Interaction-Aware Decision-Making for Autonomous Vehicles
Bibliographic record
Abstract
Complex, dynamic, and interactive environment brings huge challenges to autonomous driving technologies. Because of the strong interactions between different traffic participants, autonomous vehicles (AVs) must learn how to interact with other road users. Failure to consider interaction when making decisions may result in safety issues. In this article, an interaction-aware decision-making approach is proposed for AVs. First, focusing on the interaction at uncontrolled midblock crosswalks, the game theory is used to model the vehicle–pedestrian interaction (VPI). Then, an interaction inference framework is developed using the interaction model to obtain interaction information with pedestrians. Besides, a collaborative action planning method is proposed to generate collaborative actions. More importantly, interactive decision-making is formulated as an optimization problem by considering the task item and action item. Furthermore, considering pedestrians’ different levels of cooperation, the social force pedestrian model is developed. Then, a highly interactive environment is constructed. Finally, qualitative and quantitative evaluations are carried out against three baseline methods. The result shows that our method can interact with different pedestrians and balance safety and efficiency compared to baseline methods.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".