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A Game-Theoretic Model of Human Driving and Application to Discretionary Lane-Changes
arXiv - CS - Computer Science and Game Theory Pub Date : 2020-03-22 , DOI: arxiv-2003.09783
Jehong Yoo; Reza Langari

In this paper we consider the application of Stackelberg game theory to model discretionary lane-changing in lightly congested highway setting. The fundamental intent of this model, which is parameterized to capture driver disposition (aggressiveness or inattentiveness), is to help with the development of decision-making strategies for autonomous vehicles in ways that are mindful of how human drivers perform the same function on the road (on which have reported elsewhere.) This paper, however, focuses only on the model development and the respective qualitative assessment. This is accomplished in unit test simulations as well as in bulk mode (i.e. using the Monte Carlo methodology), via a limited traffic micro-simulation compared against the NHTSA 100-Car Naturalistic Driving Safety data. In particular, a qualitative comparison shows the relative consistency of the proposed model with human decision-making in terms of producing qualitatively similar proportions of crashes and near crashes as a function of driver inattentiveness (or aggressiveness). While this result by itself does not offer a true quantitative validation of the proposed model, it does demonstrate the utility of the proposed approach in modeling discretionary lane-changing and may therefore be of use in autonomous driving in a manner that is consistent with human decision making on the road.
更新日期:2020-03-24

 

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