Cognitive Dynamic System for Future RACE Vehicles in Smart Cities: A Risk Control Perspective
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
As one of the largest applications for the Internet of Things in smart cities, the Internet of Vehicles has attracted increasing attention over the years due to its great potential for reshaping both transportation systems and human society. While connected and autonomous vehicles (CAVs) are currently being developed all over the world, they are unfortunately under various potential threats that could endanger the entire CAV network. In this article, we envision a new class of future vehicles, namely risk-sensitive, autonomous, connected, and electric (RACE) vehicles, to cope with uncertain attacks and potential threats. The safety, security, and privacy issues in CAV networks are identified first. Next, the cognitive dynamic system (CDS) is introduced as the supervisor of RACE vehicles for improving and coordinating multiple vehicle-mounted systems. A special function of CDS, cognitive risk control, is then described in the presence of uncertain threats. Last but not least, we present the future directions and research challenges ahead.
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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) | 0.001 | 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 it