Comparing the Effects of Visual Distraction in a High-Fidelity Driving Simulator and on a Real Highway

Sun, Qinyu; Guo, Yingshi; Liu, Yongtao; Wang, Chang; Gu, Menglu; Su, Yanqi

doi:10.1109/tits.2021.3116045

Cited by 82 publications

(1 citation statement)

References 44 publications

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“…The experiments were carried out in a simulated environment [11,62] as presented in Figure 2. The study consisted of two independent experiments that were conducted separately.…”

Section: Data Acquisitionmentioning

confidence: 99%

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

Doniec

Konior

Sieciński

et al. 2023

Sensors

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To drive safely, the driver must be aware of the surroundings, pay attention to the road traffic, and be ready to adapt to new circumstances. Most studies on driving safety focus on detecting anomalies in driver behavior and monitoring cognitive capabilities in drivers. In our study, we proposed a classifier for basic activities in driving a car, based on a similar approach that could be applied to the recognition of basic activities in daily life, that is, using electrooculographic (EOG) signals and a one-dimensional convolutional neural network (1D CNN). Our classifier achieved an accuracy of 80% for the 16 primary and secondary activities. The accuracy related to activities in driving, including crossroad, parking, roundabout, and secondary activities, was 97.9%, 96.8%, 97.4%, and 99.5%, respectively. The F1 score for secondary driving actions (0.99) was higher than for primary driving activities (0.93–0.94). Furthermore, using the same algorithm, it was possible to distinguish four activities related to activities of daily life that were secondary activities when driving a car.

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“…The experiments were carried out in a simulated environment [11,62] as presented in Figure 2. The study consisted of two independent experiments that were conducted separately.…”

Section: Data Acquisitionmentioning

confidence: 99%