Intelligent vehicles (IVs) have gained worldwide attention due to their increased
convenience, safety advantages, and potential commercial value. Despite predictions of …

With recent developments, the performance of automotive radar has improved significantly.
The next generation of 4D radar can achieve imaging capability in the form of high …

Modern autonomous driving system is characterized as modular tasks in sequential order,
ie, perception, prediction, and planning. In order to perform a wide diversity of tasks and …

Predicting the future trajectories of surrounding agents is essential for autonomous vehicles
to operate safely. This paper presents QCNet, a modeling framework toward pushing the …

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace
due to the convenience, safety, and economic benefits. Although a number of surveys have …

Autonomous driving promises transformative improvements to transportation, but building
systems capable of safely navigating the unstructured complexity of real-world scenarios …

Reliable forecasting of the future behavior of road agents is a critical component to safe
planning in autonomous vehicles. Here, we represent continuous trajectories as sequences …

We present MotionDiffuser, a diffusion based representation for the joint distribution of future
trajectories over multiple agents. Such representation has several key advantages: first, our …

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 …

Predicting multimodal future behavior of traffic participants is essential for robotic vehicles to
make safe decisions. Existing works explore to directly predict future trajectories based on …