Both recognition and 3D tracking of frontal dynamic objects are crucial problems in an autonomous vehicle, while depth estimation as an essential issue becomes a challenging problem using a monocular camera. Since both camera and objects are moving, the issue can be formed as a structure from motion (SFM) problem. In this paper, to elicit features from an image, the YOLOv3 approach is utilized beside an OpenCV tracker. Subsequently, to obtain the lateral and longitudinal distances, a nonlinear SFM model is considered alongside a state-dependent Riccati equation (SDRE) filter and a newly developed observation model. Additionally, a switching method in the form of switching estimation error covariance is proposed to enhance the robust performance of the SDRE filter. The stability analysis of the presented filter is conducted on a class of discrete nonlinear systems. Furthermore, the ultimate bound of estimation error caused by model uncertainties is analytically obtained to investigate the switching significance. Simulations are reported to validate the performance of the switched SDRE filter. Finally, real-time experiments are performed through a multi-thread framework implemented on a Jetson TX2 board, while radar data is used for the evaluation.

正面动态物体的识别和 3D 跟踪都是自动驾驶汽车中的关键问题，而深度估计作为一个基本问题成为使用单目相机的具有挑战性的问题。由于相机和物体都在移动，因此该问题可以形成为运动结构 (SFM) 问题。在本文中，为了从图像中提取特征，在 OpenCV 跟踪器旁边使用了 YOLOv3 方法。随后，为了获得横向和纵向距离，将非线性 SFM 模型与状态相关的 Riccati 方程 (SDRE) 滤波器和新开发的观测模型一起考虑。此外，提出了一种以切换估计误差协方差形式的切换方法，以增强 SDRE 滤波器的鲁棒性能。所提出的滤波器的稳定性分析是在一类离散非线性系统上进行的。此外，分析获得了模型不确定性引起的估计误差的最终界限，以研究切换的重要性。报告模拟以验证切换 SDRE 滤波器的性能。最后，通过在 Jetson TX2 板上实现的多线程框架进行实时实验，同时使用雷达数据进行评估。