Current state-of-the-art autonomous driving vehicles mainly rely on each individual sensor system to perform perception tasks. Such a framework's reliability could be limited by occlusion or sensor failure. To address this issue, more recent research proposes using vehicle-to-vehicle (V2V) communication to share perception information with others. However, most relevant works focus only on cooperative detection and leave cooperative tracking an underexplored research field. A few recent datasets, such as V2V4Real, provide 3D multi-object cooperative tracking benchmarks. However, their proposed methods mainly use cooperative detection results as input to a standard single-sensor Kalman Filter-based tracking algorithm. In their approach, the measurement uncertainty of different sensors from different connected autonomous vehicles (CAVs) may not be properly estimated to utilize the theoretical optimality property of Kalman Filter-based tracking algorithms. In this paper, we propose a novel 3D multi-object cooperative tracking algorithm for autonomous driving via a differentiable multi-sensor Kalman Filter. Our algorithm learns to estimate measurement uncertainty for each detection that can better utilize the theoretical property of Kalman Filter-based tracking methods. The experiment results show that our algorithm improves the tracking accuracy by 17% with only 0.037x communication costs compared with the state-of-the-art method in V2V4Real. Our code and videos are available at https://github.com/eddyhkchiu/DMSTrack/ and https://eddyhkchiu.github.io/dmstrack.github.io/ .

翻译：当前最先进的自动驾驶车辆主要依赖各独立传感器系统执行感知任务。此类框架的可靠性可能受限于遮挡或传感器故障。为解决该问题，近期研究提出利用车-车(V2V)通信与其他车辆共享感知信息。然而，大多数相关研究仅聚焦于协同检测，协同跟踪仍属于探索不足的研究领域。诸如V2V4Real等少数近期数据集提供了三维多目标协同跟踪基准，但其提出的方法主要将协同检测结果作为标准单传感器卡尔曼滤波跟踪算法的输入。在此方法中，来自不同网联自动驾驶车辆(CAV)的不同传感器的测量不确定性可能无法被准确估计，从而难以利用卡尔曼滤波跟踪算法的最优性理论特性。本文提出一种基于可微分多传感器卡尔曼滤波的自动驾驶三维多目标协同跟踪算法。该算法通过学习估计每个检测结果的测量不确定性，能够更充分地利用卡尔曼滤波跟踪方法的理论特性。实验结果表明，与V2V4Real中的最先进方法相比，本算法仅需0.037倍的通信成本即可将跟踪精度提升17%。我们的代码及视频已发布于https://github.com/eddyhkchiu/DMSTrack/ 和https://eddyhkchiu.github.io/dmstrack.github.io/。