Estimating the states of surrounding traffic participants stays at the core of autonomous driving. In this paper, we study a novel setting of this problem: model-free single object tracking (SOT), which takes the object state in the first frame as input, and jointly solves state estimation and tracking in subsequent frames. The main purpose for this new setting is to break the strong limitation of the popular "detection and tracking" scheme in multi-object tracking. Moreover, we notice that shape completion by overlaying the point clouds, which is a by-product of our proposed task, not only improves the performance of state estimation but also has numerous applications. As no benchmark for this task is available so far, we construct a new dataset LiDAR-SOT and corresponding evaluation protocols based on the Waymo Open dataset. We then propose an optimization-based algorithm called SOTracker based on point cloud registration, vehicle shapes, and motion priors. Our quantitative and qualitative results prove the effectiveness of our SOTracker and reveal the challenging cases for SOT in point clouds, including the sparsity of LiDAR data, abrupt motion variation, etc. Finally, we also explore how the proposed task and algorithm may benefit other autonomous driving applications, including simulating LiDAR scans, generating motion data, and annotating optical flow. The code and protocols for our benchmark and algorithm are available at https://github.com/TuSimple/LiDAR_SOT/ . A video demonstration is at https://www.youtube.com/watch?v=BpHixKs91i8 .
翻译:估计交通参与者周围交通参与者的状态是自主驾驶的核心。 在本文中,我们研究了这一问题的新背景:无模型的单一物体跟踪(SOT),它将第一个框架中的物体状态作为输入,并共同解决州估算和随后的跟踪。新环境的主要目的是打破多点跟踪中流行的“探测和跟踪”计划的巨大局限性。此外,我们注意到,通过覆盖点云(这是我们拟议任务的一个副产品)来构建完成点云,不仅改善了国家估算的性能,而且还有许多应用。由于迄今还没有关于这项任务的基准,我们根据Waymo Opt数据集, 共同设计了一个新的数据集LDAR-SOT和相应的评估协议。我们随后提议基于点云登记、车辆形状和运动前运动的SOTLackracker(S)。我们的定量和定性结果证明了我们SOT的效益, 包括LD-D数据、 突然运动-D(Trentalalal) 和A-Ralbal-S(LA) 数据流,我们还探索了我们提议的SAL-LAD(S)基准/LARCL) 的演示程序。