Multi-object tracking (MOT) is an essential technique for navigation in
autonomous driving. In tracking-by-detection systems, biases, false positives,
and misses, which are referred to as outliers, are inevitable due to complex
traffic scenarios. Recent tracking methods are based on filtering algorithms
that overlook these outliers, leading to reduced tracking accuracy or even loss
of the objects trajectory. To handle this challenge, we adopt a probabilistic
perspective, regarding the generation of outliers as misspecification between
the actual distribution of measurement data and the nominal measurement model
used for filtering. We further demonstrate that, by designing a convolutional
operation, we can mitigate this misspecification. Incorporating this operation
into the widely used unscented Kalman filter (UKF) in commonly adopted tracking
algorithms, we derive a variant of the UKF that is robust to outliers, called
the convolutional UKF (ConvUKF). We show that ConvUKF maintains the Gaussian
conjugate property, thus allowing for real-time tracking. We also prove that
ConvUKF has a bounded tracking error in the presence of outliers, which implies
robust stability. The experimental results on the KITTI and nuScenes datasets
show improved accuracy compared to representative baseline algorithms for MOT
tasks.

多目标追踪是自主驾驶中的关键技术之一。本文提出了一种用于处理复杂交通场景中的异常值、提高追踪准确性的算法，称之为卷积 UKF。该算法在 KITTI 和 nuScenes 数据集上的实验结果表明，相对于基准算法，卷积 UKF 能够实现更高的追踪准确性。