A Game Theory-Based Approach for Modeling Autonomous Vehicle Behavior in Congested, Urban Lane-Changing Scenarios

Nikita Smirnov,Aso Validi,Yuzhou Liu,Cristina Olaverri-Monreal,Walter Morales-Alvarez

doi:10.3390/s21041523

Nikita Smirnov, Aso Validi + Show 3 more

Open Access

https://doi.org/10.3390/s21041523

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Abstract

Autonomous vehicles are expected to display human-like behavior, at least to the extent that their decisions can be intuitively understood by other road users. If this is not the case, the coexistence of manual and autonomous vehicles in a mixed environment might affect road user interactions negatively and might jeopardize road safety. To this end, it is highly important to design algorithms that are capable of analyzing human decision-making processes and of reproducing them. In this context, lane-change maneuvers have been studied extensively. However, not all potential scenarios have been considered, since most works have focused on highway rather than urban scenarios. We contribute to the field of research by investigating a particular urban traffic scenario in which an autonomous vehicle needs to determine the level of cooperation of the vehicles in the adjacent lane in order to proceed with a lane change. To this end, we present a game theory-based decision-making model for lane changing in congested urban intersections. The model takes as input driving-related parameters related to vehicles in the intersection before they come to a complete stop. We validated the model by relying on the Co-AutoSim simulator. We compared the prediction model outcomes with actual participant decisions, i.e., whether they allowed the autonomous vehicle to drive in front of them. The results are promising, with the prediction accuracy being 100% in all of the cases in which the participants allowed the lane change and 83.3% in the other cases. The false predictions were due to delays in resuming driving after the traffic light turned green.

Highlights

At this point in time, numerous studies have proven the effectiveness of autonomous vehicles in dealing with challenges such as road safety, fuel consumption, sustainability, etc. [1,2]
In real-life scenarios, interaction and cooperation with surrounding traffic might enable the maneuver, and we propose a decision-making model based on game theory for lane change
In our proposed decision-making model, we assumed that EGO is aware of the driving behavior of following vehicle (FV) prior to stopping at the intersection

Summary

Introduction

At this point in time, numerous studies have proven the effectiveness of autonomous vehicles in dealing with challenges such as road safety, fuel consumption, sustainability, etc. [1,2]. Drivers operating conventional vehicles might expect human-like behavior from autonomous vehicles, which could cause situations of uncertainty and mistrust [4] if the expectations are not fulfilled, and this could threaten road safety [5]. This is important in certain complex scenarios such as lane-changing maneuvers, in which cooperation with other road users is required. To address this issue, it is vital to design algorithms that are able to analyze human decision-making processes and interaction patterns as well as able to implement models to predict the action of drivers

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