14,583 research outputs found

    Data fusion for driver behaviour analysis

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    A driver behaviour analysis tool is presented. The proposal offers a novel contribution based on low-cost hardware and advanced software capabilities based on data fusion. The device takes advantage of the information provided by the in-vehicle sensors using Controller Area Network Bus (CAN-BUS), an Inertial Measurement Unit (IMU) and a GPS. By fusing this information, the system can infer the behaviour of the driver, providing aggressive behaviour detection. By means of accurate GPS-based localization, the system is able to add context information, such as digital map information, speed limits, etc. Several parameters and signals are taken into account, both in the temporal and frequency domains, to provide real time behaviour detection. The system was tested in urban, interurban and highways scenarios.This work was supported by the Spanish Government through the CICYT project (TRA2013-48314-C3-1-R) and DGT project (SPID2015-01802) and by the company SERCORE Tech. S.L. through the project: “Proyecto de Viabilidad de la Comunicación entre el BUS CAN de un Vehículo Específico con un Dispositivo de Adquisición de Datos Móviles”. SERCORE provided invaluable support in the development of the communication technologies through the CAN BUS, presented in this paper

    Aggressive driving behaviour classification using smartphone's accelerometer sensor

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    Aggressive driving is the most common factor of road accidents, and millions of lives are compromised every year. Early detection of aggressive driving behaviour can reduce the risks of accidents by taking preventive measures. The smartphone's accelerometer sensor data is mostly used for driving behavioural detection. In recent years, many research works have been published concerning to behavioural analysis, but the state of the art shows that still, there is a need for a more reliable prediction system because individually, each method has it's own limitations like accuracy, complexity etc. To overcome these problems, this paper proposes a heterogeneous ensemble technique that uses random forest, artificial neural network and dynamic time wrapping techniques along with weighted voting scheme to obtain the final result. The experimental results show that the weighted voting ensemble technique outperforms to all the individual classifiers with average marginal gain of 20%

    Video surveillance for monitoring driver's fatigue and distraction

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    Fatigue and distraction effects in drivers represent a great risk for road safety. For both types of driver behavior problems, image analysis of eyes, mouth and head movements gives valuable information. We present in this paper a system for monitoring fatigue and distraction in drivers by evaluating their performance using image processing. We extract visual features related to nod, yawn, eye closure and opening, and mouth movements to detect fatigue as well as to identify diversion of attention from the road. We achieve an average of 98.3% and 98.8% in terms of sensitivity and specificity for detection of driver's fatigue, and 97.3% and 99.2% for detection of driver's distraction when evaluating four video sequences with different drivers

    Ubiquitous sensorization for multimodal assessment of driving patterns

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    Sustainability issues and sustainable behaviours are becoming concerns of increasing signi cance in our society. In the case of transportation systems, it would be important to know the impact of a given driving behaviour over sustainability factors. This paper describes a system that integrates ubiquitous mobile sensors available on devices such as smartphones, intelligent wristbands and smartwatches, in order to determine and classify driving patterns and to assess driving e ficiency and driver's moods. It first identi fies the main attributes for contextual information, with relevance to driving analysis. Next, it describes how to obtain that information from ubiquitous mobile sensors, usually carried by drivers. Finally, it addresses the multimodal assessment process which produces the analysis of driving patterns and the classi cation of driving moods, promoting the identifi cation of either regular or aggressive driving patterns, and the classi fication of mood types between aggressive and relaxed. Such an approach enables ubiquitous sensing of personal driving patterns across diff erent vehicles, which can be used in sustainability frameworks, driving alerts and recommendation systems.This work is part-funded by ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project FCOMP-01-0124-FEDER-028980 (PTDC/EEI-SII/1386/2012). It is also supported by a doctoral grant, SFRH/BD/78713/2011, issued by FCT in Portugal

    Assessing the Human Factor in Truck Driving

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    Human factors assessment techniques are commonly applied to a variety of workplaces to examine the nature of operations and how key functions are controlled operationally; however, these tools appear to overlook key aspects of truck driving, particularly the driver’s relationship to the driving experience. The fundamental issue is with the ability to completely decompose truck driving and accurately document the truck drivers working environment will be problematic. Therefore, to demonstrate how a truck driver moves between each series of sub-tasks will require a purpose-built assessment tool that that is both practical and relevant to truck driving
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