A Reasoning Framework for Autonomous Urban Driving

Loading...
Thumbnail Image
Penn collection
Lab Papers (GRASP)
Degree type
Discipline
Subject
automated highways
mobile robots
path planning
remotely operated vehicles
road safety
road vehicles
autonomous passenger vehicle
autonomous urban driving
autonomous vehicles
complex maneuvers
context-sensitive local decision making
high-level route planning
motion planning
route-level planning
vehicle intelligent actions
Funder
Grant number
License
Copyright date
Distributor
Related resources
Author
Ferguson, Dave
Baker, Christopher
Dolan, John
Contributor
Abstract

Urban driving is a demanding task for autonomous vehicles as it requires the development and integration of several challenging capabilities, including high-level route planning, interaction with other vehicles, complex maneuvers, and ultra-reliability. In this paper, we present a reasoning framework for an autonomous vehicle navigating through urban environments. Our approach combines route-level planning, context-sensitive local decision making, and sophisticated motion planning to produce safe, intelligent actions for the vehicle. We provide examples from an implementation on an autonomous passenger vehicle that has driven over 3000 autonomous kilometers and competed in, and won, the Urban Challenge.

Advisor
Date of presentation
2008-06-04
Conference name
Lab Papers (GRASP)
Conference dates
2023-05-17T03:09:52.000
Conference location
Date Range for Data Collection (Start Date)
Date Range for Data Collection (End Date)
Digital Object Identifier
Series name and number
Volume number
Issue number
Publisher
Publisher DOI
Journal Issue
Comments
Copyright 2008 IEEE. Reprinted from: Ferguson, D.; Baker, C.; Likhachev, M.; Dolan, J., "A reasoning framework for autonomous urban driving," Intelligent Vehicles Symposium, 2008 IEEE , vol., no., pp.775-780, 4-6 June 2008 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4621247&isnumber=4621124 This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
Recommended citation
Collection