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 …

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 …

Autonomous vehicles are more likely to be accepted if they drive accurately, comfortably, but
also similar to how human drivers would. This is especially true when autonomous and …

Autonomous driving is a challenging problem because the autonomous vehicle must
understand complex and dynamic environment. This understanding consists of predicting …

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 …

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 …

Neural network-based driving planners have shown great promises in improving task
performance of autonomous driving. However, it is critical and yet very challenging to ensure …

The decision and planning system for autonomous driving in urban environments is hard to
design. Most current methods manually design the driving policy, which can be expensive to …

Driving requires the ability to handle unpredictable situations. Since it is not always possible
to predict an impending danger, a good driver should preventively assess whether a …

When learning common skills like driving, beginners usually have domain experts standing
by to ensure the safety of the learning process. We formulate such learning scheme under …