Autonomous driving, in recent years, has been receiving increasing attention for its potential
to relieve drivers' burdens and improve the safety of driving. In modern autonomous driving …

Recent advancements in perception for autonomous driving are driven by deep learning. In
order to achieve robust and accurate scene understanding, autonomous vehicles are …

Fusing the camera and LiDAR information has become a de-facto standard for 3D object
detection tasks. Current methods rely on point clouds from the LiDAR sensor as queries to …

Sensor fusion is an essential topic in many perception systems, such as autonomous driving
and robotics. Existing multi-modal 3D detection models usually involve customized designs …

The perception system in autonomous vehicles is respon-sible for detecting and tracking the
surrounding objects. This is usually done by taking advantage of several sens-ing modalities …

With recent developments, the performance of automotive radar has improved significantly.
The next generation of 4D radar can achieve imaging capability in the form of high …

A growing interest in autonomous driving (AD) and intelligent vehicles (IVs) is fueled by their
promise for enhanced safety, efficiency, and economic benefits. While previous surveys …

Vehicle detection with visual sensors like lidar and camera is one of the critical functions
enabling autonomous driving. While they generate fine-grained point clouds or high …

Object detection in camera images, using deep learning has been proven successfully in
recent years. Rising detection rates and computationally efficient network structures are …

Robust and efficient vehicle detection is an important task of environment perception of
intelligent vehicles, which directly affects the behavior decision-making and motion planning …