Balancing Individual and Collective Interests: Integrating Transformer-Based PPO and Social Intelligence for Autonomous Vehicles at Unsignalized Intersections
Bibliographic record
Abstract
Autonomous vehicles (AVs) promise a transformative shift in transportation paradigms, yet their integration into mixed-traffic environments poses complex challenges. In this paper, we propose a novel framework that combines Proximal Policy Optimization (PPO) with the Self-attention mechanism from transformers and Social Value Orientation (SVO) to address the delicate balance between individual and collective interests at unsignalized intersections. By integrating SVO, a psychological concept characterizing individuals' preferences for cooperation and competition, into PPO with Self-attention mechanism, our framework enables AVs to make ethically informed decisions that consider both individual and collective welfare. Transformers, known for their ability to capture long-range dependencies and model complex relationships within data, enhance the reinforcement learning capabilities by providing more effective and context-aware decision-making. We present a detailed formulation of the PPO-Transformer-SVO framework, discussing how SVO influences AV behavior and cooperation strategies at intersections. Through simulation experiments, we demonstrate the effectiveness of our approach in optimizing traffic flow, minimizing collisions, and ensuring collective and individual benefits among road users. Our results highlight the potential of integrating human-inspired social preferences and advanced reinforcement learning techniques into AV decision-making algorithms to enhance the safety, efficiency, and ethicality of future transportation systems.
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.
How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".