Online Collaborative Perception of Full Bridge Deck Driving Visual of Far Blind Area on Suspension Bridge during Vortex-Induced Vibration.

During a vertical vortex-induced vibration (VVIV), an undulating bridge deck will affect drivers' sightlines, causing the phenomenon of drifting and changes in the far blind area, thus presenting a potential threat to driving safety. Consequently, to ensure the safety of driving on a suspension bridge deck under VVIV, it is necessary to perceive the far blind spot caused by the occlusion of the driving sightlines under this condition, and to establish an online perception and evaluation mechanism for driving safety. With a long-span suspension bridge experiencing VVIV as the engineering background, this paper utilizes the acceleration integration algorithm and the sine function fitting method to achieve the online perception of real-time dynamic configurations of the main girder. Then, based on the configurations, the maximum height of the driver's far blind area and effective sight distance are calculated accordingly, and the impact of different driving conditions on them is discussed. The proposed technical framework for driving safety perception in far blind spots is feasible, as it can achieve real-time estimation of the maximum height and effective distance of the far blind area, thereby providing technical support for bridge-vehicle-human collaborative perception and traffic control during vortex-induced vibration.

An Automobile's Tail Lights: Sacrificing Safety for Playful Design?

OBJECTIVE: The counterintuitive "Union Jack"-inspired turn signals on versions of BMW's Mini vehicles was investigated to reveal potential impacts on human performance. BACKGROUND: When some Mini drivers indicate a change in direction, they do so with an oppositely oriented arrow. This conflict, between the task-irrelevant spatial shape and task-relevant location of the signal, mimics a "converse" spatial-Stroop effect that, in combination with the ubiquitous use of arrows on road signs, may be confusing. METHOD: Participants (n = 30) responded-via right and left keypresses-to the directions of road signs and turn signals in both pure and mixed blocks. Reaction times and accuracies were recorded to determine performance in each condition (compatible, neutral, incompatible). RESULTS: Performance suffered when the location and direction of the stimuli did not correspond. When responding to turn signals the cost to performance was especially salient in mixed blocks. Thus, when driving on roads where the meanings of arrows on road signs is important, turn signals pointing in a direction opposite from the directional intention indicated by the signals' location are likely to be confusing. CONCLUSION: The design of some Mini's "Union Jack" style taillights opposes well-established principles of cognitive functioning, caused confusion in our laboratory study and therefore may be a safety hazard-a possibility that ought to be explored in more realistic (e.g., driving simulator) situations. APPLICATION: BMW designers should consider universally adopting the neutral, "horizontal line," illumination style that is currently available in the aftermarket.

[Reduced mobility in the elderly due to medication, alcohol, and cannabinoids]. / Einschränkungen der Mobilität von älteren Verkehrsteilnehmern durch Medikamente, Alkohol und Cannabis.

Many diseases are accompanied by symptoms that can impair the ability to perform complex everyday tasks, such as active participation in road traffic. If a cure is not possible, the aim of drug therapy is to alleviate the symptoms to such an extent that the patient no longer has any restrictions in everyday life. However, around 20% of the approximately 100,000 medicines licensed in Germany have traffic-relevant side effects that can also lead to driving impairment.It is assumed that the effect of a drug is at least partially responsible for one in four traffic accidents and that one in ten victims of fatal road accidents has taken psychotropic drugs before driving. In addition to alcohol and drugs, medications from the benzodiazepine, opioid, and antidepressant groups are suspected of impairing driving safety in particular. The effects of these substances on young people have been described many times, but this review deals specifically with the traffic-relevant (side) effects of various classes of drugs on elderly people (aged 65 and over).Older people in particular often have to take different medications, which are metabolized differently compared to younger people due to underlying diseases and can also interact with each other. It was found that (1) older people often react more sensitively to substances, (2) not all representatives of a drug class have the same effect on driving safety, and (3) a general assessment of a drug's safety is not possible, since the effects also depend on other factors such as underlying diseases, treatment regimen, and the time of day the medication is taken.

Method of Evaluating Multiple Scenarios in a Single Simulation Run for Automated Vehicle Assessment.

With advances in the technology applied to automated driving systems (ADSs), active efforts have been made to evaluate the safety of ADS in various complex situations using simulations. In accordance with these efforts, numerous institutions have developed single-scenario pools that reflect a variety of road and traffic characteristics and ADS performances. However, a single scenario has limitations in comprehensively evaluating the performance of complex ADS. Therefore, this study proposed a methodology that combines and transforms single scenarios into multiple scenarios. This aided in continuously evaluating the ADS performance over entire road segments and implemented this methodology in the simulations.

A Dynamic Path-Planning Method for Obstacle Avoidance Based on the Driving Safety Field.

Establishing an accurate and computationally efficient model for driving risk assessment, considering the influence of vehicle motion state and kinematic characteristics on path planning, is crucial for generating safe, comfortable, and easily trackable obstacle avoidance paths. To address this topic, this paper proposes a novel dual-layered dynamic path-planning method for obstacle avoidance based on the driving safety field (DSF). The contributions of the proposed approach lie in its ability to address the challenges of accurately modeling driving risk, efficient path smoothing and adaptability to vehicle kinematic characteristics, and providing collision-free, curvature-continuous, and adaptable obstacle avoidance paths. In the upper layer, a comprehensive driving safety field is constructed, composed of a potential field generated by static obstacles, a kinetic field generated by dynamic obstacles, a potential field generated by lane boundaries, and a driving field generated by the target position. By analyzing the virtual field forces exerted on the ego vehicle within the comprehensive driving safety field, the resultant force direction is utilized as guidance for the vehicle's forward motion. This generates an initial obstacle avoidance path that satisfies the vehicle's kinematic and dynamic constraints. In the lower layer, the problem of path smoothing is transformed into a standard quadratic programming (QP) form. By optimizing discrete waypoints and fitting polynomial curves, a curvature-continuous and smooth path is obtained. Simulation results demonstrate that our proposed path-planning algorithm outperforms the method based on the improved artificial potential field (APF). It not only generates collision-free and curvature-continuous paths but also significantly reduces parameters such as path curvature (reduced by 62.29% to 87.32%), curvature variation rate, and heading angle (reduced by 34.11% to 72.06%). Furthermore, our algorithm dynamically adjusts the starting position of the obstacle avoidance maneuver based on the vehicle's motion state. As the relative velocity between the ego vehicle and the obstacle vehicle increases, the starting position of the obstacle avoidance path is adjusted accordingly, enabling the proactive avoidance of stationary or moving single and multiple obstacles. The proposed method satisfies the requirements of obstacle avoidance safety, comfort, and stability for intelligent vehicles in complex environments.

Driver Stress Detection Using Ultra-Short-Term HRV Analysis under Real World Driving Conditions.

Considering that driving stress is a major contributor to traffic accidents, detecting drivers' stress levels in time is helpful for ensuring driving safety. This paper attempts to investigate the ability of ultra-short-term (30-s, 1-min, 2-min, and 3-min) HRV analysis for driver stress detection under real driving circumstances. Specifically, the t-test was used to investigate whether there were significant differences in HRV features under different stress levels. Ultra-short-term HRV features were compared with the corresponding short-term (5-min) features during low-stress and high-stress phases by the Spearman rank correlation and Bland-Altman plots analysis. Furthermore, four different machine-learning classifiers, including a support vector machine (SVM), random forests (RFs), K-nearest neighbor (KNN), and Adaboost, were evaluated for stress detection. The results show that the HRV features extracted from ultra-short-term epochs were able to detect binary drivers' stress levels accurately. In particular, although the capability of HRV features in detecting driver stress also varied between different ultra-short-term epochs, MeanNN, SDNN, NN20, and MeanHR were selected as valid surrogates of short-term features for driver stress detection across the different epochs. For drivers' stress levels classification, the best performance was achieved with the SVM classifier, with an accuracy of 85.3% using 3-min HRV features. This study makes a contribution to building a robust and effective stress detection system using ultra-short-term HRV features under actual driving environments.

Bayesian criterion-based assessments of recurrent event models with applications to commercial truck driver behavior studies.

Multitype recurrent events are commonly observed in transportation studies, since commercial truck drivers may encounter different types of safety critical events (SCEs) and take different lengths of on-duty breaks in a driving shift. Bayesian nonhomogeneous Poisson process models are a flexible approach to jointly model the intensity functions of the multitype recurrent events. For evaluating and comparing these models, the deviance information criterion (DIC) and the logarithm of the pseudo-marginal likelihood (LPML) are studied and Monte Carlo methods are developed for computing these model assessment measures. We also propose a set of new concordance indices (C-indices) to evaluate various discrimination abilities of a Bayesian multitype recurrent event model. Specifically, the within-event C-index quantifies adequacy of a given model in fitting the recurrent event data for each type, the between-event C-index provides an assessment of the model fit between two types of recurrent events, and the overall C-index measures the model's discrimination ability among multiple types of recurrent events simultaneously. Moreover, we jointly model the incidence of SCEs and on-duty breaks with driving behaviors using a Bayesian Poisson process model with time-varying coefficients and time-dependent covariates. An in-depth analysis of a real dataset from the commercial truck driver naturalistic driving study is carried out to demonstrate the usefulness and applicability of the proposed methodology.

Development of a Framework for Generating Driving Safety Assessment Scenarios for Automated Vehicles.

Despite the technological advances in automated driving systems, traffic accidents involving automated vehicles (AVs) continue to occur, raising concerns over the safety and reliability of automated driving. For the smooth commercialization of AVs, it is necessary to systematically assess the driving safety of AVs under the various situations that they potentially face. In this context, these various situations are mostly implemented by using systematically developed scenarios. In accordance with this need, a scenario generation framework for the assessment of the driving safety of AVs is proposed by this study. The proposed framework provides a unified form of assessment with key components for each scenario stage to facilitate systematization and objectivity. The performance of the driving safety assessment scenarios generated within the proposed framework was verified. Traffic accident report data were used for verification, and the usefulness of the proposed framework was confirmed by generating a set of scenarios, ranging from functional scenarios to test cases. The scenario generation framework proposed in this study can be used to provide sustainable scenarios. In addition, from this, it is possible to create assessment scenarios for all road types and various assessment spaces, such as simulations, proving grounds, and real roads.

Motion Shield: An Automatic Notifications System for Vehicular Communications.

Motion Shield is an automatic crash notification system that uses a mobile phone to generate automatic alerts related to the safety of a user when the user is boarding a means of transportation. The objective of Motion Shield is to improve road safety by considering a moving vehicle's risk, estimating the probability of an emergency, and assessing the likelihood of an accident. The system, using multiple sources of external information, the mobile phone sensors' readings, geolocated information, weather data, and historical evidence of traffic accidents, processes a plethora of parameters in order to predict the onset of an accident and act preventively. All the collected data are forwarded into a decision support system which dynamically calculates the mobility risk and driving behavior aspects in order to proactively send personalized notifications and alerts to the user and a public safety answering point (PSAP) (112).

Online Intelligent Perception of Front Blind Area of Vehicles on a Full Bridge Based on Dynamic Configuration Monitoring of Main Girders.

Establishing an online perception mechanism for a driver's front blind area on a full bridge under vertical vortex-induced vibration (VVIV) is essential for ensuring road safety and traffic control on bridge decks under specific conditions. Based on accelerations of vibration monitoring of the main girders, this paper uses a real-time acceleration integration algorithm to obtain real-time displacements of measurement points; realizes the real-time estimation of the dynamic configurations of a main girder through parametric function fitting; and then can perceive the front blind area for vehicles driving on bridges experiencing VVIV in real time. On this basis, taking a long-span suspension bridge suffering from VVIV as an engineering example, the influence of different driving conditions on the front blind area is examined. Then, the applicability of the intelligent perception technology framework of the front blind area is verified. The results indicate that, during VVIV, the driver's front blind area changes periodically and the vehicle model has the most significant impact on the front blind area; in contrast, the vehicle's speed and the times of the vehicle entering the bridge have minimal impact on it. Meanwhile, it is shown that the framework can accurately perceive front blind areas of vehicles driving on the bridge, and identify different vehicle models, speeds and times of vehicle bridge entries in real time.

Are sunglasses appropriate for driving? Investigation and prototype for public testing.

BACKGROUND: Good vision through sunglasses is important to safety when driving and ISO 12312-1:2013 sets requirements for luminous transmittance and the transmittance of traffic signals. METHODS: We measured the spectral transmittances, 380-780 nm in 5-nm steps, of 232 sunglasses lenses and calculated the luminous transmittance, category (1-4) and transmittance of red, yellow, green and blue traffic signals (Q values). Furthermore, we developed a prototype for the general public to self-check sunglasses regarding safety for driving. We combined a white LED, a photodetector, and calculations to measure luminous transmittance, traffic signal transmittance, category, and Q-factors in sunglasses. RESULTS: Spectroscopy shows that 75% of sunglasses on the Brazilian market comply with ISO 12312-1:2013 requirements to be suitable for driving. The prototype was validated by testing 232 samples by trained users. Additionally, 60 other samples were tested by untrained users and results were compared to spectrophotometric measurements. Bland-Altman analyses showed no significant biases and 95% agreement of limits within the pre-defined tolerances for all measurements. CONCLUSIONS: Our prototype offers the general public a way to check whether their sunglasses are suitable for driving. As tested, 24.6% of sunglasses are not appropriate for driving and consumer must be more attentive to this information. SIGNIFICANCE: Immediate attention regarding checking sunglasses for driving conditions is needed for non-certified sunglasses.

A Novel Approach to Global Positioning System Accuracy Assessment, Verified on LiDAR Alignment of One Million Kilometers at a Continent Scale, as a Foundation for Autonomous DRIVING Safety Analysis.

This paper concerns a new methodology for accuracy assessment of GPS (Global Positioning System) verified experimentally with LiDAR (Light Detection and Ranging) data alignment at continent scale for autonomous driving safety analysis. Accuracy of an autonomous driving vehicle positioning within a lane on the road is one of the key safety considerations and the main focus of this paper. The accuracy of GPS positioning is checked by comparing it with mobile mapping tracks in the recorded high-definition source. The aim of the comparison is to see if the GPS positioning remains accurate up to the dimensions of the lane where the vehicle is driving. The goal is to align all the available LiDAR car trajectories to confirm the of accuracy of GNSS + INS (Global Navigation Satellite System + Inertial Navigation System). For this reason, the use of LiDAR metric measurements for data alignment implemented using SLAM (Simultaneous Localization and Mapping) was investigated, assuring no systematic drift by applying GNSS+INS constraints. The methodology was verified experimentally using arbitrarily chosen measurement instruments (NovAtel GNSS + INS, Velodyne HDL32 LiDAR) mounted onto mobile mapping systems. The accuracy was assessed and confirmed by the alignment of 32,785 trajectories with a total length of 1,159,956.9 km and a total of 186.4 × 109 optimized parameters (six degrees of freedom of poses) that cover the United States region in the 2016-2019 period. The alignment improves the trajectories; thus the final map is consistent. The proposed methodology extends the existing methods of global positioning system accuracy assessment, focusing on realistic environmental and driving conditions. The impact of global positioning system accuracy on autonomous car safety is discussed. It is shown that 99% of the assessed data satisfy the safety requirements (driving within lanes of 3.6 m) for Mid-Size (width 1.85 m, length 4.87 m) vehicles and 95% for Six-Wheel Pickup (width 2.03-2.43 m, length 5.32-6.76 m). The conclusion is that this methodology has great potential for global positioning accuracy assessment at the global scale for autonomous driving applications. LiDAR data alignment is introduced as a novel approach to GNSS + INS accuracy confirmation. Further research is needed to solve the identified challenges.

Patterns in transitions of visual attention during baseline driving and during interaction with visual-manual and voice-based interfaces.

Voice interfaces reduce visual demand compared with visual-manual interfaces, but the extent depends on design. This study compared visual demand during baseline driving with driving while using voice or manual inputs to place calls with Chevrolet MyLink, Volvo Sensus, or a smartphone. Mean glance duration and total eyes-off-road-time increased when using manual input compared with baseline driving; only eyes off road time increased with voice input. Confusion matrices developed with hidden Markov modelling characterise the similarity of glance sequences during baseline driving and while making phone calls. Glance sequences with the MyLink voice interface were misclassified as baseline driving more frequently than the other voice interfaces. Conversely, glance sequences with the Sensus and smartphone voice interfaces were more often misclassified as manual phone calling. Thus, the MyLink voice interface not only reduced the overall visual demand of placing calls, but produced glance patterns more similar to driving without another task. Practitioner Summary: The attention map and confusion matrix methodologies provide ways of characterising similarities and differences in glance behaviour across secondary task conditions, complementing traditional temporally based metrics (e.g. mean glance duration, long duration glances) while addressing some of the limitations of total-eyes-off-road-time (TEORT) for comparing secondary task behaviour to baseline driving.

An Integrative Review of Return to Driving After Concussion in Adolescents.

Concussion is a common injury among adolescents. It is unknown how a concussion affects driving behavior and performance in adolescents. Although there are guidelines for return to learn, exercise, and sports that school nurses can help support in school, little is known about return to driving after concussion. The goal of this integrative review was to summarize the current literature on return to driving after concussion in adolescents. Six articles published between 2016 and 2020 were included in the review. Physicians and nurse practitioners find providing driving recommendations to adolescents to be appropriate; however, they are unclear what metrics to use to determine whether a patient is fit to drive. Future studies should explore clinical predictors of readiness to return to driving in adolescents. School nurses have an opportunity to support adolescents in their resumption of typical activities after concussion including school, sports, and, as more evidence becomes available, driving.

Driving with cardiac devices in Australia. Does a review of recent evidence prompt a change in guidelines?

Australian Driving Guidelines for patients with pacemakers and implanted cardioverter defibrillators are in line with many around the world, with some minor differences. Some aspects of these guidelines lack contemporary evidence in key decision-making areas and make broad recommendations regarding groups with heterogeneous populations. In addition, more recent studies suggest lower rates of adverse events in some patients with these devices than previously thought. Through a systematic literature review, along with discussion of current guidelines, we combine new evidence with well established risk assessment tools to ask the following questions: (i) Given the heterogeneity of patient risk within the defibrillator population, should guidelines allow for further individualisation of risk and subsequent licensing restrictions?; and (ii) Could some patients with primary prevention automated cardioverter defibrillators be able to hold a commercial driving licence?

The Effects of the Driver's Mental State and Passenger Compartment Conditions on Driving Performance and Driving Stress.

Globalization has increased the number of road trips and vehicles. The result has been an intensification of traffic accidents, which are becoming one of the most important causes of death worldwide. Traffic accidents are often due to human error, the probability of which increases when the cognitive ability of the driver decreases. Cognitive capacity is closely related to the driver's mental state, as well as other external factors such as the CO2 concentration inside the vehicle. The objective of this work is to analyze how these elements affect driving. We have conducted an experiment with 50 drivers who have driven for 25 min using a driving simulator. These drivers completed a survey at the start and end of the experiment to obtain information about their mental state. In addition, during the test, their stress level was monitored using biometric sensors and the state of the environment (temperature, humidity and CO2 level) was recorded. The results of the experiment show that the initial level of stress and tiredness of the driver can have a strong impact on stress, driving behavior and fatigue produced by the driving test. Other elements such as sadness and the conditions of the interior of the vehicle also cause impaired driving and affect compliance with traffic regulations.

Intelligent Driving Assistant Based on Road Accident Risk Map Analysis and Vehicle Telemetry.

Through the application of intelligent systems in driver assistance systems, the experience of traveling by road has become much more comfortable and safe. In this sense, this paper then reports the development of an intelligent driving assistant, based on vehicle telemetry and road accident risk map analysis, whose responsibility is to alert the driver in order to avoid risky situations that may cause traffic accidents. In performance evaluations using real cars in a real environment, the on-board intelligent assistant reproduced real-time audio-visual alerts according to information obtained from both telemetry and road accident risk map analysis. As a result, an intelligent assistance agent based on fuzzy reasoning was obtained, which supported the driver correctly in real-time according to the telemetry data, the vehicle environment and the principles of secure driving practices and transportation regulation laws. Experimental results and conclusions emphasizing the advantages of the proposed intelligent driving assistant in the improvement of the driving task are presented.

A Systematic Methodology to Evaluate Prediction Models for Driving Style Classification.

Identifying driving styles using classification models with in-vehicle data can provide automated feedback to drivers on their driving behavior, particularly if they are driving safely. Although several classification models have been developed for this purpose, there is no consensus on which classifier performs better at identifying driving styles. Therefore, more research is needed to evaluate classification models by comparing performance metrics. In this paper, a data-driven machine-learning methodology for classifying driving styles is introduced. This methodology is grounded in well-established machine-learning (ML) methods and literature related to driving-styles research. The methodology is illustrated through a study involving data collected from 50 drivers from two different cities in a naturalistic setting. Five features were extracted from the raw data. Fifteen experts were involved in the data labeling to derive the ground truth of the dataset. The dataset fed five different models (Support Vector Machines (SVM), Artificial Neural Networks (ANN), fuzzy logic, k-Nearest Neighbor (kNN), and Random Forests (RF)). These models were evaluated in terms of a set of performance metrics and statistical tests. The experimental results from performance metrics showed that SVM outperformed the other four models, achieving an average accuracy of 0.96, F1-Score of 0.9595, Area Under the Curve (AUC) of 0.9730, and Kappa of 0.9375. In addition, Wilcoxon tests indicated that ANN predicts differently to the other four models. These promising results demonstrate that the proposed methodology may support researchers in making informed decisions about which ML model performs better for driving-styles classification.

A sudden variation in the visual field reduces driver's accuracy in estimation of the speed of the car ahead.

We offer the hypothesis that a variation in the visual environment of a driver affects their performance in estimating the speed of a car in front. The hypothesis was tested in a driving simulator with 18 drivers by recording their ability to estimate the relative speed of a car ahead when exposed to sudden variations in the visual environment. The sudden variation was produced by briefly (200 ms) masking the driving environment with a grey frame. The results of our study confirm the hypothesis, as the flashed mask significantly lowered the drivers' accuracy in estimating the speed of a car ahead. The results also show that it is possible to cope with variations in the visual environment and to partially recover from the loss of accuracy. The findings are relevant to the layout of driving environments, such as the placement of dynamic advertisements along the side of the road or the entrance zones of tunnels, and to the training of drivers. Practitioner summary: In our driving simulator study, we showed that sudden transitions in the visual environment reduce a driver's performance in evaluating the speed of the car ahead and are therefore a factor in accidents and traffic jams. Transitions should be limited, and drivers should be prepared for the effect of transitions. Abbreviation: ANOVA: analysis of variance.

Doctor, when can I drive? - Range of functional ankle motion during driving.

BACKGROUND: Driving a motor vehicle needs a specific joint mobility and yet only limited knowledge exists regarding the necessary ankle range of motion. The goal of this study is to characterize the sequence and range of ankle motion. METHODS: The arc of plantarflexion/dorsiflexion and supination/pronation was recorded in the right and left ankle using electrogoniometers while thirty laps were driven by fifteen healthy participants around a course in a manual transmission car with a left sided steering wheel. The driver was required to perform the following maneuvers during each lap: (I) Vehicle acceleration and gear change, (II) Sudden evasion, (III) Routine turning, (IV) Rapid turning, (V) Vehicle acceleration followed by emergency braking. RESULTS: Driving required the right ankle to plantarflex 13±9 and dorsiflex 22±7 while supinating 15±7 degrees and pronating minimally. The left ankle plantarflexed 19±10and dorsiflexed 17±10 while supinating 15±7 degrees and pronating minimally. The right ankle dorsiflexed significantly more (p=0.00), and yet the left ankle had a significantly higher maximum plantarflexion and range of plantarflexion/dorsiflexion (p=0.00). Emergency braking resulted in a significantly higher maximum plantarflexion as well as plantarflexion/dorsiflexion range when compared to other maneuvers. CONCLUSION: This study describes the range of ankle motion identified to drive a car with a manual transmission and a left-sided steering wheel. The right and left ankle exhibit different arcs of motion during driving. This knowledge may assist when evaluating a patient's driving capability. Further studies are needed to investigate whether movement restrictions impair driving. LEVEL OF EVIDENCE: Basic science study.