Autonomous Vehicle Planning With Tree Search and Agent Prediction

The planning system is segmented into distinct functional modules: a tree search algorithm for exploring maneuver sequences, a generative behavior model for predicting external agent responses, and an inverse planner for inferring agent goals. This modular segmentation allows each component to specialize in a specific aspect of the planning task, improving overall reliability while managing complexity through clear separation of concerns.

The generative behavior model performs preliminary action by pre-computing and caching predicted responses of external agents to various maneuvers before the actual planning decision is made. This preliminary computation of agent behaviors allows the tree search algorithm to evaluate maneuver sequences more efficiently, reducing the computational burden during real-time planning while maintaining high reliability.

The inverse planner changes parameters by inferring high-level goal parameters (such as destination locations, maneuver intentions, and behavior patterns) from low-level observational data. Instead of directly using raw trajectory data for prediction, the system transforms the data into meaningful goal parameters that characterize agent behavior, thereby improving prediction accuracy while reducing the effective data volume needed for training.

The generative behavior model creates simplified copies or representations of complex real-world driving behaviors by learning goal distributions from training data. Rather than storing and processing all raw driving data, the system creates compact probabilistic models (goal distributions) that capture essential behavioral patterns, achieving high prediction accuracy with reduced data storage and processing requirements.

The inverse planner applies partial action by computing goal likelihoods for only the most relevant goals rather than exhaustively evaluating all possible goals. The system prioritizes computing probabilities for goals that are most likely based on observed behavior patterns, obtaining sufficient information about agent intent without the excessive computational time required for complete goal space enumeration.

The system uses feedback mechanisms where the tree search algorithm receives goal likelihood information from the inverse planner to guide its exploration of maneuver sequences. This feedback allows the planner to focus computational resources on promising maneuver sequences that are consistent with inferred agent goals, reducing overall computation time while maintaining accurate understanding of agent intent.