Driving Behavior Signals and Machine Learning: A Personalized Driver Assistance System

Martínez, María Victoria Luna; Campo, I. del; Echanobe, Javier; Basterretxea, Koldo

doi:10.1109/itsc.2015.470

Cited by 39 publications

(17 citation statements)

References 15 publications

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“…The method introduced here, like in [2,[20][21][22][23][24][25][26][27], uses only performance-based attributes, because the variables can be obtained using the data from the available in modern vehicles sensors [19]. However, the method described here, in comparison with other performance-based approaches and with all the works mentioned in Section II, is able to measure a level of DD.…”

Section: Discussionmentioning

confidence: 99%

“…The double-class DD classifier based on GMM was described in [23]. Vehicle dynamics and driving performance results were engaged in the DD detection by an extreme learning machine algorithm in [24] and SVMin [2]. In [25], authors presented the driver behavior prediction with dynamic BN based on preliminary collected data.…”

Section: Related Work and Problem Statementmentioning

confidence: 99%

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Detection and Evaluation of Driver Distraction Using Machine Learning and Fuzzy Logic

Aksjonov

Nedoma

Vodovozov

et al. 2019

IEEE Trans. Intell. Transport. Syst.

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In addition to vehicle control, drivers often perform secondary tasks that impede driving. Reduction of driver distraction is an important challenge for the safety of intelligent transportation systems. In this paper, a methodology for detection and evaluation of driver distraction while performing secondary tasks is described and an appropriate hardware and software environment is offered and studied. The system includes a model of normal driving, a subsystem for measuring the errors from the secondary tasks, and a module for total distraction evaluation. A new machine learning algorithm defines driver performance in lane keeping and speed maintenance on a specific road segment. To recognize the errors, a method is proposed, which compares normal driving parameters with ones obtained while conducting a secondary task. To evaluate distraction, an effective fuzzy logic algorithm is used. To verify the proposed approach, a case study with driver-in-the-loop experiments was carried out, in which participants performed the secondary task, namely chatting on a cell phone. The results presented in this research confirm its capability to detect and to precisely measure a level of abnormal driver performance.

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Section: Discussionmentioning

confidence: 99%

Section: Related Work and Problem Statementmentioning

confidence: 99%

Detection and Evaluation of Driver Distraction Using Machine Learning and Fuzzy Logic

Aksjonov

Nedoma

Vodovozov

et al. 2019

IEEE Trans. Intell. Transport. Syst.

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“…The signal choice depends, however, on the desired application, which is crucial as any further processing of that data will entirely depend on that choice. Therefore, due to the plurality of applications, ranging from ADAS personalization [39] or driving correction for safety and comfort improvement [40] to fuel economy advice, there is no recommended set of parameters. Thus, for identifying aggressive drivers, high accelerations must be monitored.…”

Section: Driving Style Characterization In Car-following Scenariosmentioning

confidence: 99%

An FPGA-Based Neuro-Fuzzy Sensor for Personalized Driving Assistance

Mata-Carballeira

Gutiérrez-Zaballa

Campo

et al. 2019

Sensors

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Advanced driving-assistance systems (ADAS) are intended to automatize driver tasks, as well as improve driving and vehicle safety. This work proposes an intelligent neuro-fuzzy sensor for driving style (DS) recognition, suitable for ADAS enhancement. The development of the driving style intelligent sensor uses naturalistic driving data from the SHRP2 study, which includes data from a CAN bus, inertial measurement unit, and front radar. The system has been successfully implemented using a field-programmable gate array (FPGA) device of the Xilinx Zynq programmable system-on-chip (PSoC). It can mimic the typical timing parameters of a group of drivers as well as tune these typical parameters to model individual DSs. The neuro-fuzzy intelligent sensor provides high-speed real-time active ADAS implementation and is able to personalize its behavior into safe margins without driver intervention. In particular, the personalization procedure of the time headway (THW) parameter for an ACC in steady car following was developed, achieving a performance of 0.53 microseconds. This performance fulfilled the requirements of cutting-edge active ADAS specifications.

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“…In [12], [13], the same algorithm was combined with ANN. Other computational intelligence and statistical learning theory approaches, like SVM [14], fuzzy logic [15], ANN with SVM [16], [17] were also applied for induced by secondary activity DD classification.…”

Section: Introductionmentioning

confidence: 99%

A Method for Detection and Evaluation of Driver Distraction Induced by In-Vehicle Information Systems

Aksjonov

Nedoma

Vodovozov

et al. 2018

IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society

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Driving Behavior Signals and Machine Learning: A Personalized Driver Assistance System

Cited by 39 publications

References 15 publications

Detection and Evaluation of Driver Distraction Using Machine Learning and Fuzzy Logic

Detection and Evaluation of Driver Distraction Using Machine Learning and Fuzzy Logic

An FPGA-Based Neuro-Fuzzy Sensor for Personalized Driving Assistance

A Method for Detection and Evaluation of Driver Distraction Induced by In-Vehicle Information Systems

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