This paper presents a decoupled trajectory planning framework based on the integration of
lattice searching and convex optimization for autonomous driving in structured …

This paper presents a unified optimization-based path planning approach to efficiently
compute locally optimal solutions to optimal path planning problems in unstructured …

On-road motion planning for autonomous vehicles is in general a challenging problem. Past
efforts have proposed solutions for urban and highway environments individually. We …

Lattice-based motion planners are an established method to generate feasible motions for
car-like vehicles. However, the solution paths can only reach a discretized approximation of …

This paper presents an efficient hierarchical motion planning framework with a long
planning horizon for autonomous driving in structured environments. A 3D motion planning …

Autonomous navigation in real-world environments is still a challenging task in many
respects. One of the key open challenges is fast planning of physically executable complex …

We present a motion planner for autonomous highway driving that adapts the state lattice
framework pioneered for planetary rover navigation to the structured environment of public …

Road shoulders and slopes often appear in unstructured environments. They make 2.5 D
vehicle trajectory planning commonly seen in our daily life, which lies on a 2D manifold …

It is challenging to plan paths for autonomous vehicles on half-structured roads because of
the vast planning area and complex environmental constraints. This work aims to plan …

Optimization-based trajectory planners for an autonomous vehicle have received increasing
attention due to their convenience for handling various complex constraints. However, their …