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

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 focuses on optimal path planning for vehicles in known environments. Previous
work has presented mixed integer linear programming (MILP) formulations, which suffer …

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 …

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 …

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 …

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 …

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 …

Efficient trajectory planning of autonomous vehicles in complex traffic scenarios is of interest
both academically and in automotive industry. Time efficiency and safety are of key …

There is a strong trend for increasingly sophisticated Advanced Driver Assistance Systems
(ADAS) such as Autonomous Emergency Braking (AEB) systems, Lane Keeping Aid (LKA) …