RGB cameras and LiDAR are crucial sensors for autonomous vehicles that provide
complementary information for accurate detection. Recent early-level fusion-based …

Vehicle detection is one of the most important environment perception tasks for autonomous
vehicles. The traditional vision‐based vehicle detection methods are not accurate enough …

Detection based on LiDAR and camera fusion is increasingly popular for researchers in the
autonomous driving domain. Compared to the camera-only and LiDAR-only methods, the …

LIDAR point cloud and RGB images are often used for object detection in autonomous
driving scenarios. This paper develops a multi-view version of Frustum PointNet (F …

We propose a new method for fusing a LIDAR point cloud and camera-captured images in
the deep convolutional neural network (CNN). The proposed method constructs a new layer …

The self-diving vehicle is a critical revolution in the automobile industry, transportation and
people's daily life. Generally, self-driving vehicles utilize the LIDAR, IMU, radar along with …

Nowadays, the deep learning for object detection has become more popular and is widely
adopted in many fields. This paper focuses on the research of LiDAR and camera sensor …

This paper aims to detect objects from BEV (Bird-Eye View) images by utilizing multi-source
data from three-dimensional light detection and ranging (3D LiDAR) and a camera. Object …

With the improvements in sensor performance (cameras, Lidars) and the application of deep
learning in object detection, autonomous vehicles (AVs) are gradually becoming more …

In recent times, Autonomous driving functionalities is being developed by car manufacturers
and is revolutionizing the automotive industries. Hybrid cars are prepared with a wide range …