Evaluation of the Accuracy of ADAS Module Readings Based on an Analysis of the Transient Supply Current and Neural Network Application

Grzechca, Damian; Rybka, Paweł; Paszek, Krzysztof

doi:10.5755/j01.eie.24.3.20944

Cited by 3 publications

(6 citation statements)

References 15 publications

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“…Effective traffic management schemes that improve traffic flow and utilization of road infrastructure are very important at present times [19,20]. Magnetic sensors have advantages over other non-contact based sensing techniques because the weather conditions do not affect the efficiency of vehicle detection [21,22]. In the literature, the following features are indicated: the low price of detectors and simplicity of their installation.…”

Section: Discussionmentioning

confidence: 99%

Vehicle Speed and Length Estimation Errors Using the Intelligent Transportation System with a Set of Anisotropic Magneto-Resistive (AMR) Sensors

Markevičius

Navikas

Idźkowski

et al. 2019

Sensors

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Seeking an effective method for estimating the speed and length of a car is still a challenge for engineers and scientists who work on intelligent transportation systems. This paper focuses on a self-developed system equipped with four anisotropic magneto-resistive (AMR) sensors which are placed on a road lane. The piezoelectric polyvinylidene fluoride (PVDF) sensors are also mounted and used as a reference device. The methods applied in the research are well-known: the fixed threshold-based method and the adaptive two-extreme-peak detection method. However, the improved accuracy of estimating the length by using one of the methods, which is based on computing the difference quotient of a time-discrete signal (representing the changes in the magnitude of the magnetic field of the Earth), is observed. The obtained results, i.e., the speed and length of a vehicle, are presented for various values of the increment Δn used in numerical differentiation of magnetic field magnitude data. The results were achieved in real traffic conditions after analyzing a data set M = 290 of vehicle signatures. The accuracy was evaluated by calculating MAE (Mean Absolute Error), RMSE (Root Mean Squared Error) for different classes of vehicles. The MAE is within the range of 0.52 m–1.18 m when using the appropriate calibration factor. The results are dependent on the distance between sensors, the speed of vehicle and the signal processing method applied.

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Section: Discussionmentioning

confidence: 99%

Vehicle Speed and Length Estimation Errors Using the Intelligent Transportation System with a Set of Anisotropic Magneto-Resistive (AMR) Sensors

Markevičius

Navikas

Idźkowski

et al. 2019

Sensors

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“…, 2018) in the electrical industry. In the automobile industry, neural networks are used in Artificial Intelligence (AI) systems for self-driving cars (Grzechca et al. , 2018).…”

Section: Rq2: What Are the Commonly Used Methods Used For Anomaly Det...mentioning

confidence: 99%

“…Moreover, researchers developed methods that use single sensor data to discover errors in numerous spots, reducing the overall inspection work in defect identification (Pfingstl et al, 2020). More advanced systems like driver assistance systems make use of sensor readings to reduce the number of accidents by monitoring (Grzechca et al, 2018). Manco et al (2017) developed a predictive maintenance system that predicts door failures in metro trains.…”

Section: Fault Detection Techniquesmentioning

confidence: 99%

“…With the aid of neural networks, it is possible to anticipate motor oil conditions (Rodrigues et al, 2020;Ullah et al, 2017) and photovoltaic system maintenance (Benedetti et al, 2018) in the electrical industry. In the automobile industry, neural networks are used in Artificial Intelligence (AI) systems for self-driving cars (Grzechca et al, 2018).…”

Section: Jqme 292mentioning

confidence: 99%

“…, 2020). More advanced systems like driver assistance systems make use of sensor readings to reduce the number of accidents by monitoring (Grzechca et al. , 2018).…”

Section: Rq1: What Are the Domains That Use Anomaly Detection For Pre...mentioning

confidence: 99%

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Review of fault detection techniques for predictive maintenance

Divya

Bhasi

Kumar

2022

JQME

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PurposeThis study aims to bring awareness to the developing of fault detection systems using the data collected from sensor devices/physical devices of various systems for predictive maintenance. Opportunities and challenges in developing anomaly detection algorithms for predictive maintenance and unexplored areas in this context are also discussed.Design/methodology/approachFor conducting a systematic review on the state-of-the-art algorithms in fault detection for predictive maintenance, review papers from the years 2017–2021 available in the Scopus database were selected. A total of 93 papers were chosen. They are classified under electrical and electronics, civil and constructions, automobile, production and mechanical. In addition to this, the paper provides a detailed discussion of various fault-detection algorithms that can be categorised under supervised, semi-supervised, unsupervised learning and traditional statistical method along with an analysis of various forms of anomalies prevalent across different sectors of industry.FindingsBased on the literature reviewed, seven propositions with a focus on the following areas are presented: need for a uniform framework while scaling the number of sensors; the need for identification of erroneous parameters; why there is a need for new algorithms based on unsupervised and semi-supervised learning; the importance of ensemble learning and data fusion algorithms; the necessity of automatic fault diagnostic systems; concerns about multiple fault detection; and cost-effective fault detection. These propositions shed light on the unsolved issues of predictive maintenance using fault detection algorithms. A novel architecture based on the methodologies and propositions gives more clarity for the reader to further explore in this area.Originality/valuePapers for this study were selected from the Scopus database for predictive maintenance in the field of fault detection. Review papers published in this area deal only with methods used to detect anomalies, whereas this paper attempts to establish a link between different industrial domains and the methods used in each industry that uses fault detection for predictive maintenance.

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Bulletin of the Polish Academy of Sciences: Technical Sciences

Hanzel,

Paszek,

Grzechca

2020

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The paper presents a concept of the vehicle/road infrastructure in vehicle-to-infrastructure (V2I) communication for tagging and informing vehicles about the surrounded environment. A frame analysis and the influence of the data packet size on Ultra-wideband (UWB) were investigated. The authors have determined the distance that could be traveled by a vehicle at the given speed in relation to the amount of data that has to be transmitted during the ranging procedure. The authors propose a data frame format (using the IEEE 802.15.4a protocol) for coding/encoding the information about the road infrastructure efficiently during the positioning procedure. It affects to minimum the time that is required to exchange messages during the ranging and communication process. The whole system is an efficient and reliable element that enhances/ extends the existing components of advanced driver-assistance systems (ADAS), which will facilitate validation of the information obtained from devices such as lidar, radar or video. The impact of the transmitted payload to the distance traveled by car opens the door to future research on the possibility of implementing efficient vehicle-to-vehicle (V2V) communication for autonomous driving or and other smart city solutions.

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Evaluation of the Accuracy of ADAS Module Readings Based on an Analysis of the Transient Supply Current and Neural Network Application

Cited by 3 publications

References 15 publications

Vehicle Speed and Length Estimation Errors Using the Intelligent Transportation System with a Set of Anisotropic Magneto-Resistive (AMR) Sensors

Vehicle Speed and Length Estimation Errors Using the Intelligent Transportation System with a Set of Anisotropic Magneto-Resistive (AMR) Sensors

Review of fault detection techniques for predictive maintenance

Bulletin of the Polish Academy of Sciences: Technical Sciences

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