Scalable Game-Theoretic Decision-Making for Self-Driving Cars at Unsignalized Intersections
Bibliographic record
Abstract
Sharing the road with human drivers requires autonomous vehicles to account for interactions between them. To resolve traffic conflicts in unsignalized intersections, a robust adaptive game-theoretic decision-making algorithm with scalability is proposed based on the receding horizon optimization, level-k game theory, and switching directed graph. A mismatch between the inherent (k-1) assumption of level-k theory and actual driver type may lead to unsafe action selection and reduce driving safety. To handle this problem, in this work, an autonomous vehicle would predict the driver types of surrounding vehicles based on historical interactive behaviors between them and utilize its trust in the driver types to achieve an adaptive driving strategy. Besides, switching interaction graph is incorporated into an adaptive level-k framework for the first time, so as to cut off the connection between ego vehicle and nearby vehicles that do not affect driving behavior of the former, contributing to reducing the computing complexity. The feasibility, effectiveness, and real-time implementation of the proposed method are validated on both hardware and ROS-Gazebo platform.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".