论文标题
用于自动驾驶的概率3D多对象跟踪
Probabilistic 3D Multi-Object Tracking for Autonomous Driving
论文作者
论文摘要
3D多对象跟踪是自动驾驶应用程序中的关键模块，它为规划模块提供了可靠的世界动态表示。在本文中，我们介绍了我们的在线跟踪方法，该方法在Neurips 2019的AI驾驶奥运会研讨会上举行的Nuscenes跟踪挑战中排名第一。我们的方法通过采用Kalman过滤器来估算对象状态。我们初始化了状态协方差以及训练集的统计数据的过程和观察噪声协方差。我们还通过测量预测的对象状态和当前对象检测之间的马哈拉诺菜距离，在数据关联步骤中使用来自Kalman滤波器的随机信息。我们对Nuscenes验证和测试集的实验结果表明，我们的方法的表现优于AB3DMOT基线方法，其平均多对象跟踪准确性（AMOTA）度量度很大。
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.
