Towards Self-Organizing connected and autonomous Vehicles: A coalitional game theory approach for cooperative Lane-Changing decisions
Bibliographic record
Abstract
This research introduces a novel approach to cooperative decision-making among self-organizing connected and autonomous vehicles (CAVs). In this approach, a coalitional game is played by a group of players who form alliances of different sizes based on the collective payoff they receive. The players continuously evaluate the potential benefits of different coalition formations and adjust their decisions accordingly. The proposed approach utilizes the V2V communication feature of CAVs, which enables CAVs to participate in a cooperative game, thereby resolving conflicting situations that often arise during lane-changing decisions. By working together within the same coalition, CAVs on a hypothetical three-lane freeway segment can collectively determine their target lanes, rather than engaging in individual decision-making that could result in a win-lose situation. The proposed approach considers up to nine CAVs interacting with each other and aims to find Pareto-optimal coalitions in lane-changing decisions. The approach considers lead CAVs that cooperate via acceleration to enlarge the gap between the subject and lead CAVs. The game is modelled as a dynamic transferable utility problem, allowing the utilities obtained from the coalition agreement to be expressed as real numbers and distributed among coalition members. The framework is generalizable to other traffic and demand management problems while the cooperative CAVs can be compensated for reaching an agreement in a universal, collectible, and tradable credit scheme (UCTCS) that can be used in a wide spectrum of traffic and demand management applications. The effects of the proposed coalitional lane-changing decision-making on traffic efficiency are compared to a non-cooperative decision-making model on a simulated road segment. Overall, our analysis suggests that the proposed coalitional approach can positively impact macroscopic traffic characteristics, leading to potentially improved traffic flow, reduced congestion, and enhanced travel time efficiency.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".