Encoding Defensive Driving as a Dynamic Nash Game

Robots deployed in real-world environments should operate safely in a robust manner. In scenarios where an "ego" agent navigates in an environment with multiple other "non-ego" agents, two modes of safety are commonly proposed-adversarial robustness and probabilistic constraint s...

Full description

Saved in:
Bibliographic Details
Published in2021 IEEE International Conference on Robotics and Automation (ICRA) pp. 10749 - 10756
Main Authors Chiu, Chih-Yuan, Fridovich-Keil, David, Tomlin, Claire J.
Format Conference Proceeding
LanguageEnglish
Published IEEE 30.05.2021
Subjects
Couplings
Encoding
Games
Navigation
Probabilistic logic
Robustness
Safety
Online AccessGet full text

Cover

More Information
Summary:Robots deployed in real-world environments should operate safely in a robust manner. In scenarios where an "ego" agent navigates in an environment with multiple other "non-ego" agents, two modes of safety are commonly proposed-adversarial robustness and probabilistic constraint satisfaction. However, while the former is generally computationally intractable and leads to overconservative solutions, the latter typically relies on strong distributional assumptions and ignores strategic coupling between agents.To avoid these drawbacks, we present a novel formulation of robustness within the framework of general-sum dynamic game theory, modeled on defensive driving. More precisely, we prepend an adversarial phase to the ego agent's cost function. That is, we prepend a time interval during which other agents are assumed to be temporarily distracted, in order to render the ego agent's equilibrium trajectory robust against other agents' potentially dangerous behavior during this time. We demonstrate the effectiveness of our new formulation in encoding safety via multiple traffic scenarios.
ISSN:2577-087X
DOI:10.1109/ICRA48506.2021.9560788