3D multi-object tracking is a key module in autonomous driving applications that provides a reliable dynamic representation of the world to the planning module. In this paper, we present our on-line tracking method, which made the first place in the NuScenes Tracking Challenge, held at the AI Driving Olympics Workshop at NeurIPS 2019. Our method estimates the object states by adopting a Kalman Filter. We initialize the state covariance as well as the process and observation noise covariance with statistics from the training set. We also use the stochastic information from the Kalman Filter in the data association step by measuring the Mahalanobis distance between the predicted object states and current object detections. Our experimental results on the NuScenes validation and test set show that our method outperforms the AB3DMOT baseline method by a large margin in the Average Multi-Object Tracking Accuracy (AMOTA) metric.

中文翻译：

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用于自动驾驶的概率 3D 多目标跟踪

3D 多目标跟踪是自动驾驶应用中的一个关键模块，可为规划模块提供可靠的世界动态表示。在本文中，我们展示了我们的在线跟踪方法，该方法在 NeurIPS 2019 的 AI 驾驶奥林匹克研讨会上举办的 NuScenes 跟踪挑战赛中获得第一名。我们的方法通过采用卡尔曼滤波器来估计对象状态。我们用来自训练集的统计数据初始化状态协方差以及过程和观察噪声协方差。我们还在数据关联步骤中使用来自卡尔曼滤波器的随机信息，通过测量预测对象状态和当前对象检测之间的马氏距离。