A Fuzzy Logic Module to Estimate a Driver’s Fuel Consumption for Reality-Enhanced Serious Games

Massoud, Rana; Poslad, Stefan; Bellotti, Francesco; Berta, Riccardo; Mehran, Kamyar; Gloria, Alessandro De

doi:10.17083/ijsg.v5i4.266

Cited by 22 publications

(18 citation statements)

References 50 publications

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“…Peugeot 207 contains various parameters collected from cars, which are used to predict either the road surface or the traffic (two labels were considered in our studies: label_14: road surface and label_15: traffic) [48]. The EnviroCar dataset records various vehicular signals through the onboard diagnostic (OBDII) interface to the Controller Area Network (CAN) bus [49][50][51]. The air quality index (AQI) dataset measures air quality in Australia during a period of one year [52].…”

Section: Experimental Analysis and Resultsmentioning

confidence: 99%

Machine Learning on Mainstream Microcontrollers

Sakr

Bellotti

Berta

et al. 2020

Sensors

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This paper presents the Edge Learning Machine (ELM), a machine learning framework for edge devices, which manages the training phase on a desktop computer and performs inferences on microcontrollers. The framework implements, in a platform-independent C language, three supervised machine learning algorithms (Support Vector Machine (SVM) with a linear kernel, k-Nearest Neighbors (K-NN), and Decision Tree (DT)), and exploits STM X-Cube-AI to implement Artificial Neural Networks (ANNs) on STM32 Nucleo boards. We investigated the performance of these algorithms on six embedded boards and six datasets (four classifications and two regression). Our analysis—which aims to plug a gap in the literature—shows that the target platforms allow us to achieve the same performance score as a desktop machine, with a similar time latency. ANN performs better than the other algorithms in most cases, with no difference among the target devices. We observed that increasing the depth of an NN improves performance, up to a saturation level. k-NN performs similarly to ANN and, in one case, even better, but requires all the training sets to be kept in the inference phase, posing a significant memory demand, which can be afforded only by high-end edge devices. DT performance has a larger variance across datasets. In general, several factors impact performance in different ways across datasets. This highlights the importance of a framework like ELM, which is able to train and compare different algorithms. To support the developer community, ELM is released on an open-source basis.

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Section: Experimental Analysis and Resultsmentioning

confidence: 99%

Machine Learning on Mainstream Microcontrollers

Sakr

Bellotti

Berta

et al. 2020

Sensors

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“…When the transfer is optimized, the game is expected to be more effective in change of behaviour and attitude [3]. This inverse gamification approach is in line with the approach of realityenhanced serious games [4]. In both approaches user's real-world activities feed a digital serious game or gamified application.…”

Section: Introductionmentioning

confidence: 82%

Household Energy Conservation Intervention: a Game versus Dashboard Comparison

Fijnheer

Oostendorp²,

Veltkamp³

2019

IJSG

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This paper presents the results of a study, comparing a game versus a dashboard with respect to energy conservation in the household. In a pretest-posttest design, an empirical study tested whether change in attitude, knowledge, engagement and behaviour with respect to energy conservation in the household was different for participants playing Powersaver Game compared to a control condition where participants used an energy dashboard with the same content, but excluding game features. The aim of this game (developed using an iterative user-centered game design methodology) is to influence household energy consumption by means of electricity and gas usage in the long-term. The intervention time was at least 5 weeks and pre and post measures were based on 21 days intervals. All energy conservation activities that the application provides (e.g. washing clothes on low temperatures) take place in the real world and feedback is based on real time energy consumption. This real data into the game approach aims to optimize the transfer between the game world and the real world. Energy consumption significantly changed in the game condition compared to the control condition, and the difference between both conditions is more than 33% after the intervention. In the game condition, knowledge about energy conservation was significantly increased, although no significant differences in increase of attitude and engagement were found. We conclude that Powersaver Game is effective in transfer of energy conservation knowledge, which leads to energy saving behaviour on the long term. However, it cannot be concluded that playing the game leads to a greater change in attitude, probably because attitude scores of the participants were high from the start.

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“…Other factors (e.g., road-type, traffic conditions) do influence FC in a trip. However, for simplicity of data collection, we focused our research on these three variables because they are well known to and are directly controllable by the driver [57].…”

Section: Instant Driving Recommendationsmentioning

confidence: 99%

IoT Sensing for Reality-Enhanced Serious Games, a Fuel-Efficient Drive Use Case

Massoud

Berta

Poslad

et al. 2021

Sensors

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Internet of Things technologies are spurring new types of instructional games, namely reality-enhanced serious games (RESGs), that support training directly in the field. This paper investigates a key feature of RESGs, i.e., user performance evaluation using real data, and studies an application of RESGs for promoting fuel-efficient driving, using fuel consumption as an indicator of driver performance. In particular, we propose a reference model for supporting a novel smart sensing dataflow involving the combination of two modules, based on machine learning, to be employed in RESGs in parallel and in real-time. The first module concerns quantitative performance assessment, while the second one targets verbal recommendation. For the assessment module, we compared the performance of three well-established machine learning algorithms: support vector regression, random forest and artificial neural networks. The experiments show that random forest achieves a slightly better performance assessment correlation than the others but requires a higher inference time. The instant recommendation module, implemented using fuzzy logic, triggers advice when inefficient driving patterns are detected. The dataflow has been tested with data from the enviroCar public dataset, exploiting on board diagnostic II (OBD II) standard vehicular interface information. The data covers various driving environments and vehicle models, which makes the system robust for real-world conditions. The results show the feasibility and effectiveness of the proposed approach, attaining a high estimation correlation (R2 = 0.99, with random forest) and punctual verbal feedback to the driver. An important word of caution concerns users’ privacy, as the modules rely on sensitive personal data, and provide information that by no means should be misused.

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A Fuzzy Logic Module to Estimate a Driver’s Fuel Consumption for Reality-Enhanced Serious Games

Cited by 22 publications

References 50 publications

Machine Learning on Mainstream Microcontrollers

Machine Learning on Mainstream Microcontrollers

Household Energy Conservation Intervention: a Game versus Dashboard Comparison

IoT Sensing for Reality-Enhanced Serious Games, a Fuel-Efficient Drive Use Case

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