Autonomous driving in semi-structured environments: mapping and planning

Authors:
Dmitri Dolgov;Sebastian Thrun
Affiliations:
Toyota Research Institute, AI & Robotics Group, Ann Arbor;Stanford University, Computer Science Department, Stanford
Venue:
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Year:
2009

Citing 5
Cited 4

Numerical recipes in C (2nd ed.): the art of scientific computing

Numerical recipes in C (2nd ed.): the art of scientific computing
A complete navigation system for goal acquisition in unknown environments

IROS '95 Proceedings of the International Conference on Intelligent Robots and Systems-Volume 1 - Volume 1
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)

Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
Editorial

Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part I
Junior: The Stanford entry in the Urban Challenge

Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part II

Augmenting cartographic resources for autonomous driving

Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Utilizing prior information to enhance self-supervised aerial image analysis for extracting parking lot structures

IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Path Planning for Autonomous Vehicles in Unknown Semi-structured Environments

International Journal of Robotics Research
Obstacle-Free Pathway Detection by Means of Depth Maps

Journal of Intelligent and Robotic Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

We consider the problem of autonomous driving in semi-structured environments (e.g., parking lots). Such environments have strong topological structure (graphs of drivable lanes), but maneuvers with significant deviations from those graphs are valid and frequent. We address two main challenges of operating in such environments: i) detection of topological structure from sensor data, and ii) using that structure to guide path planning. We present experimental results on both of these topics, demonstrating robust estimation of lane networks in parking lots and the benefits of using these topological networks to guide path planning.