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 …

We present a motion planner for autonomous on-road driving, especially on highways. It
adapts the idea of a on-road state lattice. A focused search is performed in the previously …

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 …

For vehicles navigating initially unknown cluttered environments, current state-of-the-art
planning algorithms are able to plan and re-plan dynamically-feasible paths efficiently and …

We present a method for motion planning in the presence of moving obstacles that is aimed
at dynamic on-road driving scenarios. Planning is performed within a geometric graph that is …

In this paper, we present an algorithm for generating complex dynamically feasible
maneuvers for autonomous vehicles traveling at high speeds over large distances. Our …

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

We present an approach to the problem of differentially constrained mobile robot motion
planning in arbitrary cost fields. The approach is based on deterministic search in a specially …

Motion planning for a general 2-trailer system poses a hard problem for any motion planning
algorithm and previous methods have lacked any completeness or optimality guarantees. In …

In this paper, an efficient real-time autonomous driving motion planner with trajectory
optimization is proposed. The planner first discretizes the plan space and searches for the …