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Game-Theory-Lane-Changing
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Game-Theory-Lane-Changing
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# Game-Theory-Lane-Changing - Implement two-player game theory lane chaning with python, and visulaize with MATLAB Automated Driving Toolbox. - The predinfined routes are extracted from NGSIM dataset, and refer to [Multi-Player Dynamic Game-Based Automatic Lane-Changing Decision Model under Mixed Autonomous Vehicle and Human-Driven Vehicle Environment](https://journals.sagepub.com/doi/full/10.1177/0361198120940990). - The game theory model and payoff functions refer to [Modeling Lane-Changing Behavior in a Connected Environment: A Game Theory Approach](https://www.sciencedirect.com/science/article/pii/S2352146515000903). ## Usage 1. Genrate game theoretic simulation trajectory file. ``` # 'trajectory_post.csv' contains predefined routes of vehicles, and are extracted from NGSIM dataset # use simulation.py to simulate the trajectory of vehicles # where the target vehicle interacted with the lag vehicle with the predifined two-player game when performing lane channging # output 'trajectory_A.csv' for further visualization using MATLAB python simulation.py ``` 2. Visualize the trajectory file. ``` compile and run simulation_traj.m ``` 3. Analyze the lane changing trajectory ``` compile and run plot_3d.m, plot_traj.m ``` ## Simulation Result 1. In ```DrivingScenario``` directory, there are driving scenarios files that can be visualized by MATLAB automated driving toolbox. - ```rule_based.mat```: Vehicle A based on rule-based lane changing, and fails because of safety distance - ```data_based.mat```: The visualization of real world data from NGSIM data, vehicle A fails to change lane. - ```game_theory.mat```: Vehicle A succeed to change lane via game with Vehicle B, while considering safety distance. 2. In ```video``` directory, there are videos of the visualization of the three driving scenario files.
