A Vehicle-in-the-Loop Emulation Platform for Demonstrating Intelligent Transportation Systems

Griggs, Wynita M.; Ordóñez-Hurtado, Rodrigo H.; Russo, Giovanni; Shorten, Robert

doi:10.1007/978-3-319-91569-2_7

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…We are building, from the example of Section V, a hardware-in-the-loop (HiL) demo. In the demo, Algorithm 1 will be deployed on a real car, which will crowdsource a route from a large number of contributing cars generated by the HiL infrastructure of [20].…”

Section: Discussionmentioning

confidence: 99%

On the crowdsourcing of behaviors for autonomous agents

Russo

2020

Preprint

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This paper is concerned with the problem of designing, from data, agents that are able to craft their behavior from a set of contributors in order to fulfill some agent-specific task. This is not necessarily known to the contributors. After formalizing this crowdsourcing process as a control problem, we present a result to synthesize behaviors from the information made available by the contributors. The result is turned into an algorithm and the theoretical findings are complemented via an example.

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Section: Discussionmentioning

confidence: 99%

On the crowdsourcing of behaviors for autonomous agents

Russo

2020

Preprint

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“…To improve the testing accuracy, several studies conducted VIL testing in an enclosed field, meanwhile reproducing the virtual world according to the construction of the closed field, especially the road networks. For example, Tettamanti et al, [78] Griggs et al, [79] and Feng et al all replicated the road layouts of closed campuses in microtraffic flow simulation software (e.g., SUMO, VISSIM) [80][81][82] and combined with simulated large-scale traffic flows to perform VIL for testing some functions (e.g., a traffic jam assist function, a speed advisory system, the decisionmaking subsystem, and the communication ability of AVs). Solmaz et al constructed a comprehensive field-based VIL testing platform for testing a traffic management scheme of cooperative vehicle infrastructure systems.…”

Section: Road Condition Simulation In Vilmentioning

confidence: 99%

A Survey on Testbench‐Based Vehicle‐in‐the‐Loop Simulation Testing for Autonomous Vehicles: Architecture, Principle, and Equipment

Cheng,

Wang,

Zhao

et al. 2024

Advanced Intelligent Systems

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Autonomous vehicles (AVs) must be thoroughly tested to ensure safety and reliability before marketing. Simulation‐based testing has gained widespread recognition as the essential approach for AV testing by providing sufficient testing scenarios in the virtual environment. Vehicle‐in‐the‐loop (VIL) simulation has the ability to perform comprehensive tests and validations for the AVs’ overall behaviors while keeping significant testing accuracy and efficiency through the combination of the virtual scenarios and the physical AV. This article provides an overview of representative studies on testbench‐based VIL simulation testing for AVs, mainly focusing on utilizing testbenches to simulate realistic road conditions, and using physical signal stimulation methods and related equipment to generate sensors’ physical signals. This article first summarizes current AV testing studies, identifying existing issues and flaws of the state‐of‐the‐art methods and tools. Afterward, the testbench‐based VIL is addressed around architecture, principles, advantages, and characteristics. Then, the road condition simulation and the sensor physical signal generation in VIL are discussed in depth from structure, principle, and corresponding advanced equipment. Finally, research gaps between cutting‐edge technologies and AV testing applications in industrialization are identified to facilitate future research in this direction.

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“…We now use Algorithm 1 to design an intelligent parking system for connected cars and validate the results via insilico and in-vivo experiments. For the latter set of experiments, Algorithm 1 is deployed on a real car and validation is performed via an hardware-in-the-loop (HiL) platform inspired from [20]. Before reporting the results, we describe the validation scenarios and the experimental set-up.…”

Section: Using Algorithm 1 To Design An Intelligent Parking Systemmentioning

confidence: 99%

Optimal Decision-Making for Autonomous Agents via Data Composition

Emiland¹,

Lamberti²,

Russo³

2023

Preprint

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We consider the problem of designing agents able to compute optimal decisions by composing data from multiple sources to tackle tasks involving: (i) tracking a desired behavior while minimizing an agent-specific cost; (ii) satisfying safety constraints. After formulating the control problem, we show that this is convex under a suitable assumption and find the optimal solution. The effectiveness of the results, which are turned in an algorithm, is illustrated on a connected cars application via in-silico and in-vivo experiments with real vehicles and drivers. All the experiments confirm our theoretical predictions and the deployment of the algorithm on a real vehicle shows its suitability for in-car operation.

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A Vehicle-in-the-Loop Emulation Platform for Demonstrating Intelligent Transportation Systems

Cited by 6 publications

References 23 publications

On the crowdsourcing of behaviors for autonomous agents

On the crowdsourcing of behaviors for autonomous agents

A Survey on Testbench‐Based Vehicle‐in‐the‐Loop Simulation Testing for Autonomous Vehicles: Architecture, Principle, and Equipment

Optimal Decision-Making for Autonomous Agents via Data Composition

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