A Recognition Method for Road Hypnosis Based on Physiological Characteristics

Wang, Bin; Shi, Huili; Chen, Longfei; Wang, Xiaoyuan; Wang, Gang; Zhong, Fusheng

doi:10.3390/s23073404

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…The study divided the driving time into specific periods (baseline, periods [2][3][4][5] to compare EEG data between different segments of the drive and assess changes in alertness over time.…”

Section: Driving Time Periodsmentioning

confidence: 99%

Road Hypnosis Detection using Lane Detection

2023

IJFMR

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The study "Road Hypnosis Detection Using Lane Detection" focuses on the phenomenon known as "road hypnosis" or "highway hypnosis," a mental state in which a person can operate a vehicle for extended distances while reacting to outside events as expected, all without recalling that they did so consciously. Long stretches of time spent driving on smooth, featureless highways can cause this syndrome, which lowers awareness and causes a lack of stimulation. The study suggests a novel approach that makes use of lane identification algorithms to identify road hypnosis. It keeps track of the car's location in the lane and the driver's steering movements using sophisticated image processing and machine learning algorithms. Unusualities in these patterns, which could signal the start of road hypnosis, can be detected by the system. This system attempts to improve road safety and avoid accidents caused by decreased driver alertness due to road hypnosis by giving the driver real-time feedback or initiating automatic safety measures. The suggested approach is thoroughly tested and validated throughout the study in a variety of driving situations and conditions, proving its efficacy and suitability for incorporation into contemporary car safety systems. This study addresses a prevalent but frequently disregarded problem that might result in catastrophic accidents, making a substantial contribution to the field of driver safety systems. It also creates new opportunities for utilizing technology to raise driver awareness and enhance general traffic safety.

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Section: Driving Time Periodsmentioning

confidence: 99%

Road Hypnosis Detection using Lane Detection

2023

IJFMR

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“…Since the early days of mechanical vehicles, safety has been one of the key concerns in automotive systems. Several attempts have been made to address safety concerns by developing safe and secure systems to protect the driver as well as prevent injuring pedestrians [29,30]. It is one of the safety aspects of an autonomous vehicle when the driver is preoccupied with searching for the desired location to stop.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent System to Sense Textual Cues for Location Assistance in Autonomous Vehicles

Unar

Liu

et al. 2023

Sensors

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The current technological world is growing rapidly and each aspect of life is being transformed toward automation for human comfort and reliability. With autonomous vehicle technology, the communication gap between the driver and the traditional vehicle is being reduced through multiple technologies and methods. In this regard, state-of-the-art methods have proposed several approaches for advanced driver assistance systems (ADAS) to meet the requirement of a level-5 autonomous vehicle. Consequently, this work explores the role of textual cues present in the outer environment for finding the desired locations and assisting the driver where to stop. Firstly, the driver inputs the keywords of the desired location to assist the proposed system. Secondly, the system will start sensing the textual cues present in the outer environment through natural language processing techniques. Thirdly, the system keeps matching the similar keywords input by the driver and the outer environment using similarity learning. Whenever the system finds a location having any similar keyword in the outer environment, the system informs the driver, slows down, and applies the brake to stop. The experimental results on four benchmark datasets show the efficiency and accuracy of the proposed system for finding the desired locations by sensing textual cues in autonomous vehicles.

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An Identification Method for Road Hypnosis Based on Human EEG Data

Wang,

Wang

et al. 2024

Sensors

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The driver in road hypnosis has not only some external characteristics, but also some internal characteristics. External features have obvious manifestations and can be directly observed. Internal features do not have obvious manifestations and cannot be directly observed. They need to be measured with specific instruments. Electroencephalography (EEG), as an internal feature of drivers, is the golden parameter for drivers’ life identification. EEG is of great significance for the identification of road hypnosis. An identification method for road hypnosis based on human EEG data is proposed in this paper. EEG data on drivers in road hypnosis can be collected through vehicle driving experiments and virtual driving experiments. The collected data are preprocessed with the PSD (power spectral density) method, and EEG characteristics are extracted. The neural networks EEGNet, RNN, and LSTM are used to train the road hypnosis identification model. It is shown from the results that the model based on EEGNet has the best performance in terms of identification for road hypnosis, with an accuracy of 93.01%. The effectiveness and accuracy of the identification for road hypnosis are improved in this study. The essential characteristics for road hypnosis are also revealed. This is of great significance for improving the safety level of intelligent vehicles and reducing the number of traffic accidents caused by road hypnosis.

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A Recognition Method for Road Hypnosis Based on Physiological Characteristics

Cited by 4 publications

References 29 publications

Road Hypnosis Detection using Lane Detection

Road Hypnosis Detection using Lane Detection

An Intelligent System to Sense Textual Cues for Location Assistance in Autonomous Vehicles

An Identification Method for Road Hypnosis Based on Human EEG Data

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