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

To understand driving environments effectively, it is important to achieve accurate detection
and classification of objects detected by sensor-based intelligent vehicle systems, which are …

Object detection is the most critical and foundational sensing module for the autonomous
movement platform. However, most of the existing deep learning solutions are based on …

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 …

In order for autonomous vehicles to safely navigate the road ways, accurate object detection
must take place before safe path planning can occur. Currently, general purpose object …

Lidar and optical camera are common sensors in the sensor layer of autopilot system. Lidar
can use depth data to obtain accurate relative distance and contour information of obstacles …

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 …

To achieve better results on object detection for autonomous vehicle under complex outdoor
conditions, we attempt to integrated the sensor-fusion, hierarchical multi-view networks and …

Autonomous vehicles require accurate, and fast decision-making perception systems to
know the driving environment. The 2D object detection is critical in allowing the perception …

The use of LiDAR point clouds for accurate three-dimensional perception is crucial for
realizing high-level autonomous driving systems. Upon considering the drawbacks of the …