We propose an image-based real-time path planner for the self-driving car IARA, named
DeepPath. DeepPath uses a CNN for inferring paths from images. During the self-driving car …

For autonomous driving without high‐definition maps, we present a model capable of
generating multiple plausible paths from egocentric images for autonomous vehicles. Our …

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 …

In autonomous driving systems, path planning algorithms play a crucial role in ensuring
vehicle safety, driving efficiency, and passenger comfort. The traditional path planning …

With the recent advancement of deep learning technology, data-driven approaches for
autonomous car prediction and planning have achieved extraordinary performance …

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 …

Early attempts on imitating human driving behavior with deep learning have been
implemented in an reactive navigation scheme which is to directly map the sensory …

End-to-end deep learning approaches have been proven to be efficient in autonomous
driving and robotics. By using deep learning techniques for decision-making, those systems …

Automated driving in urban settings is challenging. Human participant behavior is difficult to
model, and conventional, rule-based Automated Driving Systems (ADSs) tend to fail when …

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 …