Autonomous driving requires a comprehensive understanding of the surrounding environment for reliable trajectory planning. Previous works rely on dense rasterized scene representation (e.g., agent occupancy and semantic map) to perform planning, which is computationally intensive and misses the instance-level structure information. In this paper, we propose VAD, an end-to-end vectorized paradigm for autonomous driving, which models the driving scene as a fully vectorized representation. The proposed vectorized paradigm has two significant advantages. On one hand, VAD exploits the vectorized agent motion and map elements as explicit instance-level planning constraints which effectively improves planning safety. On the other hand, VAD runs much faster than previous end-to-end planning methods by getting rid of computation-intensive rasterized representation and hand-designed post-processing steps. VAD achieves state-of-the-art end-to-end planning performance on the nuScenes dataset, outperforming the previous best method by a large margin. Our base model, VAD-Base, greatly reduces the average collision rate by 29.0% and runs 2.5x faster. Besides, a lightweight variant, VAD-Tiny, greatly improves the inference speed (up to 9.3x) while achieving comparable planning performance. We believe the excellent performance and the high efficiency of VAD are critical for the real-world deployment of an autonomous driving system. Code and models are available at https://github.com/hustvl/VAD for facilitating future research.

翻译：自动驾驶需要对周围环境进行全面理解以实现可靠的轨迹规划。以往的方法依赖密集的栅格化场景表征（例如智能体占用网格与语义地图）进行规划，但这种方法计算密集且缺失实例级结构信息。本文提出VAD——一种面向自动驾驶的端到端矢量化范式，它将驾驶场景建模为完全矢量化表征。该矢量化范式具有两大显著优势：一方面，VAD利用矢量化智能体运动与地图元素作为显式的实例级规划约束，有效提升了规划安全性；另一方面，VAD通过摒弃计算密集的栅格化表征与人工设计的后处理步骤，运行速度远超以往的端到端规划方法。在nuScenes数据集上，VAD实现了当前最优的端到端规划性能，以大幅优势超越此前最佳方法。我们的基础模型VAD-Base将平均碰撞率降低29.0%，运行速度提升2.5倍。此外，轻量级变体VAD-Tiny在保持可比规划性能的同时，将推理速度大幅提升（最高达9.3倍）。我们认为VAD的卓越性能与高效率对于自动驾驶系统的实际部署至关重要。代码与模型已开源至https://github.com/hustvl/VAD，以促进未来研究。