Autonomous driving has achieved significant milestones in research and development over
the last two decades. There is increasing interest in the field as the deployment of …

The autonomous driving community has witnessed a rapid growth in approaches that
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …

Multimodallarge language models (MLLMs) have emerged as a prominent area of interest
within the research community, given their proficiency in handling and reasoning with non …

Many existing autonomous driving paradigms involve a multi-stage discrete pipeline of
tasks. To better predict the control signals and enhance user safety, an end-to-end approach …

Building 3D perception systems for autonomous vehicles that do not rely on high-density
LiDAR is a critical research problem because of the expense of LiDAR systems compared to …

Vision-centric joint perception and prediction (PnP) has become an emerging trend in
autonomous driving research. It predicts the future states of the traffic participants in the …

End-to-end autonomous driving has great potential in the transportation industry. However,
the lack of transparency and interpretability of the automatic decision-making process …

Artificial Intelligence (AI) shows promising applications for the perception and planning tasks
in autonomous driving (AD) due to its superior performance compared to conventional …

Freespace detection is a fundamental component of autonomous driving perception.
Recently, deep convolutional neural networks (DCNNs) have achieved impressive …

Multi-modal fusion has become an important data analysis technology in Alzheimer's
disease (AD) diagnosis, which is committed to effectively extract and utilize complementary …