PersonaDrive: Controllable Trajectory Prediction with Multi-Dimensional Driving Personas

Although recent trajectory prediction and end-to-end au-tonomous driving methods improve robustness in urban environments,they still lack meaningful controllability. Existing benchmarks either pro-vide no persona-conditioned annotations or support only a single urgencyspectrum (i.e., emergency, normal, relaxed), which cannot distinguishpersonas that share the same urgency level but require different drivingdynamics. To address this, we propose (i) the Persona-Conditioned Tra-jectory (PCT) dataset, which decomposes driving personas along twoaxes—Temporal Urgency and Ride Comfort—and combines three levelsof each to form a grid of nine personas, each paired with natural-languagedescriptions and trajectories, and (ii) PersonaDrive, a framework thatcan learn driving personas from language and can generate persona-specific trajectories. PersonaDrive incorporates Persona-Conditioned An-chor Transform (PCAT), which hierarchically reshapes anchors alongboth axes, and Persona-Conditioned Multi-Modal Fusion (PCMF) forBEV-level persona fusion. Training is supervised by a Hierarchical GuideLoss enforcing axis-aligned physical orderings and an Axis-DecomposedDiversity Loss preventing diagonal mode collapse. Experimental resultsshow that PersonaDrive consistently improves over the compared base-lines across multi-dimensional scenarios. The code and PCT dataset areavailable at https://github.com/VisualAIKHU/PersonaDrive.