Social behavior for autonomous vehicles
Name
24972.full.pdf
Description
Published version
Size
1.63 MB
Format
Adobe PDF
Checksum (MD5)
b2bc3b2702d50ab0ca4aae5d5c7b4e7b
Author(s)
Schwarting, Wilko
Pierson, Alyssa
Alonso-Mora, Javier
Karaman, Sertac
Rus, Daniela
Date Issued
2019
Journal
Proceedings of the National Academy of Sciences of the United States of America
Publisher
Proceedings of the National Academy of Sciences
Version
Final published version
Abstract
© 2019 National Academy of Sciences. All rights reserved. Deployment of autonomous vehicles on public roads promises increased efficiency and safety. It requires understanding the intent of human drivers and adapting to their driving styles. Autonomous vehicles must also behave in safe and predictable ways without requiring explicit communication. We integrate tools from social psychology into autonomous-vehicle decision making to quantify and predict the social behavior of other drivers and to behave in a socially compliant way. A key component is Social Value Orientation (SVO), which quantifies the degree of an agent’s selfishness or altruism, allowing us to better predict how the agent will interact and cooperate with others. We model interactions between agents as a best-response game wherein each agent negotiates to maximize their own utility. We solve the dynamic game by finding the Nash equilibrium, yielding an online method of predicting multiagent interactions given their SVOs. This approach allows autonomous vehicles to observe human drivers, estimate their SVOs, and generate an autonomous control policy in real time. We demonstrate the capabilities and performance of our algorithm in challenging traffic scenarios: merging lanes and unprotected left turns. We validate our results in simulation and on human driving data from the NGSIM dataset. Our results illustrate how the algorithm’s behavior adapts to social preferences of other drivers. By incorporating SVO, we improve autonomous performance and reduce errors in human trajectory predictions by 25%.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1073/PNAS.1820676116
