scholarly journals Lane-changing decision modelling in congested traffic with a game theory-based decomposition algorithm

2022 ◽  
Vol 107 ◽  
pp. 104530
Author(s):  
Jian Guo ◽  
Istvan Harmati
Keyword(s):  
Game Theory ◽  
Decomposition Algorithm ◽  
Lane Changing ◽  
Congested Traffic ◽  
Decision Modelling

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

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1523
Author(s):  
Nikita Smirnov ◽  
Yuzhou Liu ◽  
Aso Validi ◽  
Walter Morales-Alvarez ◽  
Cristina Olaverri-Monreal
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Urban Traffic ◽  
Road User ◽  
Lane Change ◽  
Traffic Light ◽  
User Interactions ◽  
Lane Changing

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.

Modeling lane-changing behavior in a connected environment: A game theory approach

2015 ◽  
Vol 59 ◽  
pp. 216-232 ◽  
Author(s):  
Alireza Talebpour ◽  
Hani S. Mahmassani ◽  
Samer H. Hamdar
Keyword(s):  
Game Theory ◽  
Theory Approach ◽  
Lane Changing

scholarly journals A review of game theory models of lane changing

2020 ◽  
Vol 16 (3) ◽  
pp. 1628-1647 ◽  
Author(s):  
Ang Ji ◽  
David Levinson
Keyword(s):  
Game Theory ◽  
Lane Changing

scholarly journals WINNING STRATEGIES IN CONGESTED TRAFFIC

2012 ◽  
Vol 23 (09) ◽  
pp. 1250063 ◽  
Author(s):  
FERENC JÁRAI-SZABÓ ◽  
ZOLTÁN NÉDA
Keyword(s):  
Phase Transition ◽  
The Other ◽  
Type Model ◽  
Block Type ◽  
Lane Changing ◽  
Average Gain ◽  
Winning Probability ◽  
Congested Traffic ◽  
Winning Strategies

One-directional traffic on two-lanes is modeled in the framework of a spring-block type model. A fraction q of the cars are allowed to change lanes, following simple dynamical rules, while the other cars keep their initial lane. The advance of cars, starting from equivalent positions and following the two driving strategies is studied and compared. As a function of the parameter q the winning probability and the average gain in the advancement for the lane-changing strategy is computed. An interesting phase-transition like behavior is revealed and conclusions are drawn regarding the conditions when the lane changing strategy is the better option for the drivers.

Lane Change Decision Analysis Based on Drivers' Perception-Judgment and Game Theory

2013 ◽  
Vol 361-363 ◽  
pp. 1875-1879 ◽  
Author(s):  
Jin Shuan Peng ◽  
Ying Shi Guo ◽  
Yi Ming Shao
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Nash Equilibrium ◽  
Equilibrium Solution ◽  
Information Source ◽  
Driving Safety ◽  
External Information ◽  
Lane Change ◽  
Safety Level ◽  
Lane Changing

To clearly understand the mechanism of drivers lane-changing decision, based on drivers perception of external information, integrated cognitive judgment and game theory, the decision-making model was established, then the structure and operating mechanism of the model were detailedly analyzed. By introducing game theory-related knowledge, the non-cooperative mixed strategy game between the object vehicle and the following vehicle in the target lane was further discussed. Then, the benefits and Nash equilibrium solution of the participants in the game were deeply researched. Analysis shows that lane-changing decision is composed of information perception and three judgment-decision processes, the factors which would affect decision-making level include information source characteristics, the ability of drivers perception and comprehensive cognitive judgment, driving behavior characteristics and so on. The Nash equilibrium solution of the lane change game is determined by driving safety level, journey time and importance degree of the revenues.

scholarly journals Lane-Changing Behavior Prediction Based on Game Theory and Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Shuo Jia ◽  
Fei Hui ◽  
Cheng Wei ◽  
Xiangmo Zhao ◽  
Jianbei Liu
Keyword(s):  
Game Theory ◽  
Deep Learning ◽  
Short Term Memory ◽  
Accurate Prediction ◽  
Behavior Prediction ◽  
Short Term ◽  
Lane Changing ◽  
Proposed Model ◽  
The Game Theory ◽  
Long Short Term Memory

Lane changing is an important scenario in traffic environments, and accurate prediction of lane-changing behavior is essential to ensure traffic and driver safety. To achieve this goal, a vehicle lane-changing prediction model based on game theory and deep learning is developed. In the game theory component, the interaction between vehicles during lane changing is analyzed according to the running state of the vehicle, with the probability of lane changing as its output. For the deep-learning component, long short-term memory and a convolutional neural network are used to extract and learn data features during the lane-changing process as well as combine the output of the game theory component to obtain the prediction result of whether the vehicle will change lanes. By using an open-source traffic dataset to train and verify the proposed model, the verification results show that the prediction accuracy can reach 94.56% within 0.4 s of lane-changing operation and that the model can achieve timely and accurate prediction of the lane-changing behavior of vehicles.

Multi-Player Dynamic Game-Based Automatic Lane-Changing Decision Model under Mixed Autonomous Vehicle and Human-Driven Vehicle Environment

2020 ◽  
Vol 2674 (11) ◽  
pp. 165-183
Author(s):  
Yuewen Yu ◽  
Shikun Liu ◽  
Peter J. Jin ◽  
Xia Luo ◽  
Mengxue Wang
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Splitting Method ◽  
Dynamic Game ◽  
Cruise Control ◽  
Lane Changing ◽  
Bicycle Model ◽  
Decision Making Model

The lane-changing decision-making process is challenging but critical to ensure safe and smooth maneuvers for autonomous vehicles (AVs). Conventional Gipps-type algorithms lack the flexibility for practical use under a mixed autonomous vehicle and human-driven vehicle (AV-HV) environment. Algorithms based on utility ignore the reactions of surrounding vehicles to the lane-changing vehicle. Game theory is a good way to solve the shortcomings of current algorithms, but most models based on game theory simplify the game with surrounding vehicles to the game with the following vehicle in the target lane, which means that the lane-changing decision under a mixed environment is not realized. This paper proposes a lane-changing decision-making model which is suitable for an AV to change lanes under a mixed environment based on a multi-player dynamic game theory. The overtaking expectation parameter (OEP) is introduced to estimate the utility of the following vehicle, OEP can be calculated by the proposed non-lane-based full velocity difference model with the consideration of lateral move and aggressiveness. This paper further proposes a hybrid splitting method algorithm to obtain the Nash equilibrium solution in the multi-player game to obtain the optimal strategy of lane-changing decision for AVs. An adaptive cruise control simulation environment is developed with MATLAB’s Simulink toolbox using Next Generation Simulation (NGSIM) data as the background traffic flow. The classic bicycle model is used in the control of involved HVs. Simulation results show the efficiency of the proposed multi-player dynamic game-based algorithm for lane-changing decision making by AVs under a mixed AV-HV environment.

Logistic regression model for discretionary lane changing under congested traffic

2015 ◽  
Vol 11 (4) ◽  
pp. 333-344 ◽  
Author(s):  
Minju Park ◽  
Kitae Jang ◽  
Jinwoo Lee ◽  
Hwasoo Yeo
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Lane Changing ◽  
Congested Traffic

Assessment of Discretionary Lane-Changing Decisions using a Random Parameters Approach with Heterogeneity in Means and Variances

2021 ◽  
pp. 036119812199236
Author(s):  
Qianwen Li ◽  
Xiaopeng Li ◽  
Fred Mannering
Keyword(s):  
Traffic Flow ◽  
Unobserved Heterogeneity ◽  
Random Parameters ◽  
Lane Changing ◽  
Flow Models ◽  
Congested Traffic ◽  
Traffic Flow Models ◽  
The Mean ◽  
New Evidence ◽  
Vehicle Size

Lane-changing maneuvers on highways may cause capacity drops, create shock waves, and potentially increase collision risks. Properly managing lane-changing behavior to reduce these adverse impacts requires an understanding of their determinants. This paper investigates the determinants of lane changing in congested traffic using a next generation simulation dataset. A random parameters binary logit model with heterogeneity in means and variances was estimated to account for unobserved heterogeneity in lane-changing behavior across vehicles. Estimation results show that average headway, the original lane of the vehicle, driver acceleration/deceleration behavior, and vehicle size all significantly influence lane-changing probabilities. It was further found that the effect of vehicle size varied significantly across observations, that the mean of this variation decreased with increasing average headway, and the variance increased with increasing driver acceleration/deceleration. These empirical findings provide interesting new evidence on the determinants of lane changing, which can be used in traffic flow models to better replicate and predict traffic flow.

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