Stochastic Hierarchical Game (SHG) for Multi‐Agent Autonomous Driving With Uncertain Driver Intentions
Bibliographic record
Abstract
ABSTRACT Safe and efficient decision‐making is crucial for autonomous vehicles (AVs) to operate in complex traffic scenarios. The uncertain intentions of surrounding agents (e.g., AVs, human‐driven vehicles, and pedestrians) and their complex interactions pose significant challenges to the decision‐making process. In this work, we propose a novel stochastic hierarchical game (SHG) framework to address the challenges of decision‐making under uncertain intentions of agents in complex multi‐vehicle settings. The hierarchical game approach addresses the scalability of multi‐agent interactions. In the stochastic game framework, the uncertain intentions of traffic agents are captured using random mobility models (RMMs), the parameters of which can be estimated using data‐driven methods. We propose a novel decision‐making method for the ego vehicle based on the expected optimal actions under uncertain intentions. To solve the SHG efficiently, an uncertainty quantification method called the multivariate probabilistic collocation method with an orthogonal fractional factorial design (MPCM‐OFFD) is deployed. Comparative simulation studies are conducted to verify the effectiveness of the proposed framework.
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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".