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 …

This paper explores the emerging knowledge-driven autonomous driving technologies. Our
investigation highlights the limitations of current autonomous driving systems, in particular …

Recently significant progress has been made in vehicle prediction and planning algorithms
for autonomous driving. However, it remains quite challenging for an autonomous vehicle to …

Safely interacting with other traffic participants is one of the core requirements for
autonomous driving, especially in intersections and occlusions. Most existing approaches …

Diffusion models are promising for joint trajectory prediction and controllable generation in
autonomous driving, but they face challenges of inefficient inference steps and high …

Interactive behavior modeling of multiple agents is an essential challenge in simulation,
especially in scenarios when agents need to avoid collisions and cooperate at the same …

Effective interaction modeling and behavior prediction of dynamic agents play a significant
role in interactive motion planning for autonomous robots. Although existing methods have …

Local planning for a differential wheeled robot is designed to generate kinodynamic feasible
actions that guide the robot to a goal position along the navigation path while avoiding …

The recent development of cloud computing and edge computing shows great promise for
the Connected and Automated Vehicle (CAV), by enabling CAVs to offload their massive on …

Accurate trajectory prediction is crucial for ensuring safe and efficient autonomous driving.
However, most existing methods overlook complex interactions between traffic participants …