STT：具有Transformer的狀態追蹤技術用於自動駕駛

STT: Stateful Tracking with Transformers for Autonomous Driving

April 30, 2024

作者: Longlong Jing, Ruichi Yu, Xu Chen, Zhengli Zhao, Shiwei Sheng, Colin Graber, Qi Chen, Qinru Li, Shangxuan Wu, Han Deng, Sangjin Lee, Chris Sweeney, Qiurui He, Wei-Chih Hung, Tong He, Xingyi Zhou, Farshid Moussavi, Zijian Guo, Yin Zhou, Mingxing Tan, Weilong Yang, Congcong Li

cs.AI

摘要

在自主駕駛中，追蹤三維空間中的物體至關重要。為了確保行駛安全，追蹤器必須能夠可靠地跨幀追蹤物體，並準確估計它們的狀態，如當前的速度和加速度。現有的研究常常專注於關聯任務，而忽略了模型在狀態估計上的表現，或者部署複雜的啟發式方法來預測狀態。在本文中，我們提出了一種使用Transformer構建的具有狀態追蹤模型STT，該模型可以在場景中持續追蹤物體，同時準確預測它們的狀態。STT通過長期檢測歷史消耗豐富的外觀、幾何和運動信號，並且為數據關聯和狀態估計任務進行聯合優化。由於標準的追蹤指標如MOTA和MOTP無法捕捉兩個任務在更廣泛的物體狀態光譜中的綜合表現，我們通過新的指標S-MOTA和MOTPS來擴展它們，以解決這一限制。STT在Waymo Open Dataset上實現了具有競爭力的實時性能。

English

Tracking objects in three-dimensional space is critical for autonomous driving. To ensure safety while driving, the tracker must be able to reliably track objects across frames and accurately estimate their states such as velocity and acceleration in the present. Existing works frequently focus on the association task while either neglecting the model performance on state estimation or deploying complex heuristics to predict the states. In this paper, we propose STT, a Stateful Tracking model built with Transformers, that can consistently track objects in the scenes while also predicting their states accurately. STT consumes rich appearance, geometry, and motion signals through long term history of detections and is jointly optimized for both data association and state estimation tasks. Since the standard tracking metrics like MOTA and MOTP do not capture the combined performance of the two tasks in the wider spectrum of object states, we extend them with new metrics called S-MOTA and MOTPS that address this limitation. STT achieves competitive real-time performance on the Waymo Open Dataset.