Occupancy and 3D object detection are characterized as two standard tasks in modern autonomous driving system. In order to deploy them on a series of edge chips with better precision and time-consuming trade-off, contemporary approaches either deploy standalone models for individual tasks, or design a multi-task paradigm with separate heads. However, they might suffer from deployment difficulties (i.e., 3D convolution, transformer and so on) or deficiencies in task coordination. Instead, we argue that a favorable framework should be devised in pursuit of ease deployment on diverse chips and high precision with little time-consuming. Oriented at this, we revisit the paradigm for interaction between 3D object detection and occupancy prediction, reformulate the model with 2D convolution and prioritize the tasks such that each contributes to other. Thus, we propose a method to achieve fast 3D object detection and occupancy prediction (UltimateDO), wherein the light occupancy prediction head in FlashOcc is married to 3D object detection network, with negligible additional timeconsuming of only 1.1ms while facilitating each other. We instantiate UltimateDO on the challenging nuScenes-series benchmarks.

本研究解决了传统占用预测与3D物体检测在自动驾驶系统中的独立部署和协调不足的问题。通过采用2D卷积方法，提出了一种名为UltimateDO的新框架，实现这两个任务的高效协同，仅增加了1.1毫秒的计算时间，显著提升了模型在不同芯片上的部署效率及精准度。该方法在复杂的nuScenes基准上进行了实例化测试，展现出良好的性能潜力。

UltimateDO：高效的占用预测与3D物体检测融合框架