Connection of the SUMO Microscopic Traffic Simulator and the Unity 3D Game Engine to Evaluate V2X Communication-Based Systems

Olaverri-Monreal, Cristina; Errea-Moreno, Javier; Díaz-Álvarez, Alberto; Biurrun-Quel, Carlos; Serrano-Arriezu, Luis; Kuba, Markus

doi:10.3390/s18124399

Cited by 49 publications

(19 citation statements)

References 20 publications

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“…Comparing simulation 2 vs 6 (B � 1), the gain is 18.12%. Comparing simulation 2 vs 16 (B � 0), the gain is 30 � 6). Following this idea, we calculate the response gain comparing simulation 5 vs 14 (1 speed bumps each), simulation 11 vs 4 (2 speed bumps each), and simulation 1 vs 10 (3 speed bumps each).…”

Section: Methodsmentioning

confidence: 89%

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Impact of Buses, Taxis, Passenger Cars, and Traffic Infrastructure on Average Travel Speed

Carrillo-González

Ortega

Sandoval-Gutierrez

et al. 2021

Journal of Advanced Transportation

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It is known that many variables influence traffic, yet very little is known about the weight of each factor in the dynamics of traffic in cities of developing countries, in many cases due to their peculiar traffic regulations. In this work, we search for the variables that have the most significant impact on the average travel speed of three distinct types of vehicles: passenger cars, taxis, and buses. First, we developed a tool featuring algorithms that simulate ordinary overtaking and car-following behaviors, along with controls for setting vehicles’ actions, particularly buses’ and taxis’ stops. Then, we chose a particular zone to study, based on its common geometry and the particular traffic infrastructure (speed bumps, traffic lights, and bus stops) inside it. Later on, three experiments were carried out, with the following results. (1) Both the buses’ arrival frequency and curbside bus stops affect the passenger cars’ average travel speed. The buses’ response was affected by the bus bay and curbside bus stops. The buses’ speed tendency influenced neither the passenger cars’ nor buses’ response. (2) Taxis’ arrival frequency, stopping frequency, and speed tendency were found to influence the passenger cars’ response. Taxis’ response was altered by taxis’ speed tendency, while buses’ response was affected by taxis’ arrival frequencies. (3) The number of speed bumps, the arrival frequency of passenger cars, and their speed conditions (homogeneous and heterogeneous) affect the passenger cars’ response. We expect that the findings presented in this study, along with the recommendations made from the results, may pave the way for better road design public policies.

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Section: Methodsmentioning

confidence: 89%

“…We choose Unity Engine (used for traffic analysis in [29,30]) to develop a simulation tool (scripts and scenario). In the simulator, variables related to vehicles can be set, such as the taxis' stopping frequency and the buses' dwell time.…”

Section: Simulation Toolmentioning

confidence: 99%

Impact of Buses, Taxis, Passenger Cars, and Traffic Infrastructure on Average Travel Speed

Carrillo-González

Ortega

Sandoval-Gutierrez

et al. 2021

Journal of Advanced Transportation

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“…In order to validate the proposed decision-making model, we designed a simulation environment that replicates the scenario described in Section 1. To this end, we used the simulator for cooperative advanced driver assistance systems (ADAS) and automated vehicles, 3DCoAutoSim, which was developed to test intelligent transportation systems (ITS)-related applications and connects the game engine Unity 3D [22] with Simulation of Urban MObility (SUMO) [23] and ROS [24][25][26][27][28][29][30]. Details of the simulation framework are presented in the following subsection.…”

Section: Data Acquisition and Use Case Definition For Model Validationmentioning

confidence: 99%

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

Smirnov

Liu

Validi

et al. 2021

Sensors

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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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“…The simulation platform is linked to the Simulation of Urban Mobility (SUMO) package for micro traffic emulations and connected to ROS for architecture management and nodes handling [19]. The Traffic Control Interface as a Service library was implemented to perform the interaction between a driver-controlled vehicle and SUMO [20]. Research into VRUs has additionally been performed through pedestrian-to-vehicle and vehicle-topedestrian communication in a simulated 3D environment [21].…”

Section: Simulation Platformsmentioning

confidence: 99%

Chair for Intelligent Transportation Systems–Sustainable Transport Logistics 4.0

Lv¹

2021

IEEE Intell. Transport. Syst. Mag.

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Connection of the SUMO Microscopic Traffic Simulator and the Unity 3D Game Engine to Evaluate V2X Communication-Based Systems

Cited by 49 publications

References 20 publications

Impact of Buses, Taxis, Passenger Cars, and Traffic Infrastructure on Average Travel Speed

Impact of Buses, Taxis, Passenger Cars, and Traffic Infrastructure on Average Travel Speed

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

Chair for Intelligent Transportation Systems–Sustainable Transport Logistics 4.0

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