Motion prediction modules are crucial for autonomous vehicles to forecast the future
behavior of surrounding road users. Failures in prediction modules can mislead a …

Reinforcement learning (RL) has been widely used in the decision-making of autonomous
vehicles (AVs) in recent studies. However, existing RL methods generally find the optimal …

As the complexity of autonomous vehicles (AVs) continues to increase and artificial
intelligence algorithms are becoming increasingly ubiquitous, a novel safety concern known …

Safe, socially compliant, and efficient navigation of low-speed autonomous vehicles (AVs) in
pedestrian-rich environments necessitates considering pedestrians' future positions and …

In Intelligent Transportation Systems (ITS), ensuring road safety has paved the way for
innovative advancements such as autonomous driving. These self-driving vehicles, with …

In this study, we introduce an AR-based Meta-Vehicle Road Collaboration Testing System
(AR-MVRTs), a significant advancement in autonomous driving testing. This system utilizes …

For autonomous vehicles, scene understanding is still one of the major challenges, which
needs to be well handled to avoid jittery decisions and unsmooth trajectories. Furthermore …

The interactions between vehicles and pedestrians are complex due to their
interdependence and coupling. Understanding these interactions is crucial for the …

Perception modules in Autonomous Driving Systems (ADS) provide excellent performance
in simple, constrained environments but generally have precarious performance in real …

Precisely assessing driving threat of road segments could significantly enhance the driving
efficiency of intelligent connected vehicles (ICV) within mixed traffic scenarios. Existing …