In order to drive safely in a dynamic environment, autonomous vehicles should be able to
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …

The significant contribution of human errors, accounting for approximately 94%(with a
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …

Internet of Vehicles (IoV) technology has been attracting great interest from both academia
and industry due to its huge potential impact on improving driving experiences and enabling …

Radars and lidars are two primary sensor modalities complementing optical cameras in
active safety and autonomous driving applications. Radars and lidars operate at …

Recently, inferring lane change intention has received considerable attention. Due to the
high nonlinearity and complexity of traffic contexts, traditional methods cannot satisfy the …

Connected automated driving technologies have shown tremendous improvement in recent
years. However, it is still not clear how driving behaviors and energy consumption correlate …

In recent years, the powerful combination of Multi-access Edge Computing (MEC) and
Artificial Intelligence (AI), called edge intelligence, promotes the development of Intelligent …

The development of autonomous transportation systems has received increasing attention
over the last decades. Different from existing automatic train control systems, the decision …

Trend prediction based on sensor data in a multi-sensor system is an important topic. As the
number of sensors increases, we can measure and store more and more data. However, the …

Accurately predicting the trajectory of road agents in complex traffic scenarios is challenging
because the movement patterns of agents are complex and stochastic, not only depending …