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 …

This paper presents a practical trajectory planning framework towards fully autonomous
driving in urban environments. Firstly, based on the behavioral decision commands, a …

This paper introduces an efficient motion planning method for on-road driving of the
autonomous vehicles, which is based on the rapidly exploring random tree (RRT) algorithm …

A safe trajectory planning for on-road autonomous driving is a challenging problem owing to
the variety and complexity of driving environments. The problem should involve the …

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 …

This paper investigates real-time on-road motion planning algorithms for autonomous
passenger vehicles (APV) in urban environments, and propose a computationally efficient …

In this manuscript, we introduce a real-time motion planning system based on the Baidu
Apollo (open source) autonomous driving platform. The developed system aims to address …

In order to achieve smooth autonomous driving in real-life urban and highway environments,
a motion planner must generate trajectories that are locally smooth and responsive …

In this paper, a state space sampling-based local trajectory generation framework for
autonomous vehicles driving along a reference path is proposed. The presented framework …