In this paper, a multi-modal 360° framework for 3D object detection and tracking for
autonomous vehicles is presented. The process is divided into four main stages. First …

This paper presents two new approaches to detect surrounding vehicles in 3D urban driving
scenes and their corresponding Bird's Eye View (BEV). The proposals integrate two state-of …

Fueled by substantial advancements in deep learning, the domain of autonomous driving is
swiftly advancing towards more robust and effective intelligent systems. One of the critical …

Object detection in real-world applications such as autonomous driving scenarios is a
challenging issue since objects often occlude each other. 3D object detection has achieved …

Thanks to the major advancements in hardware and computational power, sensor
technology, and artificial intelligence, the race for fully autonomous driving systems is …

3D object detection plays a fundamental role in enabling driving automation, which is
regarded as a significant leap forward for contemporary transportation systems from the …

3D vehicle detection based on point cloud is a challenging task in real-world applications
such as autonomous driving. Despite significant progress has been made, we observe two …

Understanding driving situations regardless the conditions of the traffic scene is a
cornerstone on the path towards autonomous vehicles; however, despite common sensor …

Fusing the raw data from different automotive sensors for real-world environment perception
is still challenging due to their different representations and data formats. In this work, we …

Learned pointcloud representations do not generalize well with an increase in distance to
the sensor. For example, at a range greater than 60 meters, the sparsity of lidar pointclouds …