Evaluating connected and autonomous vehicles using a hardware-in-the-loop testbed and a living lab

Shao, Yunli; Zulkefli, Mohd Azrin Mohd; Sun, Zongxuan; Huang, Peter

doi:10.1016/j.trc.2019.03.010

Cited by 43 publications

(17 citation statements)

References 20 publications

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“…Working with the compact form (20) where acceleration errors are considered _ g ¼ _ g cp À _ g, and by following the same procedure as in the previous case has:…”

Section: Designing the Control Algorithmmentioning

confidence: 99%

“…The following expression is taken into account for the identification of dynamic model parameters: By solving the operations of the matrices (20) and grouping the dynamic parameters, the linear parameterization in (26) required for implementation in the identification scheme (see Fig. 6).…”

Section: Identification and Validationmentioning

confidence: 99%

“…One of the problems that exists in the field of robotics, is directly being able to experience control algorithms in which the necessary hardware is not available because of its high cost or its limitation for the number of tests, for which one of the alternatives for industrial automation processes is hardware in the loop (HIL). This is a technique in which a real time simulation environment is developed to test the behavior of its control algorithms without physical prototypes [19,20]. It is widely used in the automotive industry to test vehicle dynamics and drivers [21], also used in aerospace and industrial automation.…”

Section: Introductionmentioning

confidence: 99%

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Control of an Omnidirectional Robot Based on the Kinematic and Dynamic Model

Gallo

Paste

Ortiz

et al. 2021

Communications in Computer and Information Science

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This work focuses on the proposal for the implementation and evaluation of control algorithms for the monitoring of autonomous trajectories based on the kinematic and dynamic model of an omnidirectional robotic platform in four wheel configuration type mecanum also built for the process of identification and validation of the dynamic parameters obtained from the mathematical model by means of an identification algorithm, the evaluation of these control algorithms is carried out experimentally on the Robotic Platform in real four-wheel omnidirectional configuration with which it also allows to evaluate the operation of the Hardware in the Loop control scheme (HIL), a technique that constitutes the untimely connection of external hardware with computer equipment allowing to simulate in real time the behavior of the robotic system with omnidirectional traction in four-wheel configuration type mecanum. Subsequently the data resulting from the Hardware in the Loop (HIL) control scheme will be compared to the data obtained in the experimental test for corresponding validation.

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“…Working with the compact form (20) where acceleration errors are considered _ g ¼ _ g cp À _ g, and by following the same procedure as in the previous case has:…”

Section: Designing the Control Algorithmmentioning

confidence: 99%

Section: Identification and Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Control of an Omnidirectional Robot Based on the Kinematic and Dynamic Model

Gallo

Paste

Ortiz

et al. 2021

Communications in Computer and Information Science

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“…The identification of anomalous driving as means of warning information for drivers can be useful in connected vehicle applications. While research on fuel consumption and pollutant emissions in connected vehicles is welldocumented (Garceau et al, 2013;Liu et al, 2015;Astarita et al, 2019;Shao et al, 2019), few studies have addressed the relationship between vehicular jerk and fuel consumption in rural, urban and highway roads (Huang & Peng, 2017;Zhang et al, 2020;Fernandes et al, 2021), and exhaust emissions in suburban areas (Fernandes et al, 2020).…”

Section: Introduction and Research Objectivesmentioning

confidence: 99%

Micro-analysis of a single vehicle driving volatility and impacts on emissions for intercity corridors

Ferreira

Fernandes

Bahmankhah

et al. 2021

International Journal of Sustainable Transportation

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A deep understanding of driver behavior is an important step to improve road safety and environmental performance. Volatility can be defined as the extent of variations in driving, which can be characterized by accelerations/braking, lane change, and unusual high speed for roadways conditions. There is a lack of knowledge on what concerns the relationship between driver's volatility and exhaust emissions and how driving volatility can be used as safety eco-indicator. This article explores a driving volatility concept for assessing tailpipe emissions and driving behavior classification. For that purpose, an empirical approach that combined vehicle activity and emission rates for light duty vehicles was used. Field measurements were collected from four probe vehicles in one partly urban/rural, and two highway routes using Portable Emission Measurement Systems, Global Positioning System receivers, and On-board Diagnostic scan tool, to measure real-world tailpipe emissions, position and engine activity data. Accelerationbased parameters, including relative positive acceleration and mean of positive acceleration, acceleration, vehicular jerk, and power demand thresholds were used to detect differences in emissions for different driving styles. Results indicated that vehicular jerk impacted carbon dioxide and nitrogen oxides per unit distance regardless of driving style and route type, especially from negative to null jerk values and during positive accelerations. There is potential to incorporate the analyzed thresholds into a driver decision support algorithm by considering safety and environmental aspects through warning messages.

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“…Other HIL applications are autonomous vehicles [33][34][35], motor control [36][37][38] and the traction system of a railway [39]. In addition, it is used in the design of PVSs.…”

Section: Introductionmentioning

confidence: 99%

Hardware in the Loop Platform for Testing Photovoltaic System Control

et al. 2020

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The hardware in the loop (HIL) technique allows you to reproduce the behavior of a dynamic system or part of it in real time. This quality makes HIL a useful tool in the controller validation process and is widely used in multiple areas including photovoltaic systems (PVSs). This study presents the development of an HIL system to emulate the behavior of a PVS that includes a photovoltaic panel (PVP) and a DC-DC boost converter connected in series. The emulator was embedded into an NI-myRIO development board that operates with an integration time of 10 µs and reproduces the behavior of the real system with a mean percent error of 2.0478%, compared to simulation results. The implemented emulator is proposed as a platform for the validation of control systems. With it, the experimental stage is carried out on two controllers connected to the PVS without having the real system and allowing to emulate different operating conditions. The first controller is based on the Hill Climbing algorithm for the maximum power point tracking (MPPT), the second is a proportional integral (PI) controller for voltage control. Both controllers generate settling times of less than 3 s; the MPPT controller generates variations in the output in steady state inherent to the algorithm used. For both cases, the comparison of the experimental results with those obtained through software simulation show that the platform fulfills its usefulness when evaluating control systems.

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Evaluating connected and autonomous vehicles using a hardware-in-the-loop testbed and a living lab

Cited by 43 publications

References 20 publications

Control of an Omnidirectional Robot Based on the Kinematic and Dynamic Model

Control of an Omnidirectional Robot Based on the Kinematic and Dynamic Model

Micro-analysis of a single vehicle driving volatility and impacts on emissions for intercity corridors

Hardware in the Loop Platform for Testing Photovoltaic System Control

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