Smart Cyber Forensics of Rear-End Collision based on Multi-Access Edge Computing
Bibliographic record
Abstract
Due to the multiplicity of rear-end collisions and the complexity of collision forensics, it is necessary to reconstruct and evaluate the accident. In this paper, we propose a rear-end forensics and liability determination scheme based on multi-access edge computing (MEC) to facilitate rapid smart forensics and determine the accident liability reasonably. Specifically, a rear-end accident forensics model for smart forensics is first developed to collect the information about road environments, vehicle information stored on edge infrastructures, and data logging within the accident scene. We then establish a data chain of rear-end incident (CRI) from the forensic information closely related to vehicles involved in the collision. The possible security state (PSS) of the rear vehicle can be obtained by decoding the actual state (AS) of the front vehicle in the CREI using the Hidden Markov Model (HMM). Accordingly, the rear-end collision evaluation metrics is set up, calculating the estimated liabilities (ELs) for each vehicle based on the driver's attention level (AL) deduced from the proposed model. Finally, the liability of involved vehicle can be determined by HMM-based driver's AL detection algorithm. The performance evaluations show that the proposed smart forensics and rear-end liability determination scheme can determine rear-end liability faster and more reasonably.
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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 | 0.000 |
| 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 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".