Road Runner: An Autonomous Vehicle for HRI Research

Scientific American, 1990

Computer, 2006

Springer Tracts in Advanced Robotics, 2009

This paper reports on AnnieWAY, an autonomous vehicle that is capable of driving through urban scenarios and that successfully entered the finals of the 2007 DARPA Urban Challenge competition. After describing the main challenges imposed and the major hardware components, we outline the underlying software structure and focus on selected algorithms. Environmental perception mainly relies on a recent laser scanner that delivers both range and reflectivity measurements. Whereas range measurements are used to provide three-dimensional scene geometry, measuring reflectivity allows for robust lane marker detection. Mission and maneuver planning is conducted using a hierarchical state machine that generates behavior in accordance with California traffic laws. We conclude with a report of the results achieved during the competition. C 2008 Wiley Periodicals, Inc.

Abstract— This paper describes autonomous vehicle and driver assistance research beginning with the 1997 National Automated Highway System Consortium Demonstration. As a microcosm of the community at large we discuss how Carnegie Mellon autonomous vehicle research has progressed in the last decade. Since the demonstration we have formed two companies: AssistWare became a leading developer of lane departure warning systems; and, Applied Perception which emphasized off-road navigation and perception research. In parallel, we have competed in the DARPA Grand Challenges and won the Urban Challenge. Each of these endeavors has deepened our understanding of what it will take to broadly deploy autonomous vehicles.

Experience from the DARPA Urban Challenge, 2011

This paper describes the system design developed for Team Gator Nation's submission to the 2007 DARPA Urban Challenge. In this event, vehicles had to navigate on city streets while obeying basic traffic laws. One of the major challenges was interacting with other vehicles such as at intersections. To address these challenges, a hybrid Toyota Highlander was automated and instrumented with pose estimation (GPS and inertial) and object detection (vision and ladar) sensors. A control architecture was developed which integrates planning, perception, decision making, and control elements. The intelligence element implements the Adaptive Planning Framework which was developed by researchers at the University of Florida. This framework provides a means for situation assessment, behavior mode evaluation, and behavior selection and execution. The paper describes this architecture and concludes with lessons learned from participation in the Urban Challenge event.

Journal of Field Robotics, 2008

This paper reports on AnnieWAY, an autonomous vehicle that is capable of driving through urban scenarios and that successfully entered the finals of the 2007 DARPA Urban Challenge competition. After describing the main challenges imposed and the major hardware components, we outline the underlying software structure and focus on selected algorithms. Environmental perception mainly relies on a recent laser scanner that delivers both range and reflectivity measurements. Whereas range measurements are used to provide three-dimensional scene geometry, measuring reflectivity allows for robust lane marker detection. Mission and maneuver planning is conducted using a hierarchical state machine that generates behavior in accordance with California traffic laws. We conclude with a report of the results achieved during the competition. C 2008 Wiley Periodicals, Inc.

Lecture Notes in Computer Science, 1996

Experience from the DARPA Urban Challenge, 2011

Transportation Research Interdisciplinary Perspectives

Automated driving research over the past decades has mostly focused on highway environments. Recent technological developments have drawn researchers and manufacturers to look ahead at introducing automated driving in cities. The current position paper examines this challenge from the viewpoint of scientific experts. Sixteen Human Factors researchers were interviewed about their personal perspectives on automated vehicles (AVs) and the interaction with VRUs in the future urban environment. Aspects such as smart infrastructure, external human-machine interfaces (eHMIs), and the potential of augmented reality (AR) were addressed during the interviews. The interviews showed that the researchers believed that fully autonomous vehicles will not be introduced in the coming decades and that intermediate levels of automation, specific AV services, or shared control will be used instead. The researchers foresaw a large role of smart infrastructure and expressed a need for AV-VRU segregation, but were concerned about corresponding costs and maintenance requirements. The majority indicated that eHMIs will enhance future AV-VRU interaction, but they noted that implicit communication will remain dominant and advised against text-based and instructive eHMIs. AR was commended for its potential in assisting VRUs, but given the technological challenges, its use, for the time being, was believed to be limited to scientific experiments. The present expert perspectives may be instrumental to various stakeholders and researchers concerned with the relationship between VRUs and AVs in future urban traffic. Glossary • Anthropomorphic eHMIs: eHMIs that feature elements that borrow from human behaviour or appearance. Examples are artificial eyes in the area of the headlamps that can follow a crossing pedestrian (Chang et al., 2017), a car with a 'smiling' display on the bumper (Semcon, 2016), and a physically actuated hand attached to the roof of a vehicle that can signal a pedestrian to cross (Mahadevan et al., 2018). • Artificial General Intelligence (AGI): hypothetical machine intelligence that is able to understand any task that a human can.

2016

This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited. Permission is given to duplicate this document for personal use only, as long as it is unaltered and complete. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial use. For information on reprint and linking permissions, please visit www.rand.org/pubs/permissions.html. The RAND Corporation is a research organization that develops solutions to public policy challenges to help make communities throughout the world safer and more secure, healthier and more prosperous. RAND is nonprofit, nonpartisan, and committed to the public interest. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.