In driving scenes, road agents often engage in frequent interaction and strive to understand
their surroundings. Ego-agent (each road agent itself) predicts what behavior will be …

Traffic accident detection and anticipation is an obstinate road safety problem and
painstaking efforts have been devoted. With the rapid growth of video data, Vision-based …

The primary goal of traffic accident anticipation is to foresee potential accidents in real time
using dashcam videos, a task that is pivotal for enhancing the safety and reliability of …

Explainable artificial intelligence (XAI) can help build trust between AI models and
healthcare professionals in the context of medical image classification. XAI can help explain …

Traffic accident prediction in driving videos aims to provide an early warning of the accident
occurrence, and supports the decision making of safe driving systems. Previous works …

Detecting dangerous traffic agents in videos captured by vehicle-mounted dashboard
cameras (dashcams) is essential to ensure safe navigation in complex environments …

Motorcycle crashes pose a serious problem because their probability of causing casualties
is greater than that of passenger vehicle crashes. Therefore, accurately identifying the …

eXplainable Artificial Intelligence (XAI) is a new trend of machine learning. Machine learning
models are used to predict or decide something, and they derive output based on a large …

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

Explainable AI (XAI) methods provide explanations of AI models, but our understanding of
how they compare with human explanations remains limited. In image classification, we …