Drivers anticipate lead-vehicle conflicts during automated longitudinal control: Sensory cues capture driver attention and promote appropriate and timely responses

Adaptive Cruise Control (ACC) has been shown to reduce the exposure to critical situations by maintaining a safe speed and headway. It has also been shown that drivers adapt their visual behavior in response to the driving task demand with ACC, anticipating an impending lead vehicle conflict by directing their eyes to the forward path before a situation becomes critical. The purpose of this paper is to identify the causes related to this anticipatory mechanism, by investigating drivers’ visual behavior while driving with ACC when a potential critical situation is encountered, identified as a forward collision warning (FCW) onset (including false positive warnings). This paper discusses how sensory cues capture attention to the forward path in anticipation of the FCW onset. The analysis used the naturalistic database EuroFOT to examine visual behavior with respect to two manually-coded metrics, glance location and glance eccentricity, and then related the findings to vehicle data (such as speed, acceleration, and radar information). Three sensory cues (longitudinal deceleration, looming, and brake lights) were found to be relevant for capturing driver attention and increase glances to the forward path in anticipation of the threat; the deceleration cue seems to be dominant. The results also show that the FCW acts as an effective attention-orienting mechanism when no threat anticipation is present. These findings, relevant to the study of automation, provide additional information about drivers’ response to potential lead-vehicle conflicts when longitudinal control is automated. Moreover, these results suggest that sensory cues are important for alerting drivers to an impending critical situation, allowing for a prompt reaction

Ação retificadora de registro civil

Trata-se da redação de um recurso de apelação, aborda os meios de apelação e as razões para tal ato. Tem como foco a retificação do registro civil para mudança de prenome e sexo, com base a Lei 6.015, de 31 de dezembro de 1973, e seus efeitos. Traz em seu conteúdo um “modelo de apelação” e comentários abrangendo o tema em todo esse contexto

What is the relation between crashes from crash databases and near crashes from naturalistic data?

Naturalistic cycling data are increasingly available worldwide and promise ground-breaking insights into road-user behaviour and crash-causation mechanisms. Because few, low-severity crashes are available, safety analyses of naturalistic data often rely on near-crashes. Nevertheless, the relation between near-crashes and crashes is still unknown, and the debate on whether it is legitimate to use near-crashes as a proxy for crashes is still open. This paper exemplifies a methodology that combines crashes from a crash database and near-crashes from naturalistic studies to explore their potential relation. Using exposure to attribute a risk level to individual crashes and near-crashes depending on their temporal and spatial distribution, this methodology proposes an alternative to blackspots for crash analysis and compares crash risk with near-crash risk. The novelty of this methodology is to use exposure with high time and space resolution to estimate the risk for specific crashes and near-crashes

Protein kinase B/AKT isoform 2 drives migration of human mesenchymal stem cells.

This study was designed to investigate the migratory behavior of adult human mesenchymal stem cells (MSC) and the underlying mechanism. Cell migration was assessed by transwell, wound healing and time-lapse in vivo motility assays. Pharmacological inhibitors were used to determine the potential mechanism responsible for cell migration and invasion. The tests that were implemented revealed that MSC were fairly migratory. Protein kinase B (AKT) was strongly activated at the basal level. Through our analyses we demonstrated that pharmacological inactivation of AKT2 but not AKT1 significantly decreased cell migration and invasion. Although preliminary, collectively our results indicate that AKT2 activation plays a critical role in enabling MSC migration

Modeling Drivers’ Strategy When Overtaking Cyclists in the Presence of Oncoming Traffic

Overtaking a cyclist on a two-lane rural road with oncoming traffic is a challenging task for any driver. Failing this task can lead to severe injuries or even death, because of the potentially high impact speed in a possible collision. To avoid a rear-end collision with the cyclist, drivers need to make a timely and accurate decision about whether to steer and overtake the cyclist, or brake and let the oncoming traffic pass first. If this decision is delayed, for instance because the driver is distracted, neither braking nor steering may eventually keep the driver from crashing—at that point, rear-ending a cyclist may be the safest alternative for the driver. Active safety systems such as forward collision warning that help drivers being alert and avoiding collisions may be enhanced with driver models to reduce activations perceived as false positive. In this study, we developed a driver model based on logistic regression using data from a test-track experiment. The model can predict the probability and confidence of drivers braking and steering while approaching a cyclist during an overtaking, and therefore this model may improve collision warning systems. In both an in-sample and out-of-sample evaluation, the model identified drivers’ intent to overtake with high accuracy (0.99 and 0.90, respectively). The model can be integrated into a warning system that leverages the deviance of the actual driver behavior from the behavior predicted by the model to allow timely warnings without compromising driver acceptance

Stroke: epidemiology and outcomes

Objective: To determine the frequency of mortality, length of stay and nosocomial pneumonia outcomes, as well as their distribution according to predictor variables, in stroke patients treated at the emergency room of a tertiary hospital. Methods: A retrospective cohort study, with a sample of patients attended between January 1 and December 31, 2018. Based on the data collected in the medical records, the sample was characterized. Therefore, the frequency of each outcome was checked, as well as its distribution according to the predictor variables. Results: The sample population consisted of 210 patients. The frequencies observed in death and nosocomial pneumonia were 17.6% and 17.1%, respectively. The general mean length of stay was 13.8 ±12.9 days. Statistically significant differences were observed both in the occurrence of nosocomial pneumonia and atrial fibrillation (AF); days of hospitalization in intensive care unit; total days of hospitalization; orotracheal intubation; use of nasoenteral tube and surgical procedure secondary to stroke. Morever, there was also the relation of total time of hospitalization regarding dyslipidemia; orotracheal intubation; use of nasoenteral tube and surgical procedure secondary to stroke. Conclusion: The results found in the frequency of mortality, nosocomial pneumonia and mean total number of days of hospitalization are comparable with other Brazilian studies. However, it is possible to optimize the time of care provided for patients who arrive in the emergency room. In addition, the decrease of hospitalization days in dyslipidemic patients and the increase of nosocomial pneumonia in AF patients require further studies to verify such findings

Driver Visual Attention Before and After Take-Over Requests During Automated Driving on Public Roads

ObjectiveThis study aims to understand drivers’ visual attention before and after take-over requests during automated driving (AD), when the vehicle is fully responsible for the driving task on public roads.BackgroundExisting research on transitions of control from AD to manual driving has mainly focused on take-over times. Despite its relevance for vehicle safety, drivers’ visual attention has received little consideration.MethodThirty participants took part in a Wizard of Oz study on public roads. Drivers’ visual attention was analyzed before and after four take-over requests. Visual attention during manual driving was also recorded to serve as a baseline for comparison.ResultsDuring AD, the participants showed reduced visual attention to the forward road and increased duration of single off-road glances compared to manual driving. In response to take-over requests, the participants looked away from the forward road toward the instrument cluster. Levels of visual attention towards the forward road did not return to the levels observed during manual driving until after 15\ua0s had passed.ConclusionDuring AD, drivers may look toward non-driving related task items (e.g., mobile phone) instead of forward. Further, when a transition of control is required, drivers may take over control before they are aware of the driving environment or potential threat(s). Thus, it cannot be assumed that drivers are ready to respond to events shortly after the take-over request.ApplicationIt is important to consider the effect of the design of take-over requests on drivers’ visual attention alongside take-over times

A data-driven framework for the safe integration of micro-mobility into the transport system: Comparing bicycles and e-scooters in field trials

Introduction: Recent advances in technology create new opportunities for micro-mobility solutions even as they pose new challenges to transport safety. For instance, in the last few years, e-scooters have become increasingly popular in several cities worldwide; however, in many cases, the municipalities were simply unprepared for the new competition for urban space between traditional road users and e-scooters, so that bans became a necessary, albeit drastic, solution. In many countries, traditional vehicles (such as bicycles) may not be intrinsically safer than e-scooters but are considered less of a safety threat, possibly because—for cyclists—social norms, traffic regulations, and access to infrastructure are established, reducing the number of negative stakeholders. Understanding e-scooter kinematics and e-scooterist behavior may help resolve conflicts among road users, by favoring a data-driven integration of these new e-vehicles into the transport system. In fact, regulations and solutions supported by data are more likely to be acceptable and effective for all stakeholders. As new personal-mobility solutions enter the market, e-scooters may just be the beginning of a micro-mobility revolution. Method: This paper introduces a framework (including planning, execution, analysis, and modeling) for a data-driven evaluation of micro-mobility vehicles. The framework leverages our experience assessing bicycle dynamics in real traffic to make objective and subjective comparisons across different micro-mobility solutions. In this paper, we use the framework to compare bicycles and e-scooters in field tests. Results: The preliminary results show that e-scooters may be more maneuverable and comfortable than bicycles, although the former require longer braking distances. Practical Applications: Data collected from e-scooters may, in the short term, facilitate policy making, geo-fencing solutions, and education; in the long run, the same data will promote the integration of e-scooters into a cooperative transport system in which connected automated vehicles share the urban space with micro-mobility vehicles. Finally, the framework and the models presented in this paper may serve as a reference for the future assessment of new micro-mobility vehicles and their users’ behavior (although advances in technology and novel micro-mobility solutions will inevitably require some adjustments)

Modeling the Braking Behavior of Micro-Mobility Vehicles

According to the community database on accidents on the roads in Europe, 2035 cyclist fatalities happened in Europe in 2019 [S]. In Sweden, 10440 bicycle crashes were reported in the Swedish Traffic Accident Data Acquisition database during 2019, and 30% of the cyclist fatalities were in car-to-cyclist rear-end crashes [6]. Nowadays, new micromobility vehicles (MMVs), for example, e-scooters, and Segways, are becoming more popular. Unlike traditional bicycles, these new MMVs usually have novel designs in appearance, kinematics, operation method, and power source (e.g., electricity-driven/assisted), which bring new hazards to traditional road users [1, 4]. Thus, it is essential to understand and quantify the behavior of the new MMV users to improve road safety