Abstract
We present TinyBEV, a unified, camera only Bird's Eye View (BEV) framework that distills the full-stack capabilities of a large planning-oriented teacher (UniAD [19]) into a compact, real-time student model. Unlike prior efficient camera only baselines such as VAD[23] and VADv2[7], TinyBEV supports the complete autonomy stack 3D detection, HD-map segmentation, motion forecasting, occupancy prediction, and goal-directed planning within a streamlined 28M-parameter backbone, achieving a 78% reduction in parameters over UniAD [19]. Our model-agnostic, multi-stage distillation strategy combines feature-level, output-level, and adaptive region-aware supervision to effectively transfer high-capacity multi-modal knowledge to a lightweight BEV representation. On nuScenes[4], Tiny-BEV achieves 39.0 mAP for detection, 1.08 minADE for motion forecasting, and a 0.32 collision rate, while running 5x faster (11 FPS) and requiring only camera input. These results demonstrate that full-stack driving intelligence can be retained in resource-constrained settings, bridging the gap between large-scale, multi-modal perception-planning models and deployment-ready real-time autonomy.
Abstract (translated)
我们介绍了TinyBEV，这是一种统一的、仅基于相机的鸟瞰图（BEV）框架，它将大型规划导向教师模型（如UniAD [19]）的所有能力提炼到一个紧凑且实时的学生模型中。与之前的高效仅基于相机的基础模型（例如VAD[23]和VADv2[7]）不同，TinyBEV在一个精简的28M参数骨干网络中支持完整的自主驾驶栈，包括三维检测、高精度地图分割、运动预测、占用预测以及目标导向规划等功能，相比UniAD [19]实现了高达78%的参数减少。我们的模型无关、多阶段的知识蒸馏策略结合了特征级、输出级和自适应区域感知监督方法，能够有效地将大型模型中的高层次、多模态知识转移到一个轻量级的BEV表示中。 在nuScenes数据集[4]上，Tiny-BEV达到了39.0 mAP的检测精度，在运动预测任务上的minADE为1.08，并且碰撞率为0.32，同时运行速度提高了5倍（达到每秒11帧），仅需基于相机输入。这些结果表明，在资源受限的情况下也可以保留完整的自动驾驶智能，从而弥合了大规模、多模态感知规划模型与部署就绪的实时自主驾驶系统之间的差距。
URL
https://arxiv.org/abs/2509.18372
