Driving Environment Uncertainty-Aware Motion Planning for Autonomous Vehicles

Motion- and Uncertainty-aware Path Planning for Micro Aerial Vehicles

Markus W. Achtelik and Simon Lynen Autonomous Systems Lab, ETH Zurich, CH-8092 Zurich, Switzerland e-mail: [email protected], [email protected] Stephan Weiss Computer Vision Group, NASA Jet Propulsion Laboratory/California Institute of Technology, Pasadena, California 91109 e-mail: [email protected] Margarita Chli Vision for Robotics Lab, School of Informatics, University...

On-Road Motion Planning for Autonomous Vehicles

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 identified region in which the optimal trajectory is most likely to exist. The main contribution of this paper is a computationally efficient planner which handles dynamic environments generically. The Dynamic Programm...

Safe Motion Planning for Autonomous Driving

Self-driving cars have the potential to revolutionize transportation by making it cheaper, safer, and more efficient. In this thesis we describe a novel motion planning system, which translates high-level navigation goals into low-level actions for controlling a vehicle. Specifically, the motion planning system is responsible for choosing at each time step an appropriate velocity and steering a...

Dynamic motion planning of autonomous vehicles

This paper presents a method for planning the motions of autonomous vehicles moving on general terrains. The method obtains the geometric path and vehicle speeds that minimize motion time considering vehicle dynamics, terrain topography, obstacles, and surface mobility. The terrain is represented by a smooth cubic B patch, and the geometric path consists of a B spline curve mapped to the surfac...

Real-Time Motion Planning 
 and
 Autonomous Driving