Agent-Based Modeling (ABM) is a style of modelling to investigate and predict the emergence of complex group behaviors through simulating the actions and interactions of a large among of autonomous agents in given scenarios. ABM has been extensively used in the academia, e.g., game theory, complex system and computational sociology. With respect to autonomous driving, ABM is also a popular simulation approach, e.g., in developing driving policies and safety verification. The conventional motion planning and control methods, e.g., PID control, feedback linearization or model predictive control, expect a prediction over the future trajectories of other traffic participants in order to avoid collisions, but real traffic scenarios involve complex interactions among various road users (Schwarting, Planning and Decision-Making for Autonomous Vehicles, MIT, 2018). To conquer this challenge, the emerging trends are the behavior-aware motion planning and learning-based approaches. ABM is then could be naturally exploited to handle the complex, cluttered environments while modeling the uncertain interactions with each other. This would expend Simulink as an integrated platform to develop and verify learning-based algorithms. For a description, you can see this video: https://www.youtube.com/watch?v=nKOleGGEwJI