A Game-Theoretic Model of Human Driving and Application to Discretionary Lane-Changes

by   Jehong Yoo, et al.

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.


page 9

page 10


Brain-Inspired Modelling and Decision-making for Human-Like Autonomous Driving in Mixed Traffic Environment

In this paper, a human-like driving framework is designed for autonomous...

A Repeated Game Freeway Lane Changing Model

Lane changes are complex safety and throughput critical driver actions. ...

Contextual Intelligent Decisions: Expert Moderation of Machine Outputs for Fair Assessment of Commercial Driving

Commercial driving is a complex multifaceted task influenced by personal...

Multiple criteria decision-making for lane-change model

Simulation has long been an essential part of testing autonomous driving...

Estimating the Probability That a Vehicle Reaches a Near-Term Goal State Using Multiple Lane Changes

This paper presents a model to estimate the probability of reaching a ne...

CARPAL: Confidence-Aware Intent Recognition for Parallel Autonomy

Predicting the behavior of road agents is a difficult and crucial task f...

Detecting Driveable Area for Autonomous Vehicles

Autonomous driving is a challenging problem where there is currently an ...