… urban driving scenarios. Along with the trend of personalized autonomous driving, we …
tunability and use machine learning techniques to distill individual-specific driving patterns. …

… introduce a motion planning framework consisting of two components: a data-driven policy
that uses visual inputs and human feedback to generate socially compliant driving behaviors (…

… -changing motion planning strategy for personalized energy-efficient autonomous driving
is proposed in this research. The key technologies consist of trajectory planning and trajectory …

… Given these tools, we design a motion planner that is guaranteed to follow a path which is
not known in advance, but has a bounded curvature, all of that while guaranteeing that the car …

… tremendous efforts in autonomous driving. Motion planning for autonomous driving plays an
… Although many learning-based motion planning algorithms have been proposed such as …

… proposes an integrated motion planning and control approach for autonomous car navigation.
Existing approaches to autonomous vehicle navigation typically plan a trajectory and pass …

… , resulting in less efficient motion planning algorithms. To solve this problem, … motion planning
is a feasible research direction. We proposed a motion planning algorithm for autonomous …

… target motion prediction and motion planning of autonomous vehicles … motion prediction
on motion planning algorithms has been conducted qualitatively and quantitatively. For motion …

… input for motion planning, which enables safe autonomous driving on public roads. In this
paper, a safe motion planning … In addition, a GNN prediction model-enabled motion planner is …

… kinodynamic motion planner, for obtaining the best trajectory and velocity profile for
autonomous driving in dynamic complex environments, such as driving in urban scenarios. The …