Human inspiration for autonomous vehicle tactics

Tactical control is needed in environments characterized by uncertainty and continuous, dynamic change. Given the likelihood of time constraints and high risks associated with poor tactical choices, current autonomous vehicles do not possess the decision making abilities to successfully perform in these environments. However, human experts frequently operate in these domains where they are forced to make quick, reactive decisions based on incomplete information. We propose, then, that the first step in augmenting autonomous vehicles (AVs) with improved tactical control capabilities is to learn, encode, and apply tactics exhibited by human experts. To test the method, five human subjects were given the task of performing an armed reconnaissance mission in a simulation environment over multiple cases with varying terrain and probability of enemy contact. By scoring the performance in each case, the best actions and decisions were filtered out and analyzed in depth to understand the strategies and tactics behind them. Human cognitive models and decision making theories were utilized to determine the cognitive processes underneath the decisions as displayed by the human subjects' think aloud reports and surveys.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2006

Author dc:creator

• Beaton, Jonathan Scott

Advisor dc:contributor.advisor

• Christopher Dever and Brent Appleby.

• Aeronautics and Astronautics.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/36172

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/36172