This paper proposes a multimodal fusion 3D target detection algorithm based on the attention mechanism to improve the performance of 3D target detection. The algorithm utilizes point cloud data and information from the camera. For image feature extraction, the ResNet50 + FPN architecture extracts features at four levels. Point cloud feature extraction employs the voxel method and FCN to extract point and voxel features. The fusion of image and point cloud features is achieved through regional point fusion and voxel fusion methods. After information fusion, the Coordinate and SimAM attention mechanisms extract fusion features at a deep level. The algorithm’s performance is evaluated using the DAIR-V2X dataset. The results show that compared to the Part-A2 algorithm; the proposed algorithm improves the mAP value by 7.9% in the BEV view and 7.8% in the 3D view at IOU = 0.5 (cars) and IOU = 0.25 (pedestrians and cyclists). At IOU = 0.7 (cars) and IOU = 0.5 (pedestrians and cyclists), the mAP value of the SECOND algorithm is improved by 5.4% in the BEV view and 4.3% in the 3D view, compared to other comparison algorithms.

In recent years, autonomous driving technology has been developing rapidly, but there is still a certain gap from commercial production, in which the safety issue is one of the important factors. In the working of most companies and colleges, the Traffic Jam Pilot (TJP), serving as a prototypical Level 3 autonomous driving function, has predominantly focused on functional implementation and enhancement, prioritizing functional completeness and user comfort. However, the aspect of functional safety in the system has received comparatively less attention. To guarantee the safety of the driver’s life and property, it is essential to consider the functional safety aspect regarding automobile operation after system function failure. In this paper, according to the functional safety development process and related regulations in ISO 26262, the functions and operation environment of the TJP system are defined in detail, and the longitudinal function is taken as an example to be developed following the functional safety process and verified by simulation. Simulation verification results show that the control algorithm is safe and reliable, and can ensure the safety and stability of the vehicle during operation. The research in this paper further explores the combination of functional safety and high-level automated driving function, providing some ideas for their practical application in industry.

This paper proposes a multimodal fusion 3D target detection algorithm based on the attention mechanism to improve the performance of 3D target detection. The algorithm utilizes point cloud data and information from camera. For image feature extraction, the ResNet50+FPN architecture extracts features at four levels. Point cloud feature extraction employs the voxel method and FCN to extract point and voxel features. The fusion of image and point cloud features is achieved through regional point fusion and regional voxel fusion methods. After information fusion, the Coordinate attention mechanism and SimAM attention mechanism extract fusion features at a deep level. The algorithm's performance is evaluated using the DAIR-V2X dataset. The results show that compared to the Part-A2 algorithm, the proposed algorithm improves the mAP value by 7.9% in BEV view and 7.8% in 3D view at IOU=0.5 (cars) and IOU=0.25 (pedestrians and cyclist). At IOU=0.7 (cars) and IOU=0.5 (pedestrians and cyclist), the mAP value of the SECOND algorithm is improved by 5.4% in the BEV view and 4.3% in the 3D view, compared to other comparison algorithms.

High-accuracy and high-efficiency target positioning method is crucial for the 3-D measurement performance of large-scale components. However, most existing methods typically involve the time-consuming process of identification and matching of target markers, which can lead to the measurement system failing to meet the real-time demands of the manufacturing industry. To solve this problem, we propose an end-to-end target monocular positioning method. Specifically, we first design a speckle texture spherical target to eliminate the necessity of identifying and matching target markers. Next, we propose a fast target positioning regression network to model the mapping relationship between the speckle texture information of the spherical target and its spatial position parameters, thereby ensuring the accuracy and efficiency of the target positioning. Experimental results demonstrate that our proposed method achieves a translation error of less than 0.15 mm and a rotation error of less than 0.17$^{\circ }$, with a visual positioning time of 0.02 s. In addition, our proposed method can achieve precise target positioning within a measurement range of 2 m. Furthermore, extensive experiments demonstrate the robustness and reliability of our proposed method under various conditions.