Driven by deep learning techniques, perception technology in autonomous driving has
developed rapidly in recent years, enabling vehicles to accurately detect and interpret …

Due to the rising concept of advanced air mobility (AAM), electric vertical take-off and
landing (eVTOL) aircraft has become the hotspot for academic research and commercial …

Millimeter-wave radar and machine vision are both important means for intelligent vehicles
to perceive the surrounding environment. Aiming at the problem of multi-sensor fusion, this …

We survey the current state of millimeter-wave (mmWave) radar applications in robotics with
a focus on unique capabilities, and discuss future opportunities based on the state of the art …

Automotive traffic scenes are complex due to the variety of possible scenarios, objects, and
weather conditions that need to be handled. In contrast to more constrained environments …

Reliable detection and tracking of surrounding objects are indispensable for comprehensive
motion prediction and planning of autonomous vehicles. Due to the limitations of individual …

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

One of the main paths towards the reduction of traffic accidents is the increase in vehicle
safety through driver assistance systems or even systems with a complete level of autonomy …

The majority of uncertainty quantification methods for deep object detectors are based on
the network output, such as sampling strategies like Monte-Carlo dropout or deep …

To improve the vehicle recognition technology in automatic driving, an improved faster
region convolutional neural network (Faster RCNN) method is proposed. Firstly, a basic …