Autonomous driving needs to rely on high-quality 3D object detection to ensure safe navigation in the world. Uncertainty estimation is an effective tool to provide statistically accurate predictions, while the associated detection uncertainty can be used to implement a more safe navigation protocol or include the user in the loop. In this paper, we propose a Variational Neural Network-based TANet 3D object detector to generate 3D object detections with uncertainty and introduce these detections to an uncertainty-aware AB3DMOT tracker. This is done by applying a linear transformation to the estimated uncertainty matrix, which is subsequently used as a measurement noise for the adopted Kalman filter. We implement two ways to estimate output uncertainty, i.e., internally, by computing the variance of the CNN outputs and then propagating the uncertainty through the post-processing, and externally, by associating the final predictions of different samples and computing the covariance of each predicted box. In experiments, we show that the external uncertainty estimation leads to better results, outperforming both internal uncertainty estimation and classical tracking approaches. Furthermore, we propose a method to initialize the Variational 3D object detector with a pretrained TANet model, which leads to the best performing models.

翻译：自动驾驶需依赖高质量的三维目标检测以确保在环境中的安全导航。不确定性估计是一种提供统计准确预测的有效工具，而相关的检测不确定性可用于实现更安全的导航协议或将用户纳入决策回路。本文提出一种基于变分神经网络的TANet三维目标检测器，用于生成带不确定性的三维目标检测结果，并将这些检测结果引入至不确定性感知的AB3DMOT跟踪器中。具体通过将估计的不确定性矩阵进行线性变换实现，变换后的矩阵随后作为所采用卡尔曼滤波器的测量噪声。我们实现了两种估计输出不确定性的方法：其一为内部估计，通过计算卷积神经网络输出的方差并将不确定性传播至后处理过程；其二为外部估计，通过关联不同样本的最终预测结果并计算每个预测边界框的协方差。实验表明，外部不确定性估计能取得更优结果，其性能既优于内部不确定性估计，也超越经典跟踪方法。此外，我们提出一种使用预训练TANet模型初始化变分三维目标检测器的方法，该方法可得到性能最优的模型。