Fig 1 - uploaded by Sridevi Muthukumarasamy
Content may be subject to copyright.
Context in source publication
Context 1
... modeling and creates trajectories for the autonomous car to navigate the identified objects. 3D OD predicts bounding boxes that surround all objects of an image in 3D space, i.e., the bounding box gives information about the height, width, and depth of all the objects with the camera sensor as an origin. The process of monocular 3D OD is shown in Fig. 1. A light detection and ranging (LiDAR) sensor mounted on an autonomous driving car keeps rotating continuously and sends thousands of infrared light pulses every second. The time taken for the light to bounce off after colliding with the surrounding objects and reflected back to the sensor is measured. A 3D-point cloud map is created ...
Similar publications
Lane detection technology is mainly studied in the traditional method at first, but this method is not suitable for practice. Because in some unfavorable conditions usually do not have the required robustness of autonomous driving systems. Later, Deep learning-based techniques that could increase recognition speed and accuracy were put forth. Howev...
In recent years, convolutional neural networks (CNNs) are used in a large number of tasks in computer vision. One of them is object detection for autonomous driving. Although CNNs are used widely in many areas, what happens inside the network is still unexplained on many levels. Our goal is to determine the effect of Intrinsic dimension (i.e. minim...
In the field of autonomous driving, accurate and comprehensive perception of the 3D environment is crucial. Bird's Eye View (BEV) based methods have emerged as a promising solution for 3D object detection using multi-view images as input. However, existing 3D object detection methods often ignore the physical context in the environment, such as sid...
According to the fact that single‐modal data usually contain limited information, a great deal of effort has been devoted to making use of the complementary information contained in the multi‐modal data on various patterns. Thus, this paper is concerned with an object detection method that can fully utilize multi‐modal data. First, the method intro...
Multi-modal fusion has shown initial promising results for object detection of autonomous driving perception. However, many existing fusion schemes do not consider the quality of each fusion input and may suffer from adverse conditions on one or more sensors. While predictive uncertainty has been applied to characterize single-modal object detectio...
