UniDrive-WM: Unified Understanding, Planning and Generation World Model For Autonomous Driving

World models have become central to autonomous driving,where accurate scene understanding and future prediction are crucialfor safe control. Recent work has explored using vision–language models(VLMs) for planning, yet existing approaches typically treat perception,prediction, and planning as separate modules. We propose UniDrive-WM, a unified VLM-based world model that jointly performs driving-scene understanding, trajectory planning, and trajectory-conditioned fu-ture image generation within a single architecture. UniDrive-WM’s tra-jectory planner predicts a future trajectory, which conditions a VLM-based image generator to produce plausible future frames. These predic-tions provide additional supervisory signals that enhance scene under-standing and iteratively refine trajectory generation. We further comparediscrete and continuous output representations for future image predic-tion, analyzing their influence on downstream driving performance. Ex-periments on the challenging Bench2Drive benchmark show that UniDrive-WM produces high-fidelity future images and improves planning perfor-mance by 7.3% in L2 trajectory error and 10.4% in collision rate overthe previous best method. These results demonstrate the advantagesof tightly integrating VLM-driven reasoning, planning, and generativeworld modeling for autonomous driving. The project page is available athttps://unidrive-wm.github.io/UniDrive-WM.