Stackelberg Game Based on Trajectory Prediction for Lane Change in Mixed Traffic
To address lane-changing conflicts between intelligent and human-driven vehicles in mixed traffic environments, this study proposes a Stackelberg game-based decision-making method for autonomous vehicles. A Stackelberg game framework is established between autonomous vehicles (leaders) and target-la...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11105015/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | To address lane-changing conflicts between intelligent and human-driven vehicles in mixed traffic environments, this study proposes a Stackelberg game-based decision-making method for autonomous vehicles. A Stackelberg game framework is established between autonomous vehicles (leaders) and target-lane human-driven vehicles (followers) in three typical scenarios. The method develops a utility function for human-driven vehicles incorporating driving styles and safety-comfort-efficiency factors, with a corresponding cost function for autonomous vehicles. An improved Stackelberg game model integrates trajectory prediction of human-driven vehicles, while a bi-level optimization algorithm combining model predictive control and genetic algorithms jointly optimizes acceleration sequences and lane-change timing. Simulations demonstrate 60% reduction in heading angle variation and 67.59% decrease in yaw rate compared to non-game strategies, confirming enhanced safety, comfort, and efficiency of the proposed method. |
|---|---|
| ISSN: | 2169-3536 |