Deep-Learning-Assisted Sensor with Multiple Perception Capabilities for an Intelligent Driver Assistance Monitoring System.

Driver assistance systems can help drivers achieve better control of their vehicles while driving and reduce driver fatigue and errors. However, the current driver assistance devices have a complex structure and severely violate the privacy of drivers, hindering the development of driver assistance technology. To address these limitations, this article proposes an intelligent driver assistance monitoring system (IDAMS), which combines a Kresling origami structure-based triboelectric sensor (KOS-TS) and a convolutional neural network (CNN)-based data analysis. For different driving behaviors, the output signals of the KOS-TSs contain various features, such as a driver's pressing force, pressing time, and sensor triggering sequence. This study develops a multiscale CNN that employs different pooling methods to process KOS-TS data and analyze temporal information. The proposed IDAMS is verified by driver identification experiments, and the results show that the accuracy of the IDAMS in discriminating eight different users is improved from 96.25% to 99.38%. In addition, the results indicate that IDAMS can successfully monitor driving behaviors and can accurately distinguish between different driving behaviors. Finally, the proposed IDAMS has excellent hands-off detection (HOD), identification, and driving behavior monitoring capabilities and shows broad potential for application in the fields of safety warning, personalization, and human-computer interaction.

EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features.

Epilepsy and psychogenic non-epileptic seizures (PNES) often show over-lap in symptoms, especially at an early disease stage. During a PNES, the electrical activity of the brain remains normal but in case of an epileptic seizure the brain will show epileptiform discharges on the electroencephalogram (EEG). In many cases an accurate diagnosis can only be achieved after a long-term video monitoring combined with EEG recording which is quite expensive and time-consuming. In this paper using short-term EEG data, the classification of epilepsy and PNES subjects is analyzed based on signal, functional network and EEG microstate features. Our results showed that the beta-band is the most useful EEG frequency sub-band as it performs best for classifying subjects. Also the results depicted that when the coverage feature of the EEG microstate analysis is calculated in beta-band, the classification shows fairly high accuracy and precision. Hence, the beta-band and the coverage are the most important features for classification of epilepsy and PNES patients.

Severity assessment of accidents involving roadside trees based on occupant injury analysis.

The aims of this study were to achieve a quantitative assessment of the severity of accidents involving roadside trees on highways and to propose corresponding safety measures to reduce accident losses. This paper used the acceleration severity index (ASI), head injury criteria (HIC) and chest resultant acceleration (CRA) as indicators of occupant injuries and horizontal radii, vehicle departure speeds, tree diameters and roadside tree spacing as research variables to carry out bias collision tests between cars, trucks and trees by constructing a vehicle rigid body system and an occupant multibody system in PC-crash 10.0® simulation software. A total of 2,256 data points were collected. For straight and curved segments of highways, the occupant injury evaluation models of cars were fitted based on the CRA, and occupant injury evaluation models of trucks and cars were fitted based on the ASI. According to the Fisher optimal segmentation method, reasonable classification standards of severities of accidents involving roadside trees and the corresponding ASI and CRA thresholds were determined, and severity assessment methods for accidents involving roadside trees based on the CRA and ASI were provided. Additionally, a new index by which to evaluate the accuracy of the accident severity classification and the degree of misclassification was built and applied for the validity verification of the proposed severity assessment methods. A proportion of trucks was introduced to further improve the ASI evaluation model. For the same simulation conditions, the results show that driver chest injuries are more serious than driver head injuries and that the average ASI of cars is greater than that of trucks. The CRA and ASI have a positive linear correlation with the departure speed and a logarithmic correlation with the roadside tree diameters. The larger the spacing of roadside trees is and the smaller the horizontal radius is, the smaller the chance that a vehicle will experience a second collision and the lower the risk of occupant injury. In method validation, the evaluation results from two proposed severity assessment methods based on the CRA and ASI are consistent, and the degrees of misclassification are 4.65% and 4.26%, respectively, which verifies the accuracy of the methods proposed in this paper and confirms that the ASI can be employed as an effective index for evaluating occupant injuries in accidents involving roadside trees.

Drivers' Visual Search Patterns during Overtaking Maneuvers on Freeway.

Drivers gather traffic information primarily by means of their vision. Especially during complicated maneuvers, such as overtaking, they need to perceive a variety of characteristics including the lateral and longitudinal distances with other vehicles, the speed of others vehicles, lane occupancy, and so on, to avoid crashes. The primary object of this study is to examine the appropriate visual search patterns during overtaking maneuvers on freeways. We designed a series of driving simulating experiments in which the type and speed of the leading vehicle were considered as two influential factors. One hundred and forty participants took part in the study. The participants overtook the leading vehicles just like they would usually do so, and their eye movements were collected by use of the Eye Tracker. The results show that participants' gaze durations and saccade durations followed normal distribution patterns and that saccade angles followed a log-normal distribution pattern. It was observed that the type of leading vehicle significantly impacted the drivers' gaze duration and gaze frequency. As the speed of a leading vehicle increased, subjects' saccade durations became longer and saccade angles became larger. In addition, the initial and destination lanes were found to be key areas with the highest visual allocating proportion, accounting for more than 65% of total visual allocation. Subjects tended to more frequently shift their viewpoints between the initial lane and destination lane in order to search for crucial traffic information. However, they seldom directly shifted their viewpoints between the two wing mirrors.

The impact of continuous driving time and rest time on commercial drivers' driving performance and recovery.

PROBLEM: This real road driving study was conducted to investigate the effects of driving time and rest time on the driving performance and recovery of commercial coach drivers. METHODS: Thirty-three commercial coach drivers participated in the study, and were divided into three groups according to driving time: (a) 2 h, (b) 3 h, and (c) 4 h. The Stanford Sleepiness Scale (SSS) was used to assess the subjective fatigue level of the drivers. One-way ANOVA was employed to analyze the variation in driving performance. RESULTS: The statistical analysis revealed that driving time had a significant effect on the subjective fatigue and driving performance measures among the three groups. After 2 h of driving, both the subjective fatigue and driving performance measures began to deteriorate. After 4 h of driving, all of the driving performance indicators changed significantly except for depth perception. A certain amount of rest time eliminated the negative effects of fatigue. A 15-minute rest allowed drivers to recover from a two-hour driving task. This needed to be prolonged to 30 min for driving tasks of 3 to 4 h of continuous driving. PRACTICAL IMPLICATIONS: Drivers' attention, reactions, operating ability, and perceptions are all affected in turn after over 2 h of continuous driving. Drivers should take a certain amount of rest to recover from the fatigue effects before they continue driving.