Cooperative perception in the field of connected autonomous vehicles (CAVs) aims to
overcome the inherent limitations of single-vehicle perception systems, including long-range …

Roadside perception is a fundamental task for vehicle-to-road cooperative perception and
traffic scheduling. However, most existing roadside perception strategies prefer to deploy …

The pursuit of autonomous driving relies on developing perception systems capable of
making accurate, robust, and rapid decisions to interpret the driving environment effectively …

Millimeter-wave (mmWave) radar is widely used in autonomous driving thanks to its
robustness under harsh weather conditions. However, compared to LiDAR point clouds …

Autonomous driving is a popular research area within the computer vision research
community. Since autonomous vehicles are highly safety-critical, ensuring robustness is …

Environment perception remains one of the key tasks in autonomous driving for which
solutions have yet to reach maturity. Multi-modal approaches benefit from the …

In the context of autonomous driving environment perception, multi-modal fusion plays a
pivotal role in enhancing robustness, completeness, and accuracy, thereby extending the …

3D object detection is a significant aspect of the perception module in autonomous driving;
however, with current technology, data sharing between vehicles and cloud servers for …

Collaborative perception aims for a holistic perceptive construction by leveraging
complementary information from nearby connected automated vehicle (CAV), thereby …

Accurate 3D object detection is vital for autonomous driving since it facilitates accurate
perception of the environment through multiple sensors. Although cameras can capture …