Disabilities of the upper limb, such as hemiplegia or upper limb amputation, can limit automobile drivers to steering with one healthy arm. For the benefit of these drivers, recent studies have developed prototype interfaces that realized surface electromyography (sEMG)-controlled steering assistance with path-following accuracy that has been validated with driving simulations. In contrast, the current study expands the application of sEMG-controlled steering assistance by validating the Myo armband, a mass-produced sEMG-based interface, with respect to the path-following accuracy of a commercially available automobile. It was hypothesized that one-handed remote steering with the Myo armband would be comparable or superior to the conventional operation of the automobile steering wheel. Although results of low-speed field testing indicate that the Myo armband had lower path-following accuracy than the steering wheel during a 90° turn and wide U-turn at twice the minimum turning radius, the Myo armband had superior path-following accuracy for a narrow U-turn at the minimum turning radius and a 45° turn. Given its overall comparability to the steering wheel, the Myo armband could be feasibly applied in future automobile studies.

In order to improve the adequacy and safety of transportation for elderly people, the reasons for, and the after-effects of stopping driving by elderly people were analyzed. The subjects consisted of 500 car manufacturing retirees, aged 60 years old and over, living in Kanagawa prefecture. Of the 500 people questioned, 298 people (59.6%) responded. Of these 196 currently held licences. In this group, 149 still drove, 9 intended to stop driving soon, and 38 had already stopped. Of the 38 people who had already stopped driving, 7 quit before age 55. These people were omitted from analysis because factors other than aging possibly contributing to their stopping driving. We were interested in the 31 people older than 55 and the 9 people who intended to quit soon. The results were the following: I) The main reasons why they had stopped driving were the following: a) anxiety felt because of decreased driving ability and the feeling to need to walk in order to improve their health; b) the needlessness of driving because of availability of other means of transportation; II) The anxiety from decreased driving ability correlated with difficulty with narrow roads and imparied vision making it difficult to observed road signs; III) The reasons for stopping driving correlated with the effects: the needlessness of driving because of use of other means of transportation correlated with decreased frequency of going out for hobby and group activities. These results show that stopping driving also affects the social activities of the Japanese elderly. It was concluded that we should inform the elderly drivers of the effects of stopping driving as well as improving conditions on roads and comfort in automobiles.

A paradigm shift is required to prevent the increasing automobile accident deaths that are mostly due to the inattentive behavior of drivers. Knowledge of gaze region can provide valuable information regarding a driver's point of attention. Accurate and inexpensive gaze classification systems in cars can improve safe driving. However, monitoring real-time driving behaviors and conditions presents some challenges: dizziness due to long drives, extreme lighting variations, glasses reflections, and occlusions. Past studies on gaze detection in cars have been chiefly based on head movements. The margin of error in gaze detection increases when drivers gaze at objects by moving their eyes without moving their heads. To solve this problem, a pupil center corneal reflection (PCCR)-based method has been considered. However, the error of accurately detecting the pupil center and corneal reflection center is increased in a car environment due to various environment light changes, reflections on glasses surface, and motion and optical blurring of captured eye image. In addition, existing PCCR-based methods require initial user calibration, which is difficult to perform in a car environment. To address this issue, we propose a deep learning-based gaze detection method using a near-infrared (NIR) camera sensor considering driver head and eye movement that does not require any initial user calibration. The proposed system is evaluated on our self-constructed database as well as on open Columbia gaze dataset (CAVE-DB). The proposed method demonstrated greater accuracy than the previous gaze classification methods.

This study aimed to assess driving reaction times (DRTs) after spinal surgery to establish a timeframe for safe resumption of driving by the patient postoperatively. The MEDLINE and Google Scholar databases were analyzed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) Statement for clinical studies that investigated changes in DRTs following cervical and lumbar spinal surgery. Changes in DRTs and patients' clinical presentation, pathology, anatomical level affected, number of spinal levels involved, type of intervention, pain level, and driving skills were assessed. The literature search identified 12 studies that investigated postoperative DRTs. Six studies met the inclusion criteria; five studies assessed changes in DRT after lumbar spine surgery and two studies after cervical spina surgery. The spinal procedures were selective nerve root block, anterior cervical discectomy and fusion, and lumbar fusion and/ordecompression. DRTs exhibited variable responses to spinal surgery and depended on the patients' clinical presentation, spinal level involved, and type of procedure performed. The evidence regarding the patients' ability to resume safe driving after spinal surgery is scarce. Normalization of DRT or a return of DRT to pre-spinal intervention level is a widely accepted indicator for safe driving, with variable levels of statistical significance owing to multiple confounding factors. Considerations of the type of spinal intervention, pain level, opioid consumption, and cognitive function should be factored in the assessment of a patient's ability to safely resume driving.

Mind wandering is a pervasive threat to transportation safety, potentially accounting for a substantial number of crashes and fatalities. In the current study, mind wandering was induced through completion of the same task for 5 days, consisting of a 20-min monotonous freeway-driving scenario, a cognitive depletion task, and a repetition of the 20-min driving scenario driven in the reverse direction. Participants were periodically probed with auditory tones to self-report whether they were mind wandering or focused on the driving task. Self-reported mind wandering frequency was high, and did not statistically change over days of participation. For measures of driving performance, participant labeled periods of mind wandering were associated with reduced speed and reduced lane variability, in comparison to periods of on task performance. For measures of electrophysiology, periods of mind wandering were associated with increased power in the alpha band of the electroencephalogram (EEG), as well as a reduction in the magnitude of the P3a component of the event related potential (ERP) in response to the auditory probe. Results support that mind wandering has an impact on driving performance and the associated change in driver's attentional state is detectable in underlying brain physiology. Further, results suggest that detecting the internal cognitive state of humans is possible in a continuous task such as automobile driving. Identifying periods of likely mind wandering could serve as a useful research tool for assessment of driver attention, and could potentially lead to future in-vehicle safety countermeasures.

Improving physical functioning may be a future intervention to keep older adults driving safely longer as it can help maintain both physical and cognitive health longer. This systematic review assesses the evidence on the association between three physical functioning measures: the Short Physical Performance Battery, the Timed Up-and-Go test, and the Rapid Pace Walk with driving outcomes in older adults.

Pedestrian detection is a specific application of object detection. Compared with general object detection, it shows similarities and unique characteristics. In addition, it has important application value in the fields of intelligent driving and security monitoring. In recent years, with the rapid development of deep learning, pedestrian detection technology has also made great progress. However, there still exists a huge gap between it and human perception. Meanwhile, there are still a lot of problems, and there remains a lot of room for research. Regarding the application of pedestrian detection in intelligent driving technology, it is of necessity to ensure its real-time performance. Additionally, it is necessary to lighten the model while ensuring detection accuracy. This paper first briefly describes the development process of pedestrian detection and then concentrates on summarizing the research results of pedestrian detection technology in the deep learning stage. Subsequently, by summarizing the pedestrian detection dataset and evaluation criteria, the core issues of the current development of pedestrian detection are analyzed. Finally, the next possible development direction of pedestrian detection technology is explained at the end of the paper.

Drowsy driving is a major cause of automobile accidents. Previous studies used neuroimaging based approaches such as analysis of electroencephalogram (EEG) activities to understand the brain dynamics of different cortical regions during drowsy driving. However, the coupling between brain regions responding to this vigilance change is still unclear. To have a comprehensive understanding of neural mechanisms underlying drowsy driving, in this study we use transfer entropy, a model-free measure of effective connectivity based on information theory. We investigate the pattern of information transfer between brain regions when the vigilance level, which is derived from the driving performance, changes from alertness to drowsiness. Results show that the couplings between pairs of frontal, central, and parietal areas increased at the intermediate level of vigilance, which suggests that an enhancement of the cortico-cortical interaction is necessary to maintain the task performance and prevent behavioral lapses. Additionally, the occipital-related connectivity magnitudes monotonically decreases as the vigilance level declines, which further supports the cortical gating of sensory stimuli during drowsiness. Neurophysiological evidence of mutual relationships between brain regions measured by transfer entropy might enhance the understanding of cortico-cortical communication during drowsy driving.

Physicians often find significant challenges in assessing automobile driving in persons with mild cognitive impairment and mild dementia and deciding when to report to transportation administrators. Care must be taken to balance the safety of patients and other road users with potential negative effects of issuing such reports.

The purpose of this study was to conduct an evidence-based review to determine predictors of fitness to drive and return to driving in persons with traumatic brain injury (TBI). Relevant databases (MEDLINE/PubMed, CINAHL, Cochrane Library, and SCOPUS) were searched for primary articles published before June 2016 using MeSH search terms. Using the American Academy of Neurology's classification criteria, 24 articles were included after reviewing 1998 articles. Studies were rated by class (I⁻IV), with I being the highest level of evidence. Articles were classified according to TBI severity, as well as types of assessments (on-road, simulator and surveys). There were no Class I studies. Based on Class II studies, only Post-traumatic amnesia (PTA) duration was found to be probably predictive of on-road driving performance. There is limited evidence concerning predictors of return to driving. The findings suggest further evidence is needed to identify predictors of on-road driving performance in persons with TBI. Class I studies reporting Level A recommendations for definitive predictors of driving performance in drivers with TBI are needed by policy makers and clinicians to develop evidence-based guidelines.

Car driving is more and more automated, to such an extent that driving without active steering control is becoming a reality. Although active driving requires the use of visual information to guide actions (i.e., steering the vehicle), passive driving only requires looking at the driving scene without any need to act (i.e., the human is passively driven).

Mental stress can lead to traffic accidents by reducing a driver's concentration or increasing fatigue while driving. In recent years, demand for methods to detect drivers' stress in advance to prevent dangerous situations increased. Thus, we propose a novel method for detecting driving stress using nonlinear representations of short-term (30 s or less) physiological signals for multimodal convolutional neural networks (CNNs). Specifically, from hand/foot galvanic skin response (HGSR, FGSR) and heart rate (HR) short-term input signals, first, we generate corresponding two-dimensional nonlinear representations called continuous recurrence plots (Cont-RPs). Second, from the Cont-RPs, we use multimodal CNNs to automatically extract FGSR, HGSR, and HR signal representative features that can effectively differentiate between stressed and relaxed states. Lastly, we concatenate the three extracted features into one integrated representation vector, which we feed to a fully connected layer to perform classification. For the evaluation, we use a public stress dataset collected from actual driving environments. Experimental results show that the proposed method demonstrates superior performance for 30-s signals, with an overall accuracy of 95.67%, an approximately 2.5-3% improvement compared with that of previous works. Additionally, for 10-s signals, the proposed method achieves 92.33% classification accuracy, which is similar to or better than the performance of other methods using long-term signals (over 100 s).

As automobile manufacturers have begun to design, engineer, and test autonomous driving systems of the future, brain imaging with functional near-infrared spectroscopy (fNIRS) can provide unique insights about cognitive processes associated with evolving levels of autonomy implemented in the automobile. Modern fNIRS devices provide a portable, relatively affordable, and robust form of functional neuroimaging that allows researchers to investigate brain function in real-world environments. The trend toward "naturalistic neuroscience" is evident in the growing number of studies that leverage the methodological flexibility of fNIRS, and in doing so, significantly expand the scope of cognitive function that is accessible to observation via functional brain imaging (i.e., from the simulator to on-road scenarios). While more than a decade's worth of study in this field of fNIRS driving research has led to many interesting findings, the number of studies applying fNIRS during autonomous modes of operation is limited. To support future research that directly addresses this lack in autonomous driving research with fNIRS, we argue that a cogent distillation of the methods used to date will help facilitate and streamline this research of tomorrow. To that end, here we provide a methodological review of the existing fNIRS driving research, with the overarching goal of highlighting the current diversity in methodological approaches. We argue that standardization of these approaches will facilitate greater overlap of methods by researchers from all disciplines, which will, in-turn, allow for meta-analysis of future results. We conclude by providing recommendations for advancing the use of such fNIRS technology in furthering understanding the adoption of safe autonomous vehicle technology.

The number of older drivers is steadily increasing, and advancing age is associated with a high rate of automobile crashes and fatalities. This can be attributed to a combination of factors including decline in sensory, motor, and cognitive functions due to natural aging or neurodegenerative diseases such as HIV-Associated Neurocognitive Disorder (HAND). Current clinical assessment methods only modestly predict impaired driving. Thus, there is a need for inexpensive and scalable tools to predict on-road driving performance. In this study EEG was acquired from 39 HIV+ patients and 63 healthy participants (HP) during: 3-Choice-Vigilance Task (3CVT), a 30-min driving simulator session, and a 12-mile on-road driving evaluation. Based on driving performance, a designation of Good/Poor (simulator) and Safe/Unsafe (on-road drive) was assigned to each participant. Event-related potentials (ERPs) obtained during 3CVT showed increased amplitude of the P200 component was associated with bad driving performance both during the on-road and simulated drive. This P200 effect was consistent across the HP and HIV+ groups, particularly over the left frontal-central region. Decreased amplitude of the late positive potential (LPP) during 3CVT, particularly over the left frontal regions, was associated with bad driving performance in the simulator. These EEG ERP metrics were shown to be associated with driving performance across participants independent of HIV status. During the on-road evaluation, Unsafe drivers exhibited higher EEG alpha power compared to Safe drivers. The results of this study are 2-fold. First, they demonstrate that high-quality EEG can be inexpensively and easily acquired during simulated and on-road driving assessments. Secondly, EEG metrics acquired during a sustained attention task (3CVT) are associated with driving performance, and these metrics could potentially be used to assess whether an individual has the cognitive skills necessary for safe driving.

Resident physicians often work longer than 24 consecutive hours with little or no sleep. A systematic review of the literature was conducted to investigate the risk of resident physician motor vehicle collisions (MVC), and dangerous driving, after extended-duration work shifts (EDWS).

Comfort is a significant factor that affects passengers' choice of autonomous vehicles. The comfort of an autonomous vehicle is largely determined by its control algorithm. Therefore, if the comfort of passengers can be predicted based on factors that affect comfort and the control algorithm can be adjusted, it can be beneficial to improve the comfort of autonomous vehicles. In view of this, in the present study, a human-driven experiment was carried out to simulate the typical driving state of a future autonomous vehicle. In the experiment, vehicle motion parameters and the comfort evaluation results of passengers with different physiological characteristics were collected. A single-factor analysis method and binary logistic regression analysis model were used to determine the factors that affect the evaluation results of passenger comfort. A passenger comfort prediction model was established based on the bidirectional long short-term memory network model. The results demonstrate that the accuracy of the passenger comfort prediction model reached 84%, which can provide a theoretical basis for the adjustment of the control algorithm and path trajectory of autonomous vehicles.

Developing a human-like autonomous driving system has gained increasing amounts of attention from both technology companies and academic institutions, as it can improve the interpretability and acceptance of the autonomous system. Planning a safe and human-like obstacle avoidance trajectory is one of the critical issues for the development of autonomous vehicles (AVs). However, when designing automatic obstacle avoidance systems, few studies have focused on the obstacle avoidance characteristics of human drivers. This paper aims to develop an obstacle avoidance trajectory planning and trajectory tracking model for AVs that is consistent with the characteristics of human drivers' obstacle avoidance trajectory. Therefore, a modified artificial potential field (APF) model was established by adding a road boundary repulsive potential field and ameliorating the obstacle repulsive potential field based on the traditional APF model. The model predictive control (MPC) algorithm was combined with the APF model to make the planning model satisfy the kinematic constraints of the vehicle. In addition, a human driver's obstacle avoidance experiment was implemented based on a six-degree-of-freedom driving simulator equipped with multiple sensors to obtain the drivers' operation characteristics and provide a basis for parameter confirmation of the planning model. Then, a linear time-varying MPC algorithm was employed to construct the trajectory tracking model. Finally, a co-simulation model based on CarSim/Simulink was established for off-line simulation testing, and the results indicated that the proposed trajectory planning controller and the trajectory tracking controller were more human-like under the premise of ensuring the safety and comfort of the obstacle avoidance operation, providing a foundation for the development of AVs.

At an intersection with complex traffic flow, the early detection of the intention of drivers in surrounding vehicles can enable advanced driver assistance systems (ADAS) to warn the driver in advance or prompt its subsystems to assess the risk and intervene early. Although different drivers show various driving characteristics, the kinematic parameters of human-driven vehicles can be used as a predictor for predicting the driver's intention within a short time. In this paper, we propose a new hybrid approach for vehicle behavior recognition at intersections based on time series prediction and deep learning networks. First, the lateral position, longitudinal position, speed, and acceleration of the vehicle are predicted using the online autoregressive integrated moving average (ARIMA) algorithm. Next, a variant of the long short-term memory network, called the bidirectional long short-term memory (Bi-LSTM) network, is used to detect the vehicle's turning behavior using the predicted parameters, as well as the derived parameters, i.e., the lateral velocity, lateral acceleration, and heading angle. The validity of the proposed method is verified at real intersections using the public driving data of the next generation simulation (NGSIM) project. The results of the turning behavior detection show that the proposed hybrid approach exhibits significant improvement over a conventional algorithm; the average recognition rates are 94.2% and 93.5% at 2 s and 1 s, respectively, before initiating the turning maneuver.

Speed perception tests are already used in several countries as part of the driver licensing curriculum; however, this test is not compulsively required in China. The purpose of this study was to investigate the relationship between speed perception and eye movement for different driver groups. Forty-eight drivers, including 28 crash-involved (CI), with rear-end or side collisions, and 20 crash-not-involved (CNI) drivers, were recruited for the speed perception experiments. Drivers' reaction characteristics as well as eye movement data were analyzed. The results showed that CI drivers were more likely to overestimate the speed of visual stimuli and react in advance. The speed perception of CI drivers was more accurate than that of CNI drivers for visual stimuli with middle to high moving speeds, indicating that CNI drivers are more cautious and conservative when driving. Regarding eye movement, significant differences in saccade speed were found between the CI and CNI drivers in the occlusion area under high speed and the occlusion ratio. The relationship between visual pattern and speed perception accuracy was found to some extent. Implications of the speed perception test for the driver aptitude test were discussed.

Most of the tactic manoeuvres during driving require a certain understanding of the surrounding environment from which to devise our future behaviour. In this paper, a Convolutional Neural Network (CNN) approach is used to model the lane change behaviour to identify when a driver is going to perform this manoeuvre. To that end, a slightly modified CNN architecture adapted to both spatial (i.e., surrounding environment) and non-spatial (i.e., rest of variables such as relative speed to the front vehicle) input variables. Anticipating a driver's lane change intention means it is possible to use this information as a new source of data in wide range of different scenarios. One example of such scenarios might be the decision making process support for human drivers through Advanced Driver Assistance Systems (ADAS) fed with the data of the surrounding cars in an inter-vehicular network. Another example might even be its use in autonomous vehicles by using the data of a specific driver profile to make automated driving more human-like. Several CNN architectures have been tested on a simulation environment to assess their performance. Results show that the selected architecture provides a higher degree of accuracy than random guessing (i.e., assigning a class randomly for each observation in the data set), and it can capture subtle differences in behaviour between different driving profiles.