Formulating Vehicle Aggressiveness Towards Social Cognitive Autonomous Driving
Bibliographic record
Abstract
Accurately identifying the driving threat could greatly improve the driving safety for autonomous vehicles in the mixed traffic, where the human-driven and driverless, as well as different types of vehicles coexist. The existing safety evaluation methods merely focus on the possibility of collision, which is deficient to evaluate the hazard level due to the symmetry for both the interactive vehicles. Thus, the vehicle aggressiveness model is proposed in this paper based on the asymmetric interactions between different types of vehicles from the perspective of the social cognitions in the human driving. Firstly, a new conceptual framework of the vehicle aggressiveness is constructed, and the factors are analyzed. Secondly, the general mathematic formulation of the aggressiveness is deduced elaborately based on the analogy with the mechanical wave. Thirdly, the simplified formulation is derived by introducing resonant assumption, and an illustration of aggressiveness distribution is presented and discussed. The mathematical analysis and simulation results indicate that the proposed model could explicitly describe the asymmetric characteristics as regards the vehicle mass, motion states and position. Finally, the potential applications in safety assessment, decision-making and motion planning of the social cognitive autonomous driving are discussed. The aggressiveness model provides a new perspective in asymmetric driving safety evaluation and heterogeneous driving behavior model under complex and mixed traffic environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".