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.

通过可微分的多传感器卡尔曼滤波器，本文提出了一种新颖的用于自动驾驶的 3D 多目标合作追踪算法，该算法学习估计每个检测的测量不确定性，以更好地利用卡尔曼滤波器追踪方法的理论属性，实验结果表明，与 V2V4Real 中的最新方法相比，我们的算法提高了 17% 的追踪准确性，且仅有 0.037 倍的通信成本。