Detecting driver drowsiness using feature-level fusion and user-specific classification

Jo, Jaeik; Lee, Sung Joo; Park, Kang Ryoung; Kim, Ig-Jae; Kim, Jaihie

doi:10.1016/j.eswa.2013.07.108

Cited by 142 publications

(73 citation statements)

References 38 publications

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“…This technology was designed and tested based on the low cost sensor, although it could be adapted to any 3D sensor device, such as stereo camera, or time of flight cameras presented before. The high performance and the 3D information based with low cost nat ure of the sensor represented an important advance in relation to other similar works, such as those presented by Jo et al (2014) or Flores, Armingol, and Escalera (2009).…”

Section: Driver Safety Through Facial Recognitionmentioning

confidence: 99%

“…A different approach, but also related with traf fic security is presented on Abellán, López, and De Oña (2013) where an algorithm to identify the severity of the accidents based on decision trees is presented. Finally driver drowsiness is ana lyzed based on computer vision algorithms and biological mea surements in Jo, Lee, Park, Kim, and Kim (2014). All these applications represent important advances in the latest years in the expert system field related to the road safety and intelligent transport systems topics.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

IVVI 2.0: An intelligent vehicle based on computational perception

Martín

García

Musleh

et al. 2014

Expert Systems with Applications

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This is a postprint version of the following published document: Martín, D., et al. (2014) a b s t r a c tThis paper presents the IVVI 2.0 a smart research platform to foster intelligent systems in vehicles. Com putational perception in intelligent transportation systems applications has advantages, such as huge data from vehicle environment, among others, so computer vision systems and laser scanners are the main devices that accomplish this task. Both have been integrated in our intelligent vehicle to develop cutting edge applications to cope with perception difficulties, data processing algorithms, expert knowl edge, and decision making. The long term in vehicle applications, that are presented in this paper, out perform the most significant and fundamental technical limitations, such as, robustness in the face of changing environmental conditions. Our intelligent vehicle operates outdoors with pedestrians and oth ers vehicles, and outperforms illumination variation, i.e.: shadows, low lighting conditions, night vision, among others. So, our applications ensure the suitable robustness and safety in case of a large variety of lighting conditions and complex perception tasks. Some of these complex tasks are overcome by the improvement of other devices, such as, inertial measurement units or differential global positioning sys tems, or perception architectures that accomplish sensor fusion processes in an efficient and safe manner. Both extra devices and architectures enhance the accuracy of computational perception and outreach the properties of each device separately.

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Section: Driver Safety Through Facial Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

IVVI 2.0: An intelligent vehicle based on computational perception

Martín

García

Musleh

et al. 2014

Expert Systems with Applications

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show abstract

“…Many studies focus predominantly on physiological measures for workload, and consider relatively few performance measures (Healey & Picard, 2005;Rodrigues, Vieira, Vinhoza, Barros, & Cunha, 2010;Reimer et al, 2012;Mehler et al, 2012;Flores, Armingol, & de la Escalera, 2011;Jo, Lee, Park, Kim, & Kim, 2014). This is likely due to the higher responsiveness of physiological measures to workload, and performance measures extracted from vehicle telemetry data are often used only as secondary inputs (Wollmer et al, 2011).…”

Section: Driver Workload Monitoringmentioning

confidence: 99%

Investigating the Feasibility of Vehicle Telemetry Data as a Means of Predicting Driver Workload

Taylor

Griffiths

Bhalerao

et al. 2017

International Journal of Mobile Human Computer Interaction

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Driving is a safety critical task that requires a high level of attention and workload from the driver. Despite this, people often also perform secondary tasks such as eating or using a mobile phone, which increase workload levels and divert cognitive and physical attention from the primary task of driving. If a vehicle is aware that the driver is currently under high workload, the vehicle functionality can be changed in order to minimize any further demand. Traditionally, workload measurements have been performed using intrusive means such as physiological sensors. Another approach may be to use vehicle telemetry data as a performance measure for workload. In this paper, we present the Warwick-JLR Driver Monitoring Dataset (DMD) and analyse it to investigate the feasibility of using vehicle telemetry data for determining the driver workload. We perform a statistical analysis of subjective ratings, physiological data, and vehicle telemetry data collected during a track study. A data mining methodology is then presented to build predictive models using this data, for the driver workload monitoring problem

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“…Applications such as an autonomous overtaking system (Milanés, Llorca, Villagrá, Pérez.Fernández, et al, 2012) or automatic stopping (Milanés, Llorca, Villagrá, Pérez, Parra, et al, 2012) are based on advanced visión together with control applications. Other classical applications are advanced localization (Gruyer, Belaroussi, & Revilloud, 2016), pedestrian detection , driver drowsiness detection (Jo, Lee, Park, Kim, & Kim, 2014), lañe departure (Son, Yoo, Kim, & Sohn, 2015), rear obstaele detection (Kim, Choi, Yoo, Yang, & Sohn, 2015), and roundabout moving obstacle detection (Hassannejad, Medici, Cardarelli, & Cerri, 2015). Other advanced control applications use V2V information, such as cooperative control for highways (Pérez, Milanés, Godoy, Villagrá, & Onieva, 2013) and intersection management (Bi, Srinivasan, Lu, Sun, & Zeng, 2014).…”

Section: State Of the Artmentioning

confidence: 99%