Automated vehicles represent an exciting advancement in transportation, offering a range of benefits that have the potential to revolutionize how we travel. They can improve safety, efficiency, accessibility, and sustainability, holding promise for transforming our cities and communities. However, generating safe, comfortable, and efficient motion plans, especially in interactive scenarios like forced lane merging, remains a significant challenge in the field.

Lane merging is a pivotal skill for automated vehicles, as it frequently involves changing lanes to reach a destination. For instance, when approaching an intersection, a vehicle might need to merge into a specific lane beforehand to execute a turn later. Traditional pipelines in automated driving typically decouple prediction and planning. They assume perfect upstream prediction and generate robust motion plans to avoid collisions with multi-modal predictions. However, in dense traffic conditions, conservative planning might hinder the ego vehicle from merging effectively, resulting in it becoming stuck. This underscores the necessity of combining prediction and planning, a concept we term interaction-aware planning algorithms.

The first major contribution of this thesis is an efficient game-theoretic behavior planner that captures interactions under different behavior modes. In this approach, we represent the behaviors of the vehicles as actions in a matrix game and select the Nash equilibrium to capture their mutual influence. To generate the cost of the action pairs, we model the merging process as a gap selection process and evaluate the trajectories generated by interactive models. The effectiveness of the proposed planner is validated in the high-fidelity CARLA simulator.

In the real world, human drivers may not always adhere rationally to the equilibrium of a game model. They could choose a behavior mode different from the game theory solution. Therefore, it might be more beneficial to consider different motion modes simultaneously, rather than favoring the "most likely" one while neglecting the others.

For this purpose, we also explore the usage of Branch Model Predictive Control (B-MPC) in this thesis. By predicting the motion of the surrounding vehicle as a scenario tree, the B-MPC approach can generate a trajectory tree as a motion plan. By executing only the root node, the ego vehicle can consider different future scenarios simultaneously and plan contingency motions. We further extend the B-MPC approach by incorporating interactive policies, using different solving schemes, and including collision avoidance constraints that consider the orientations of the vehicles. The effectiveness of these proposed methods was validated through experiments conducted in a handcrafted lightweight simulator.

Overall, this thesis focuses on developing interaction-aware planning methods to facilitate safe, successful, and comfortable lane merging scenarios. Nevertheless, the major limitations lie in the modeling error and the potential long-tail issues. To address these challenges and further improve motion planning, future work could explore data-driven (learning-based) approaches that leverage real-world driver behavior to generate more informed and adaptable motion plans.