Socially Intelligent Path-Planning for Autonomous Vehicles Using Type-2 Fuzzy Estimated Social Psychology Models
Why this work is in the frame
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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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.181~m/s^{2}$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.196~m/s^{2}$ </tex-math></inline-formula> 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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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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