Formulating Vehicle Aggressiveness Towards Social Cognitive Autonomous Driving
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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