To deal with the issue of computational complexity and robustness of traditional motion
planning methods for autonomous driving, an end-to-end motion planning model based on …

Motion planning is one of the most significant technologies for autonomous driving. To make
motion planning models able to learn from the environment and to deal with emergency …

Motion Planning, as a fundamental technology of automatic navigation for autonomous
vehicle, is still an open challenging issue in real-life traffic situation and is mostly applied by …

In autonomous driving, many learning-based methods for motion planing have been
proposed in literature, which can predict motion commands directly from the sensory data of …

Motion planning, as a principle technology for autonomous navigation of unmanned vehicle,
is mostly realized by the mode of pre-programming. However, due to the complexity of the …

Different deep learning approaches have been devised for Autonomous Vehicle (AV) motion
planning. However, most of such learning approaches rely on generic visual features. To …

To handle the issue of preventing emergencies for motion planning in autonomous driving,
we present a novel parallel motion planning framework. Artificial traffic scenes are firstly …

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

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 …

Motion planning with imitation learning in autonomous driving is challenging, because of the
complexity of scenarios and the distribution difference between training data and actual …