Co-Simulation Platform for Modeling and Evaluating Connected and
                    Automated Vehicles and Human Behavior in Mixed Traffic

Zhao, Xuanpeng; Liao, Xishun; Wang, Ziran; Wu, Guoyuan; Barth, Matthew; Han, Kyungtae; Tiwari, Prashant

doi:10.4271/12-05-04-0025

Cited by 16 publications

(8 citation statements)

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“…They use a combination of SUMO and Unity to provide a lightweight solution but note that this work lacks usability and user requirement studies. According to our experience using such federated platforms for urban mobility solutions, the modelling and requirements analysis are very time-consuming and necessitate extensive skills in simulation modelling, as well as experienced field experts for the validation Zhao et al (2022) Urban logistics sim guide presents work on how to use federated platforms for overcoming the challenge of not having the possibility to collect real-world data.…”

Section: Literature Reviewmentioning

confidence: 99%

From novice to expert: advancing step-by-step simulation guideline for urban logistics with an open-source simulation tool

Baalsrud Hauge,

Jeong

2024

IJLM

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PurposeThis research analyses challenges faced by users at various levels in planning and designing participatory simulation models of cities. It aims to identify issues that hinder experts from maximising the effectiveness of the SUMO tool. Additionally, evaluating current methods highlights their strengths and weaknesses, facilitating the use of participatory simulation advantages to address these issues. Finally, the presented case studies illustrate the diversity of user groups and emphasise the need for further development of blueprints.Design/methodology/approachIn this research, action research was used to assess and improve a step-by-step guideline. The guideline's conceptual design is based on stakeholder analysis results from those involved in developing urban logistics scenarios and feedback from potential users. A two-round process of application and refinement was conducted to evaluate and enhance the guideline's initial version.FindingsThe guidelines still demand an advanced skill level in simulation modelling, rendering them less effective for the intended audience. However, they have proven beneficial in a simulation course for students, emphasising the importance of developing accurate conceptual models and the need for careful implementation.Originality/valueThis paper introduces a step-by-step guideline designed to tackle challenges in modelling urban logistics scenarios using SUMO simulation software. The guideline's effectiveness was tested and enhanced through experiments involving diverse groups of students, varying in their experience with simulation modelling. This approach demonstrates the guideline's applicability and adaptability across different skill levels.

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Section: Literature Reviewmentioning

confidence: 99%

From novice to expert: advancing step-by-step simulation guideline for urban logistics with an open-source simulation tool

Baalsrud Hauge,

Jeong

2024

IJLM

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“…AI can be programmed to emulate the behaviour of the traffic flow information, considering factors such as traffic rules, driving styles, and responses to various road conditions. By incorporating traffic flow information that dictates realistic behaviours for these agents, a complex network of human-driven traffic can be generated [65,66]. Unity's physics engine and NavMesh system can also aid in managing the movement and interaction of these agents, simulating real-world traffic scenarios, and providing a robust environment for the comprehensive testing and validation of AVs in diverse and dynamic traffic conditions [67].…”

Section: Traffic Flow Integrationmentioning

confidence: 99%

SLAV-Sim: A Framework for Self-Learning Autonomous Vehicle Simulation

Crewe,

Humnabadkar,

Liu

et al. 2023

Sensors

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With the advent of autonomous vehicles, sensors and algorithm testing have become crucial parts of the autonomous vehicle development cycle. Having access to real-world sensors and vehicles is a dream for researchers and small-scale original equipment manufacturers (OEMs) due to the software and hardware development life-cycle duration and high costs. Therefore, simulator-based virtual testing has gained traction over the years as the preferred testing method due to its low cost, efficiency, and effectiveness in executing a wide range of testing scenarios. Companies like ANSYS and NVIDIA have come up with robust simulators, and open-source simulators such as CARLA have also populated the market. However, there is a lack of lightweight and simple simulators catering to specific test cases. In this paper, we introduce the SLAV-Sim, a lightweight simulator that specifically trains the behaviour of a self-learning autonomous vehicle. This simulator has been created using the Unity engine and provides an end-to-end virtual testing framework for different reinforcement learning (RL) algorithms in a variety of scenarios using camera sensors and raycasts.

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“…A common approach toward these arising aspects is to narrow the complexity of the numerical modeling through the isolation of the main macro-domains, and thus solving the different phenomena through dedicated languages, tools, and algorithms [2]. However, in applications where the strict interdependence between the different physical domains and scales cannot be decoupled the introduction of a complete multidomain co-simulation architecture is required necessary [3]- [5]. Matlab/Simulink® certainly represents the most advanced graphical programming environment for the simulation of dynamic systems, being developed for the analysis of multipurpose and general nature problems, the proposed co-simulation algorithm exploits the capabilities of a system-level environment in order to integrate under a single dynamic model, whose core is designed into a multibody model, several device-level simulators implemented respectively for the electronic and magnetostatic analysis through the equivalent circuit methodology (ECM) and the 3D finite volume modeling (3DFVM).…”

Section: Introductionmentioning

confidence: 99%

Electro-magneto-mechanical co-simulation in strongly coupled dynamic applications

Reato,

Ricci,

Cinquemani

et al. 2024

Preprint

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Multibody-based design methodologies are techniques that have seen an increasing use, both in industry and science, in the last few decades. This boost was made possible by the more and more performing computers and the increasingly reliable simulation software. Normally the analysis of large and complex mechanical systems tends to be decoupled to isolate the main macro phenomena and thus allow the models to be simulated by different techniques, tools, and algorithms. All these aspects highlight the difficulties of analysis of coupled heterogeneous systems. The strict interdependence between the different physical domains or different scales of analysis has clearly increased the difficulties in multibody prediction capabilities. An interesting new approach is represented by the multi-physics co-simulations technique where the global model of a coupled system is solved through the inter-exchange of effort and flows variables coming from events of different natures. The paper intends to propose a novel co-simulation architecture for the integration of the magnetic and analog electronic domains into the mechanic one through the implementation among the others of a Matlab-Python based bi-directional communication routine for the interexchange of effort and flow independent variables between the master model (multibody-based) and the equivalent circuit model developed through Spice® as well as the possibility to integrate the dynamic analysis of the 3D electro-magnetic field through the open package ESRF Radia®. To highlight the potentiality of the multi-domain architecture and to validate the results obtained from the co-simulation a comparison with the experimental results of a micro electro-magnetic actuated drive [1] are proposed.

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Co-Simulation Platform for Modeling and Evaluating Connected and Automated Vehicles and Human Behavior in Mixed Traffic

Cited by 16 publications

References 0 publications

From novice to expert: advancing step-by-step simulation guideline for urban logistics with an open-source simulation tool

From novice to expert: advancing step-by-step simulation guideline for urban logistics with an open-source simulation tool

SLAV-Sim: A Framework for Self-Learning Autonomous Vehicle Simulation

Electro-magneto-mechanical co-simulation in strongly coupled dynamic applications

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