Trajectory planning has generally been framed as finding the lowest cost one from a set of
trajectory candidates, where the cost function has been hand-crafted with carefully tuned …

Human-like driving is of great significance for safety and comfort of autonomous vehicles,
but existing trajectory planning methods for on-road vehicles rarely take the similarity with …

Driving in a human-like manner is important for an autonomous vehicle to be a smart and
predictable traffic participant. To achieve this goal, parameters of the motion planning …

Trajectory planning is one of the most important modules of the Autonomous Driving
Systems (ADSs), which aims to achieve a safe and comfortable interaction between the …

Learning-based approaches have achieved remarkable performance in the domain of
autonomous driving. Leveraging the impressive ability of neural networks and large …

Reliable trajectory planning like human drivers in real-world dynamic urban environments is
a critical capability for autonomous driving. To this end, we develop a vision and imitation …

The motion planners used in self-driving vehicles need to generate trajectories that are safe,
comfortable, and obey the traffic rules. This is usually achieved by two modules: behavior …

Autonomous driving has shown the capability of providing driver convenience and
enhancing safety. While introducing autonomous driving into our current traffic system, one …

In the coming decades, it is a universal consensus that autonomous vehicles (AVs) and
human-driven vehicles will share the traffic roads. Trajectory planning of AVs has been …

The ultimate goal of self-driving technologies is to offer a safe and human-like driving
experience. As one of the most important enabling functionalities, trajectory planning has …