W Zhang, H Shi, Y Zhao, Z Feng, R Lovreglio - Expert Systems with …, 2024 - Elsevier
In this paper, we propose a 3D object detection method called MMAF-Net that is based on the multi-view and multi-stage adaptive fusion of RGB images and LiDAR point cloud data …
Detecting small obstacles on the road is critical for autonomous driving. In this paper, we present a method to reliably detect such obstacles through a multi-modal framework of …
H Li, Y Chen, Q Zhang, D Zhao - IEEE transactions on …, 2021 - ieeexplore.ieee.org
Multisensor fusion-based road segmentation plays an important role in the intelligent driving system since it provides a drivable area. The existing mainstream fusion method is mainly to …
This work presents a probabilistic deep neural network that combines LiDAR point clouds and RGB camera images for robust, accurate 3D object detection. We explicitly model …
Autonomous driving requires accurate, robust, and fast decision-making perception systems to understand the driving environment. Object detection is critical in allowing the perception …
Y Zhu, R Xu, H An, C Tao, K Lu - Sensors, 2022 - mdpi.com
3D object detection methods based on camera and LiDAR fusion are susceptible to environmental noise. Due to the mismatch of physical characteristics of the two sensors, the …
T Kim, J Ghosh - Advances in Neural Information …, 2019 - proceedings.neurips.cc
Algorithms that fuse multiple input sources benefit from both complementary and shared information. Shared information may provide robustness against faulty or noisy inputs, which …
In this paper, we introduce a new deep learning architecture for camera and Lidar sensor fusion. The proposed scheme performs 2D object detection using the RGB camera image …