Autonomous Vehicle Maneuver Planning Using Game Tree Prediction

The planning problem is segmented into a tree structure where the root node represents the current driving scenario, child nodes represent possible maneuvers, and subsequent nodes represent anticipated states after those maneuvers. This segmentation allows the system to systematically explore different maneuver sequences while managing computational complexity through hierarchical decomposition of the decision space.

The system performs preliminary action by simulating external agent behaviors using a generative behavior model before finalizing maneuver decisions. The generative model predicts how other agents will respond to potential ego vehicle maneuvers, allowing the planner to evaluate anticipated driving scenarios in advance and select safe maneuvers before execution.

The generative behavior model performs preliminary simulations of external agent responses to different ego vehicle maneuvers. By predicting how other agents will behave in advance, the system can evaluate multiple maneuver sequences more efficiently without requiring exhaustive real-time simulation of all possible outcomes, thus reducing planning time while maintaining safety.

The tree search algorithm incorporates feedback from the generative behavior model at each node, where the predicted responses of external agents inform the evaluation of maneuver sequences. This feedback mechanism allows the system to prune less promising branches and focus computational resources on evaluating maneuvers that are more likely to lead to safe outcomes, balancing exploration with time constraints.

The generative behavior model processes external agent behavior predictions in a segmented manner, evaluating responses for each potential ego vehicle maneuver separately as the tree search progresses. This avoids computing all possible agent responses simultaneously, reducing peak computational energy consumption while maintaining prediction accuracy for the maneuvers being considered.

The system applies partial action by using the generative behavior model only for the specific maneuvers being evaluated in the current planning context, rather than pre-computing all possible agent responses for all potential scenarios. This selective application of the computationally intensive model reduces overall energy consumption while maintaining accuracy for relevant predictions.