FlowPlan: Flow-Matching Trajectory Planning Guided by Counterfactual Interaction Consequences

Modeling inter-agent interactions and their evolution in complex traffic scenarios remains a fundamental challenge in autonomous driving planning. Despite advances in ego-conditioned prediction and joint prediction-planning, it remains difficult to reliably characterize how changes in ego behavior affect critical conflicts and to select effective interaction responses based on these effects. To address this problem, we propose FlowPlan, a flow-matching trajectory planning framework guided by counterfactual interaction consequences. Its Counterfactual Interaction Consequence Model (CF-ICM) evaluates complete ego-trajectory queries under the same observed scene and predicts object- and time-resolved proximity and contact outcomes. Coupled lateral-longitudinal probes use these predictions to construct branch-specific consequence memories and fixed strategy conditions that guide learned corrections to the flow velocity field. By reevaluating selected intermediate trajectory estimates, FlowPlan updates these memories as generation proceeds while retaining a fixed strategy condition within each branch. Completed candidates are reevaluated and selected using predicted interaction consequences, feasibility, and driving quality. Experiments on the large-scale real-world nuPlan benchmark demonstrate strong closed-loop planning performance in both non-reactive and reactive settings. We further introduce nuPlan-CIP (nuPlan Counterfactual Interaction Perturbation Benchmark), which applies controlled perturbations of varying interaction intensity while preserving the underlying scene context. Results on nuPlan-CIP show consistent responses to controlled interaction perturbations, complementing conventional planning metrics with a diagnostic assessment of interaction-dependent behavior.