Recent years have seen significant advances in world models, which primarily focus on learning fine-grained correlations between an agent's motion trajectory and the resulting changes in its surrounding environment. However, existing methods often struggle to capture such fine-grained correlations and achieve real-time predictions. To address this, we propose a new 4D occupancy world model for autonomous driving, termed T$^3$Former. T$^3$Former begins by pre-training a compact triplane representation that efficiently compresses the 3D semantically occupied environment. Next, T$^3$Former extracts multi-scale temporal motion features from the historical triplane and employs an autoregressive approach to iteratively predict the next triplane changes. Finally, T$^3$Former combines the triplane changes with the previous ones to decode them into future occupancy results and ego-motion trajectories. Experimental results demonstrate the superiority of T$^3$Former, achieving 1.44$\times$ faster inference speed (26 FPS), while improving the mean IoU to 36.09 and reducing the mean absolute planning error to 1.0 meters.

本研究解决了现有世界模型在捕捉智能体运动轨迹与周围环境变化之间的细粒度关联和实时预测能力不足的问题。我们提出了 T$^3$Former，这是一种新型的4D占用世界模型，通过高效压缩3D语义占用环境的三平面表示并提取多尺度时间运动特征，实现了更快的推理速度和更准确的空间规划。实验结果表明，T$^3$Former 的推理速度提升至26帧每秒，平均交并比提升至36.09，绝对规划误差降低至1.0米。

时间三平面变换器作为占用世界模型