StyleFinger: Learning Vehicle-Level Interaction Tendencies for Controllable Scenario Generation in Autonomous Driving Testing

Scenario-based testing of autonomous vehicles requires surrounding agents that respond diversely yet realistically within the same traffic situation. Trajectory replay offers no behavioral diversity, while stochastic trajectory generators produce diversity without a stable, behavior-grounded control axis. We present StyleFinger, which learns a vehicle-level interaction tendency from non-overlapping segments of the same vehicle and exposes it as a continuous conditioning code for behavior generation. On NGSIM two-car interactions under a vehicle-disjoint, temporally non-overlapping, history-only protocol, same-vehicle retrieval of the learned tendency reaches an AUC of 0.754, indicating that the signal is persistent and recoverable. The tendency complements rather than replaces the scenario: intent prediction with scenario and style attains a macro-F1 of 0.678, versus 0.220 for style alone. Under an equal eight-candidate budget, style conditioning achieves 0.661 ground-truth behavior match and 0.893 observed behavior-bin coverage, versus 0.571 and 0.855 for stochastic latent sampling. Shuffling the scenario–style pairing collapses the intervention effect (1.542 vs. 0.315), indicating that the code carries behaviorally meaningful structure rather than acting as arbitrary noise. All evidence is open-loop; interactive closed-loop validation is left as future work.