Automated driving desires better performance on tasks like motion planning and interacting
with pedestrians in mixed-traffic environments. Deep learning algorithms can achieve high …

Purpose-Perception has been identified as the main cause underlying most autonomous
vehicle related accidents. As the key technology in perception, deep learning (DL) based …

User trust has been identified as a critical issue that is pivotal to the success of autonomous
vehicle (AV) operations where artificial intelligence (AI) is widely adopted. For such …

In the last decade, deep learning (DL) approaches have been used successfully in computer
vision (CV) applications. However, DL-based CV models are generally considered to be …

Different systems based on Artificial Intelligence (AI) techniques are currently used in
relevant areas such as healthcare, cybersecurity, natural language processing, and self …

Driver attention prediction is becoming an essential research problem in human-like driving
systems. This work makes an attempt to predict the d river a ttention in d riving a ccident …

How to make precise multi-agent trajectory prediction is a crucial problem in the context of
autonomous driving. It is significant to have the ability to predict surrounding road …

Humans learn to drive through both practice and theory, for example, by studying the rules,
while most self‐driving systems are limited to the former. Being able to incorporate human …

Large vision-language models (VLMs) have garnered increasing interest in autonomous
driving areas, due to their advanced capabilities in complex reasoning tasks essential for …

Autonomous vehicles operating in complex real-world environments require accurate
predictions of interactive behaviors between traffic participants. This paper tackles the …