Understanding how the surrounding environment changes is crucial for performing
downstream tasks safely and reliably in autonomous driving applications. Recent occupancy …

When autonomous vehicles are deployed on public roads, they will encounter countless and
diverse driving situations. Many manually designed driving policies are difficult to scale to …

Generating safe and non-conservative behaviors in dense, dynamic environments remains
challenging for automated vehicles due to the stochastic nature of traffic participants' …

On highways, the interaction with surrounding vehicles is very crucial for the decision-
making and planning of autonomous vehicles. However, the multi-modal driving intentions …

Sampling, generation, and evaluation of scenarios are essential steps for intelligent testing
of autonomous vehicles. Since uncertainty in driving behavior always leads to different …

The bio-inspired flocking model has been widely utilized in self-organized swarms.
However, existing potential field methods fail to guarantee safe and orderly swarm …

Prediction, decision-making, and motion planning are essential for autonomous driving. In
most contemporary works, they are considered as individual modules or combined into a …

Motion prediction for traffic participants is essential for a safe and robust automated driving
system, especially in cluttered urban environments. However, it is highly challenging due to …

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 …

Velocity planning is an important module of autonomous driving, which aims to generate the
velocity profile given a reference path. However, most existing algorithms fail to adequately …