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 …

In this paper we propose a novel end-to-end learnable network that performs joint
perception, prediction and motion planning for self-driving vehicles and produces …

Making safe and human-like decisions is an essential capability of autonomous driving
systems, and learning-based behavior planning presents a promising pathway toward …

Predicting the future states of surrounding traffic participants and planning a safe, smooth,
and socially compliant trajectory accordingly are crucial for autonomous vehicles (AVs) …

In this paper we present the first safe system for full control of self-driving vehicles trained
from human demonstrations and deployed in challenging, real-world, urban environments …

The goal of autonomous vehicles is to navigate public roads safely and comfortably. To
enforce safety, traditional planning approaches rely on handcrafted rules to generate …

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 …

Learning-based driving solution, a new branch for autonomous driving, is expected to
simplify the modeling of driving by learning the underlying mechanisms from data. To …

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 …

Autonomous vehicles need to handle various traffic conditions and make safe and efficient
decisions and maneuvers. However, on the one hand, a single optimization/sampling-based …