially deployed on accident prone spots, extend the coverage and enable time-critical applications for every equipped vehicle from the start of deployment. It is also possible to reduce the latency of cellular communication by moving the applications closer to the road. Thereby, applications reside directly on mobile base stations and do not need additional connectivity to the core network.A second approach is hybrid communication providing seamless connectivity. Vehicles equipped with multiple wireless technologies are able to decide which interface to use based on the availability of the technology, its current coverage, or requirements of the applications. With this approach, all traffic participants including pedestrians and vulnerable users canbe integrated seamlessly into one common ITS system. Hence, the overall question is: What will bring us closest to the goal of seamless V2X connectivity? Is the full V2V penetration rate the ultimate solution? Do we have to wait for the next evolution of cellular communication technologies? Or will the hybrid concept with seamless connectivity and evolutionary integration of other technologies pave the way?
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How this classification was reachedexpand
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; both teacher heads agree on what is shown here.
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".