Advancing Vulnerable Road Users Safety: Interdisciplinary Review on V2X Communication and Trajectory Prediction
Bibliographic record
Abstract
The advancements in Intelligent Transportation Systems have brought a heightened focus on safety, driven by innovative solutions like Vehicle-to-Everything (V2X) communication, Advanced Driver Assistance Systems(ADAS), and Cooperative Intelligent Transport Systems (C-ITS). Ensuring the safety of vulnerable road users (VRUs) remains a top priority in the transportation sector, and harnessing these cutting-edge technologies offers immense potential to address this concern effectively. This collaborative approach greatly enhances VRUs safety, reduces accidents, and promotes efficient and sustainable mobility. This paper reviews the latest developments in V2X technology, emphasizing its role in improving VRU safety. It explores current V2X standards, use cases on VRU safety, and the evolving research landscape, particularly in trajectory prediction models. These models are critical for foreseeing potential collisions and mitigating V2X-based data transmission delays. Trajectory prediction models can also offer a promising solution to ongoing challenges such as data association, scalability, and bandwidth requirements. By focusing on trajectory prediction, this paper highlights the vital role of predictive analytics in safeguarding vulnerable road users and advancing transportation safety.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".