针对自动驾驶3D感知的延迟问题，提出实时感知新框架。
Towards Latency-Aware 3D Streaming Perception for Autonomous Driving
- 通过连续历史特征融合缓解延迟影响
- 利用预测轨迹补偿检测结果，实测在线性能达离线80%
- 适合边缘设备部署，无需加速技术
现有3D感知算法虽性能优异，但在边缘设备部署时仍面临显著运行延迟挑战。本文提出面向在线评估的新型基准，并基于此构建延迟感知的3D流式感知框架（LASP）。该框架包含两个核心组件：1）延迟感知的历史特征融合，将查询传播扩展为连续过程，确保在不同延迟条件下仍能整合历史特征；2）延迟感知的预测检测模块，通过预测轨迹和后验延迟信息补偿检测结果。引入延迟感知机制后，方法在多种延迟水平下均具泛化能力，在Jetson AGX Orin上实现无加速技术下的在线性能接近离线评估的80%。
原文摘要 · Abstract (English)
Although existing 3D perception algorithms have demonstrated significant improvements in performance, their deployment on edge devices continues to encounter critical challenges due to substantial runtime latency. We propose a new benchmark tailored for online evaluation by considering runtime latency. Based on the benchmark, we build a Latency-Aware 3D Streaming Perception (LASP) framework that addresses the latency issue through two primary components: 1) latency-aware history integration, which extends query propagation into a continuous process, ensuring the integration of historical feature regardless of varying latency; 2) latency-aware predictive detection, a module that compensates the detection results with the predicted trajectory and the posterior accessed latency. By incorporating the latency-aware mechanism, our method shows generalization across various latency levels, achieving an online performance that closely aligns with 80\% of its offline evaluation on the Jetson AGX Orin without any acceleration techniques.
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