Estimation of lane data-based features by odometric vehicle data for driver state monitoring

Friedrichs, Fabian; Miksch, Michael; Yang, Bin

doi:10.1109/itsc.2010.5624976

Cited by 7 publications

(2 citation statements)

References 4 publications

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“…Attention Assist System'' developed by the Mercedes automobile company observes sensors installed in the steering wheel, acceleration and braking patterns of the car, road condition, etc. to determine the driver's mental state 10 . During a particular trip, the system generates a driver's profile with certain thresholds based on the above-mentioned factors.…”

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confidence: 99%

Utilizing EEG and fNIRS for the detection of sleep-deprivation-induced fatigue and its inhibition using colored light stimulation

Shoaib

Akbar

Kim

et al. 2023

Sci Rep

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Drowsy driving is a common, but underestimated phenomenon in terms of associated risks as it often results in crashes causing fatalities and serious injuries. It is a challenging task to alert or reduce the driver’s drowsy state using non-invasive techniques. In this study, a drowsiness reduction strategy has been developed and analyzed using exposure to different light colors and recording the corresponding electrical and biological brain activities. 31 subjects were examined by dividing them into 2 classes, a control group, and a healthy group. Fourteen EEG and 42 fNIRS channels were used to gather neurological data from two brain regions (prefrontal and visual cortices). Experiments shining 3 different colored lights have been carried out on them at certain times when there is a high probability to get drowsy. The results of this study show that there is a significant increase in HbO of a sleep-deprived participant when he is exposed to blue light. Similarly, the beta band of EEG also showed an increased response. However, the study found that there is no considerable increase in HbO and beta band power in the case of red and green light exposures. In addition to that, values of other physiological signals acquired such as heart rate, eye blinking, and self-reported Karolinska Sleepiness Scale scores validated the findings predicted by the electrical and biological signals. The statistical significance of the signals achieved has been tested using repeated measures ANOVA and t-tests. Correlation scores were also calculated to find the association between the changes in the data signals with the corresponding changes in the alertness level.

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mentioning

confidence: 99%

Utilizing EEG and fNIRS for the detection of sleep-deprivation-induced fatigue and its inhibition using colored light stimulation

Shoaib

Akbar

Kim

et al. 2023

Sci Rep

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“…Lane detection has been one of the areas that has caught tremendous attention from researchers in the past decade, mainly because of its relevance in a number of ADAS applications such as lane departure warning [18,31,77], driver-attention monitoring [34,78,79] or automated cruise control [35,80,81]. One of the most crucial steps in lane detection is the extraction of lane markings [32,65,82].…”

Section: Lane Marking Detectionmentioning

confidence: 99%

Detection of road markings for advanced driver assistance

Sathyanarayana¹

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Thambipillai Srikanthan, for his unceasing support, time, valuable guidance and constant encouragement that he offered throughout this thesis. I am greatly indebted to him for the constructive suggestions and ideas, that have immensely contributed to the unfolding of the research work. I also want to thank him for having created an enjoyable research atmosphere and for providing all the advanced facilities at Center for High Performance Embedded Systems (CHiPES). I would like to offer my special thanks my teammate and husband, Ravi Kumar Satzoda, who has played a vital role at every step of my thesis. The regular team discussions and brainstorming have greatly contributed towards the research work. More importantly, it was his support, encouragement and love that made it possible for me to complete this thesis. I would like to thank my colleagues, team members, and friends, specially Supriya and Nirmala, for their support in one way or the other towards the completion of the thesis. My gratitude goes out to Mr.Muthukrishnan for helping me with the test drives and generation of dataset for the purpose of evaluation. Thanks to the technician Mr. Jeremiah Chua for the technical support throughout. I would like to thank my parents, siblings and parents-in-law for their encouragement and support through the period of the thesis. My humble gratitude to my spiritual master, Paramahamsa Sri Nithyananda, for being an eternal source of inspiration and inner strength. Finally, I would like to acknowledge School of Computer Engineering, Nanyang Technological University for providing this opportunity to me for carrying out this research work.

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