Roadrunner+: An Autonomous Intersection Management Cooperating with Connected Autonomous Vehicles and Pedestrians with Spillback Considered

Wang, I-Chih; Wen, Charles H.-P.; Jonathan, ChaoH.

doi:10.1145/3488246

Cited by 8 publications

(6 citation statements)

References 33 publications

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“…On the other hand, it also requires more communication effort to ensure information consistency, and reaching a global objective is more difficult (11,12). If human drivers, pedestrians, or bicyclists are considered as well, then a central controller is typically assumed, since there is no clear communication protocol with human road users (6,7,(15)(16)(17)(18).…”

Section: Traffic Coordinationmentioning

confidence: 99%

“…Zhu and Ukkusuri ( 22 ) propose to instead generate a matrix that indicates whether two paths intersect and includes the exact distances between conflict points. Then, two vehicles with conflicting movements are allowed in the intersection zone at the same time if sufficient spacing between these two vehicles at their conflict point can be guaranteed ( 17 , 22 , 23 ).…”

Section: Related Workmentioning

confidence: 99%

“…The presented results were very promising, but the disadvantages of FCFS-based control were apparent when considering large vehicle and pedestrian demand. Both Chen et al ( 16 ) and Wang et al ( 17 ) describe the integration of pedestrian right-of-way into optimization-based AIM. However, the studies do not include pedestrians in the simulation or provide limited results that, because of restrictive assumptions, do not allow for a comprehensive comparison with state-of-the-art TSC.…”

Section: Related Workmentioning

confidence: 99%

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Optimization-Based Intersection Control for Connected Automated Vehicles and Pedestrians

Niels

Bogenberger

Papageorgiou

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Traffic at larger or busier urban intersections is currently coordinated using traffic signals to prevent dangerous traffic situations and to regulate the flow of traffic. In future scenarios with 100% connected automated vehicles, conventional traffic signals could be replaced, and vehicles at intersections could be seamlessly coordinated via vehicle-to-vehicle and vehicle-to-infrastructure communication. In the past two decades, many such control strategies have been presented, commonly referred to as autonomous intersection management (AIM). In recent years, an evolution from simpler first come, first served to more sophisticated optimization-based AIM strategies can be observed. Optimization-based AIM can significantly improve capacity and reduce delays as compared to slot-based strategies and conventional traffic signal control (TSC). In addition, it allows for prioritizing road users. This paper is among the first to consider pedestrians in optimization-based AIM. The proposed approach consists of a signal-free vehicle control in combination with pedestrian signal phases that are fully integrated into the optimization problem. Since the communication range of the controller is limited in real-world applications, a rolling horizon scheme is presented and explained in detail. The presented strategy is implemented and evaluated using a microscopic traffic simulation framework. Results show that vehicle delays can be significantly reduced and vehicle capacity can be increased compared to fully actuated TSC, while pedestrian waiting times are comparable. In addition, focus is put on how vehicle and pedestrian delays can be balanced in the presented setup. Three different control parameters can be adjusted, which need to be tuned based on the considered demand scenario.

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Section: Traffic Coordinationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Optimization-Based Intersection Control for Connected Automated Vehicles and Pedestrians

Niels

Bogenberger

Papageorgiou

et al. 2023

Transportation Research Record: Journal of the Transportation R

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“…According to the inspired studies above, cooperative control for CAVs is executable. Several recent studies further investigate utilizing all lanes from the intersection (Chen et al., 2021; Ding et al., 2021; Khayatian et al., 2020; Luo et al., 2019; Sun et al., 2018; Wang et al., 2021). For example, Sun et al.…”

Section: Literature Reviewmentioning

confidence: 99%

Rolling‐horizon–based strategy of fully cooperative traffic under signalized intersections

Yao

Zhou

Xia

et al. 2022

Computer aided Civil Eng

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Dedicated left-turn lanes are traditionally used at intersections. This practice may not be optimal where heavy traffic exists from multiple directions. As is well known, the capacity can be increased if vehicles are grouped in the same direction in advance, but the additional infrastructure is usually needed, such as presignal systems. Fortunately, under the development of connected and automated vehicle (CAV) technology, the presignal system can be achieved by coordinating vehicles directly. To this end, we establish a modeling framework and management strategies for pure CAV traffic to achieve direction-based grouping. A rolling time-horizon-based sorting strategy is developed to achieve real-time coordination under uncertain vehicle arrivals. A series of numerical experiments have shown that the improvement of the proposed sorting strategy is significant and robust compared with traditional dedicated left-turn lane intersections without coordination, especially when the traffic is busy.

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“…Simulations of autonomous and non-autonomous fleets: Wang et al [18] extend an approach of Autonomous Intersection Management at an intersection with the focus on connected AVs by considering different challenges such as pedestrian road crossings. Vakayil et al [17] describe a model for the allocation of user demand between an autonomous mobility-ondemand-system and mass transit.…”

Section: Related Workmentioning

confidence: 99%

SUMO4AV: An Environment to Simulate Scenarios for Shared Autonomous Vehicle Fleets with SUMO Based on OpenStreetMap Data

et al. 2022

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In the past years the progress in the development of autonomous vehicles has increased tremendously. There are still technical, infrastructural and regulative obstacles which need to be overcome. However, there is a clear consent among experts that fully autonomous vehicles (level 5 of driving automation) will become reality in the coming years or at least in the coming decades. When fully autonomous vehicles are widely available for a fair trip price and when they can easily be utilized, a big shift from privately owned cars to carsharing will happen. On the one hand, this shift can bring a lot of chances for cities like the need of less parking space. But on the other hand, there is the risk of an increased traffic when walking or biking trips are substituted by trips with shared autonomous vehicle fleets. While the expected social, ecological and economical impact of widely used shared autonomous vehicle fleets is tremendous, there are hardly any scientific studies or data available for the effects on cities and municipalities. The research project KI4ROBOFLEET addressed this demand. A result of the project was SUMO4AV, a simulation environment for shared autonomous vehicle fleets, which we present in this paper. This simulation tool is based on SUMO, an open-source traffic simulation package. SUMO4AV can support city planners and carsharing companies to evaluate the chances and risks of running shared autonomous fleets in their local environment with their specific infrastructure. At its core it comprises the mapping of OpenStreetMap1 entities into SUMO objects as well as a Scenario Builder to create different operation scenarios for autonomous driving. Additionally, the simulation tool offers a recursive execution with different fleet sizes and optimization strategies evaluated by economic and ecologic parameters. As evaluation of the toolset a simulation of an ordinary scenario was performed. The workflow to simulate the scenario for shared autonomous vehicle fleets was successfully processed with the SUMO4AV environment.

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Roadrunner+: An Autonomous Intersection Management Cooperating with Connected Autonomous Vehicles and Pedestrians with Spillback Considered

Cited by 8 publications

References 33 publications

Optimization-Based Intersection Control for Connected Automated Vehicles and Pedestrians

Optimization-Based Intersection Control for Connected Automated Vehicles and Pedestrians

Rolling‐horizon–based strategy of fully cooperative traffic under signalized intersections

SUMO4AV: An Environment to Simulate Scenarios for Shared Autonomous Vehicle Fleets with SUMO Based on OpenStreetMap Data

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