Socially Intelligent Path-Planning for Autonomous Vehicles Using Type-2 Fuzzy Estimated Social Psychology Models
Bibliographic record
Abstract
This paper presents a novel framework for socially aware path-planning in autonomous vehicles, integrating Social Value Orientation (SVO) within Artificial Potential Fields (APF) and employing Type-2 Fuzzy Logic for robust SVO approximation. By incorporating an adaptive gradient descent algorithm and leveraging a Type-2 fuzzy system for dynamic modeling of social psychology in vehicular navigation, we enhance autonomous vehicles’ ability to interpret and react to social cues in real-time traffic scenarios. Our approach significantly improves interaction with human road users, ensuring safer and more efficient navigation. The proposed model addresses the limitations of traditional APFs, such as local minima issues, by incorporating dynamic enhanced firework algorithms and resistance networks. It also considers vehicle dynamics, including nonholonomic constraints and tire forces, using a bicycle model for realistic trajectory planning. We introduce a comprehensive set of social cues for pedestrians and vehicles, operationalized through interval type-2 fuzzy system (IT2FS) approximation, to accurately estimate SVO and adjust AV behavior accordingly. Validation is conducted through extensive simulations in a realistic environment using the CARLA simulator, demonstrating the effectiveness of our socially intelligent path-planning mechanism in diverse driving situations. The results show a significant improvement in AV performance, with a 2.93% more altruistic estimation for the vehicle in the right lane and a 1.85% more altruistic estimation for the immobile vehicle. Additionally, the system demonstrated smoother acceleration and steering profiles, reducing peak longitudinal acceleration from$4.181~m/s^{2}$to$0.196~m/s^{2}$and improving overall driving stability. This framework enhances autonomous vehicles’ safety, efficiency, and social acceptability, contributing to their successful integration into urban traffic systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".