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

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

Driving context influences drivers\u27 decision to engage in visual-manual phone tasks: evidence from a naturalistic driving study

Visual-manual phone tasks (i.e., texting, dialing, reading) are associated with anincreased crash risk. This study investigated how the driving context influences drivers\u27 decisions toengage in visual-manual phone tasks in naturalistic driving. Method: Video-recordings of 1432 car tripswere viewed to identify visual-manual phone tasks and passenger presence. Video, vehicle signals, andmap data were used to classify driving context (i.e., curvature, other vehicles) before and during thephone tasks (N=374). Vehicle signals (i.e., speed, yaw rate, forward radar) were available for alldriving. Results: The drivers were more likely to engage in phone tasks while standing still, and lesslikely while driving at high speeds or executing sharp turns, or when a passenger was present. Leadvehicle presence did not influence how likely drivers were to engage, but they adjusted their tasktiming to situations when the lead vehicle was increasing speed, resulting in increasing time headway.The drivers adjusted task timing until after making sharp turns and lane change maneuvers. Incontrast to previous driving simulator studies, there was no evidence of drivers reducing speed as aconsequence of phone task engagement. Conclusions: The results show that experienced drivers areskilled at using information about current and upcoming driving context to decide when to safelyengage in visual-manual phone tasks. However, drivers may fail to sufficiently increase safety marginsto allow time to respond to possible unpredictable events (e.g., lead vehicle braking). PracticalApplications: Advanced driver assistance systems should facilitate and possibly boost drivers\u27 selfregulatingbehavior. For instance, they might recognize when appropriate adaptive behavior is missingand advice or alert accordingly. The results from this study could also inspire training programs fornovice drivers, or locally classify roads in terms of the risk associated with secondary task engagementwhile driving

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

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

Modeling collision avoidance maneuvers for micromobility vehicles

Introduction: In recent years, as novel micromobility vehicles (MMVs) have hit the market and rapidly gained popularity, new challenges in road safety have arisen, too. There is an urgent need for validated models that comprehensively describe the behaviour of such novel MMVs. This study aims to compare the longitudinal and lateral control of bicycles and e-scooters in a collision- avoidance scenario from a top-down perspective, and to propose appropriate quantitative models for parameterizing and predicting the trajectories of the avoidance—braking and steering— maneuvers. Method: We compared a large e-scooter and a light e-scooter with a bicycle (in assisted and non-assisted modes) in field trials to determine whether these new vehicles have different maneuverability constraints when avoiding a rear-end collision by braking and/or steering. Results: Braking performance in terms of deceleration and jerk varies among the different types of vehicles; specifically, e-scooters are not as effective at braking as bicycles, but the large e-scooter demonstrated better braking performance than the light one. No statistically significant difference was observed in the steering performance of the vehicles. Bicycles were perceived as more stable, maneuverable, and safe than e-scooters. The study also presents arctangent kinematic models for braking and steering, which demonstrate better accuracy and informativeness than linear models. Conclusions: This study demonstrates that the new micromobility solutions have some maneuverability characteristics which differ significantly from those of bicycles, and even within their own kind. Steering could be a more efficient collision- avoidance strategy for MMVs than braking under certain circumstances, such as in a rear-end collision. More complicated modelling for MMV kinematics can be beneficial but needs validation. Practical Applications: The proposed arctangent models could be used in new advanced driving assistance systems to prevent crashes between cars and MMV users. Micromobility safety could be improved by educating MMV riders to adapt their behavior accordingly. Further, knowledge about the differences in maneuverability between e-scooters and bicycles could inform infrastructure design, and traffic regulations

On the evaluation of visual nudges to promote safe cycling: Can we encourage lower speeds at intersections?

Crashes between cars and cyclists at urban intersections are common, and their consequences are often severe. Typical causes for this type of crashes included the excessive speed of the cyclist as well as car drivers failing to see the cyclist. Measures that decrease the cyclists’ speed may lead to safer car-cyclist interactions. This study aimed to investigate the extent to which cyclists may approach intersections at a lower speed when nudged to do so.Visual flat-stripe nudges were placed on bicycle lanes in the proximity of uncontrolled intersections (with a history of car-cyclist crashes) in two locations in Gothenburg, Sweden. This specific nudge was the one obtaining the best results from a previous study that tested different nudges in controlled experiments. Video data from the intersections were recorded with a site-based video recording system both before (baseline), and after (treatment), the nudge was installed.\ua0 The video data was processed to extract trajectory and speed for cyclists. The baseline and treatment periods were equivalent in terms of day of the week, light, and weather conditions. Furthermore, two treatment periods were recorded to capture the effect of the nudge over time in one of the locations.Leisure cyclists showed lower speeds in treatment than in baseline for both locations. Commuters were less affected by the nudge than leisure cyclists. This study shows that visual nudges to decrease cyclist speed at intersections are hard to evaluate in the wild because of the many confounders. We also found that the effect of visual nudges may be smaller than the effect of environmental factors such as wind and demographics, making their evaluation even harder. The observed effect of speed might not be very high, but the advantage both in terms of cyclist acceptance and monetary cost makes an investment in the measure very low risk. This study informs policymakers and road authorities that want to promote countermeasures to intersection crashes and improve the safety of cyclists at urban intersections

A Bayesian reference model for visual time-sharing behaviour in manual and automated naturalistic driving

Visual time-sharing (VTS) behavior characterizes an inattentive driver. Because inattention has been identified as the major contributing factor in traffic crashes, understanding the relation between VTS and crash risk could help reduce crash risk through the development of inattention countermeasures. The aims of this study are 1) to develop a reference model of VTS behavior and 2) reveal if vehicle automation influences VTS behavior. The reference model was based on naturalistic eye-tracking data. VTS sequences were extracted from routine driving data (including manual and automated driving). We used Bayesian Generalized Linear Mixed Models for a range of on- and off-path glance-based metrics. Each parameter was estimated with a probability distribution and summarized with credible intervals containing the model parameters with 95% probability. The reference model corroborates previous findings from driving simulator experiments and on-road studies, but also captures the characteristics of on-path and off-path glance behavior in greater detail. The model demonstrated that 1) there was minimal change in VTS behavior due to automation, and 2) the percentage of time that glances fell on-path (PRC) was greater for all routine driving (~80%) than for VTS sequences (~50%). The PRC was the only metric that was sensitive to VTS, but it did not differentiate between manual and automated driving. Our model, by describing a measure of inattention (VTS behavior), can be used in future driver models to improve the computer simulations used to design ADASs and evaluate their safety benefits. Additionally, the model could serve as a detailed reference for inattention guidelines

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