Integrating deep reinforcement learning and social-behavioral cues: A new human-centric cyber-physical approach in automated vehicle decision-making
Bibliographic record
Abstract
This paper proposes a novel human-centric approach to enhance decision-making for autonomous vehicles in complex urban driving situations by integrating Deep Q-Network (DQN) reinforcement learning and social value orientation. In the proposed method, a deep neural network (DNN) is employed to approximate the optimal Q-values for various states and actions in the space of possible actions and reachable states. In order to improve the optimization convergence, an Adam optimization is proposed by combining the advantages of adaptive learning rates and momentum methods. The proposed framework also incorporates a collision avoidance component that allows vehicles to navigate safely through pedestrian crossings. The proposed method is validated through simulation experiments, which show that the proposed approach outperforms traditional decision-making and RL methods in terms of safety and efficiency. Finally, the results demonstrate that integrating social value orientation and DQN-RL can lead to more human-like and socially compliant decision-making frameworks for automated vehicles. This research contributes to developing a new human-centric cyber-physical approach for automated vehicle decision-making and has significant implications for designing future intelligent transportation 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".