STT：用于自动驾驶的Transformer进行状态跟踪

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

在自动驾驶中，跟踪三维空间中的物体至关重要。为了在驾驶过程中确保安全，跟踪器必须能够可靠地跨帧跟踪物体，并准确估计它们的状态，如速度和加速度。现有研究经常侧重于关联任务，而忽视模型在状态估计上的性能，或者采用复杂的启发式方法来预测状态。在本文中，我们提出了一种使用Transformer构建的具有状态跟踪功能的模型STT，它可以在场景中持续跟踪物体，并准确预测它们的状态。STT通过长期检测历史消耗丰富的外观、几何和运动信号，并同时针对数据关联和状态估计任务进行联合优化。由于标准跟踪指标如MOTA和MOTP无法捕捉两个任务在更广泛的物体状态范围内的综合性能，我们使用新的指标S-MOTA和MOTPS来扩展它们，以解决这一局限性。STT在Waymo开放数据集上实现了具有竞争力的实时性能。

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.