Coordinated decisions of discretionary lane change between connected and automated vehicles on freeways: a game theory-based lane change strategy

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.

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Highway Traffic Scenario-Based Lane Change Strategy for Autonomous Vehicle

Lecture Notes in Mechanical Engineering - Innovative Design, Analysis and Development Practices in Aerospace and Automotive Engineering (I-DAD 2018) ◽

10.1007/978-981-13-2718-6_39 ◽

2018 ◽

pp. 405-413

Author(s):

Gourish Hiremath ◽

Kiran Wani ◽

Sanjay Patil

Keyword(s):

Autonomous Vehicle ◽

Lane Change ◽

Highway Traffic ◽

Change Strategy ◽

Traffic Scenario

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Cellular Automata–Based Modeling and Simulation of a Mixed Traffic Flow of Manual and Automated Vehicles

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2622-10 ◽

2017 ◽

Vol 2622 (1) ◽

pp. 105-116 ◽

Cited By ~ 4

Author(s):

Da Yang ◽

Xiaoping Qiu ◽

Lina Ma ◽

Danhong Wu ◽

Liling Zhu ◽

...

Keyword(s):

Reaction Time ◽

Traffic Flow ◽

Road Traffic ◽

Flow Characteristics ◽

Lane Change ◽

Mixed Traffic ◽

Automated Vehicles ◽

Obvious Effect ◽

Mixed Traffic Flow ◽

Automated Vehicle

In recent years, automated vehicles have been developing rapidly, and some automated vehicles have begun to drive on highways. The market share of automated vehicles is expected to increase and will greatly affect traffic flow characteristics. This paper focuses on the mixed traffic flow of manual and automated vehicles. The study improves the existing cellular automaton model to capture the differences between manual vehicles and automated vehicles. Computer simulations are employed to analyze the characteristic variations in the mixed traffic flow under different automated vehicle proportions, lane change probabilities, and reaction times. Several new conclusions are drawn in the paper. First, with the increment of the proportion of automated vehicles, freeway capacity increases; the capacity increment is more significant for single-lane traffic than for two-lane traffic. Second, for single-lane traffic flow, reducing the reaction time of the automated vehicle can significantly improve road traffic capacity—as much as doubling it—and reaction time reduction has no obvious effect on the capacity of the two-lane traffic. Third, with the proportion increment of automated vehicles, lane change frequency reduces significantly. Fourth, when the density is 15 < ρ < 55 vehicles/km, the addition of 20% automated vehicles to a traffic flow that consisted of only manual vehicles can decrease congestion by up to 16.7%.

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An Interaction-Aware Lane Change Behavior Planner for Automated Vehicles on Highways Based on Polygon Clipping

IEEE Robotics and Automation Letters ◽

10.1109/lra.2019.2898093 ◽

2019 ◽

Vol 4 (2) ◽

pp. 1876-1883 ◽

Cited By ~ 3

Author(s):

Manuel Schmidt ◽

Carlo Manna ◽

Jan Holger Braun ◽

Christian Wissing ◽

Manoj Mohamed ◽

...

Keyword(s):

Lane Change ◽

Automated Vehicles ◽

Change Behavior

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A Novel On-Ramp Merging Strategy for Connected and Automated Vehicles Based on Game Theory

Journal of Advanced Transportation ◽

10.1155/2020/2529856 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Haigen Min ◽

Yukun Fang ◽

Runmin Wang ◽

Xiaochi Li ◽

Zhigang Xu ◽

...

Keyword(s):

Game Theory ◽

Travel Time ◽

Fuel Consumption ◽

Transportation Network ◽

Control Algorithms ◽

Traffic Density ◽

Automated Vehicles ◽

Network Efficiency ◽

Total Travel Time ◽

The Cost

Connected and automated vehicles (CAVs) have attracted much attention of researchers because of its potential to improve both transportation network efficiency and safety through control algorithms and reduce fuel consumption. However, vehicle merging at intersection is one of the main factors that lead to congestion and extra fuel consumption. In this paper, we focused on the scenario of on-ramp merging of CAVs, proposed a centralized approach based on game theory to control the process of on-ramp merging for all agents without any collisions, and optimized the overall fuel consumption and total travel time. For the framework of the game, benefit, loss, and rules are three basic components, and in our model, benefit is the priority of passing the merging point, represented via the merging sequence (MS), loss is the cost of fuel consumption and the total travel time, and the game rules are designed in accordance with traffic density, fairness, and wholeness. Each rule has a different degree of importance, and to get the optimal weight of each rule, we formulate the problem as a double-objective optimization problem and obtain the results by searching the feasible Pareto solutions. As to the assignment of merging sequence, we evaluate each competitor from three aspects by giving scores and multiplying the corresponding weight and the agent with the higher score gets comparatively smaller MS, i.e., the priority of passing the intersection. The simulations and comparisons are conducted to demonstrate the effectiveness of the proposed method. Moreover, the proposed method improved the fuel economy and saved the travel time.

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