A Repeated Game Freeway Lane Changing Model.

Kyungwon Kang,Hesham A. Rakha

doi:10.3390/s20061554

Kyungwon Kang, Hesham A. Rakha

Open Access

https://doi.org/10.3390/s20061554

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Abstract

Lane changes are complex safety- and throughput-critical driver actions. Most lane-changing models deal with lane-changing maneuvers solely from the merging driver’s standpoint and thus ignore driver interaction. To overcome this shortcoming, we develop a game-theoretical decision-making model and validate the model using empirical merging maneuver data at a freeway on-ramp. Specifically, this paper advances our repeated game model by using updated payoff functions. Validation results using the Next Generation SIMulation (NGSIM) empirical data show that the developed game-theoretical model provides better prediction accuracy compared to previous work, giving correct predictions approximately 86% of the time. In addition, a sensitivity analysis demonstrates the rationality of the model and its sensitivity to variations in various factors. To provide evidence of the benefits of the repeated game approach, which takes into account previous decision-making results, a case study is conducted using an agent-based simulation model. The proposed repeated game model produces superior performance to a one-shot game model when simulating actual freeway merging behaviors. Finally, this lane change model, which captures the collective decision-making between human drivers, can be used to develop automated vehicle driving strategies.

Highlights

Driving behavior strongly affects the safety and throughput of the transportation system [1], Due to its interference with surrounding vehicles, lane-changing significantly affects traffic stream flow.Several studies have concluded that lane-changing produces a capacity drop, forming a bottleneck [2,3,4].The impacts of lane-changing maneuvers have been modeled in several studies [5,6,7,8]
This study develops an A simulation model based on agent-based model (ABM), including a vehicle acceleration controller based on the game model and a car-following model, conducts a simulation study to evaluate the performance of the repeated game model
The repeated game model using the cumulative payoffs with factor on instantaneous status only; (2) the repeated game model using the cumulative payoffs with factor δ of various conducted every verify performanceofofthe theupdated updatedpayoff payoff functions functions in of various ratesrates conducted every

Summary

Introduction

Driving behavior strongly affects the safety and throughput of the transportation system [1], Due to its interference with surrounding vehicles, lane-changing significantly affects traffic stream flow.Several studies have concluded that lane-changing produces a capacity drop, forming a bottleneck [2,3,4].The impacts of lane-changing maneuvers have been modeled in several studies [5,6,7,8]. Driving behavior strongly affects the safety and throughput of the transportation system [1], Due to its interference with surrounding vehicles, lane-changing significantly affects traffic stream flow. The applications of lane-changing models can be broadly classified into two groups: adaptive cruise control and microscopic traffic simulation [1]. Lane-changing models were proposed based on various methodologies, which are reviewed, and calibrated based on field data collected on freeways. These models are an important component of microscopic traffic simulation [11]. Focus on only the lane-changing vehicle in decision-making and vehicle control, which could be detrimental in microscopic traffic simulation, as interaction with surrounding vehicles is critical in lane-changing. A human driver will sometimes not Sensors 2020, 20, 1554; doi:10.3390/s20061554 www.mdpi.com/journal/sensors

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