Crash probability estimation via quantifying driver hazard perception

Modeling distracted driving behavior considering cognitive processes

The modeling of distracted driving behavior has been studied for many years, however, there remain many distraction phenomena that can not be fully modeled. This study proposes a new method that establishes the model using the queuing network model human processor (QN-MHP) framework. Unlike previous models that only consider distracted-driving-related human factors from a mathematical perspective, the proposed method reflects the information processing in the human brain, and simulates the distracted driver's cognitive processes based on a model structure supported by physiological and cognitive research evidence. Firstly, a cumulative activation effect model for external stimuli is adopted to mimic the phenomenon that a driver responds only to stimuli above a certain threshold. Then, dual-task queuing and switching mechanisms are modeled to reflect the cognitive resource allocation under distraction. Finally, the driver's action is modeled by the Intelligent Driver Model (IDM). The model is developed for visual distraction auditory distraction separately. 773 distracted car-following events from the Shanghai Naturalistic Driving Study data were used to calibrate and verify the model. Results show that the model parameters are more uniform and reasonable. Meanwhile, the model accuracy has improved by 57% and 66% compared to the two baseline models respectively. Moreover, the model demonstrates its ability to generate critical pre-crash scenarios and estimate the crash rate of distracted driving. The proposed model is expected to contribute to safety research regarding new vehicle technologies and traffic safety analysis.

Effect of Five Driver's Behavior Characteristics on Car-Following Safety

Driver's behavior characteristics (DBCs) influence car-following safety. Therefore, this paper aimed to analyze the effect of different DBCs on the car-following safety based on the desired safety margin (DSM) car-following model, which includes five DBC parameters. Based on the Monte Carlo simulation method, the effect of DBCs on car-following safety is investigated under a given rear-end collision (RECs) condition. We find that larger subjective risk perception levels can reduce RECs, a smaller acceleration sensitivity (or a larger deceleration sensitivity) can improve car-following safety, and a faster reaction ability of the driver can avoid RECs in the car-following process. It implies that DBCs would cause a traffic wave in the car-following process. Therefore, a reasonable value of DBCs can enhance traffic flow stability, and a traffic control strategy can improve car-following safety by using the adjustment of DBCs.

A Case Study on Vestibular Sensations in Driving Simulators

Motion platforms have been used in simulators of all types for several decades. Since it is impossible to reproduce the accelerations of a vehicle without limitations through a physically limited system (platform), it is common to use washout filters and motion cueing algorithms (MCA) to select which accelerations are reproduced and which are not. Despite the time that has passed since their development, most of these algorithms still use the classical washout algorithm. In the use of these MCAs, there is always information that is lost and, if that information is important for the purpose of the simulator (the training simulators), the result obtained by the users of that simulator will not be satisfactory. This paper shows a case study where a BMW 325Xi AUT fitted with a sensor, recorded the accelerations produced in all degrees of freedom (DOF) during several runs, and data have been introduced in mathematical simulation software (washout + kinematics + actuator simulation) of a 6DOF motion platform. The input to the system has been qualitatively compared with the output, observing that most of the simulation adequately reflects the input to the system. Still, there are three events where the accelerations are lost. These events are considered by experts to be of vital importance for the outcome of a learning process in the simulator to be adequate.

Effect of daily car-following behaviors on urban roadway rear-end crashes and near-crashes: A naturalistic driving study

The rear-end crash is one of the most common types of crashes, and key risk factors have been broadly identified in the car-following behaviors preceding a crash. However, the relationships between rear-end crash risk and daily car-following behaviors, or habits, have not been well examined. This study aims to identify the daily car-following behaviors on urban surface roads and urban expressways that have the most influence on rear-end crashes and near-crashes (CNC). Two months of naturalistic driving study data were used to investigate the daily car-following behavior of 54 drivers. A paired t-test and a Wilcoxon matched-pairs signed rank test were conducted to find the differences in behaviors on the two road types, and basic Poisson regression and Poisson hurdle regression models were used to explore significant risk factors. Results revealed that (1) drivers' longitudinal vehicle control, time control, and emergency behaviors are significantly different on urban surface roads and urban expressways; (2) for surface roads, three key influencing factors were ranked, in descending order, as the standard deviation of relative speed, percentage of time gap less than 1 s, and maximum acceleration; (3) for expressways, four key factors were ranked: minimum time gap, maximum deceleration, percentage of TTC less than 5 s, and the percentage of large positive jerk. The knowledge achieved on risky daily driving behaviors can be applied to training drivers to improve safe practices, assist insurance companies in creating usage-based insurance strategies, and support driver assistant systems design.

Quantifying effects of reverse linear perspective as a visual cue on vehicle and platoon crash risk variations in car-following using path analysis

Road markings are prevalent in practice as perceptual countermeasures to crashes, and a great deal of them have been used for speed reduction. However, there is rare seen any equivalent measures especially for distance control. More importantly, the visual perceptual mechanism of road markings on driving behaviors and crash risk is still blur. Given this, in the present study, we comprehensively quantified the effects of reverse linear perspective (RLP) from its origin as a visual cue, produced by a kind of transverse line markings on road, and explored the effects on car-following behaviors and crash risk variations by path analyses imbedded in a structural equations model, which was approximated with naturalistic driving and traffic flow data. In the model, multiple sources of observed factors in visual perception, driver behaviors, and traffic flow characteristics, and exogenous unobserved factors of distance risk perception, speed risk perception, and platoon risk status were comprehensively structured to explain the vehicle crash risk variation and the platoon crash risk variation. The results indicate that (1) distance risk perception, speed risk perception, and platoon risk status were well explanatory and predictive to vehicle crash risk variation and platoon crash risk variation; (2) the effects of reverse linear perspective as a visual cue on driving behaviors and crash risk variations in car-following were adequately quantified by its geometrical characteristics concerning distance perception; (3) the visual cue of reverse linear perspective in addition with initial distance, stopping sight distance, and the type of leading vehicles explained 33 % of the variance in distance risk perception; the temporal frequency, initial speed, and the type of following vehicles explained 23 % of the variance in speed risk perception; distance risk perception, speed risk perception, and platoon risk status combinedly explained 25 % and 22 % of the total variance in vehicle crash risk variation and platoon crash risk variation, respectively; (4) vehicle crash risk variation and platoon crash risk variation were equivalently specified by those observed explanatory factors. The findings of this study suggest the usefulness and importance of understanding the contribution of psychological factors on crash risk, and emphasize that the road markings can be an effective and readily practical countermeasure in easing traffic safety issues.

Evasive behavior-based method for threat assessment in different scenarios: A novel framework for intelligent vehicle

The threat assessment process is a crucial part of intelligent vehicles (IVs) for evaluating the levels of criticality and taking possible measures to avoid the collision, especially for the collision avoidance systems (CAS). In this study, a novel threat assessment framework based on the driver's evasive behavior, namely the CPIC, is proposed, which integrates the crash probability (CP) and inevitable crash (IC) state to be widely used by different CAS in different scenarios. In the first step of the CPIC framework, the detailed evasive driver behavior models (E-DBMs) in the form of probability density functions (PDFs) were introduced to generate more realistic collision-avoidance trajectories. Two techniques for sampling these trajectories, namely the Markov Chain Monte Carlo (MCMC) and adaptive Gaussian mixture framework (GMM) methods, were utilized to ensure the samples were from the area of high probability density in the E-DBMs. The CP value could be derived by considering multiple collision-avoidance trajectories. To confirm the IC state in step 2, the CPIC framework employed the driving limit-based approach for IC checking, which combined the CP value to double-check the unavoidable collision. A total of 82 critical events from the real-world naturalistic driving study, the Strategic Highway Research Program 2 (SHRP2), were extracted to verify the performance of the CPIC framework in different scenarios. Results show that the proposed method clearly revealed the risk levels when two vehicles were approaching, and 80 events were successfully identified as near crashes/crashes. Moreover, the real-time performance of the CPIC framework was also demonstrated. The findings indicate this CPIC framework could be used in practical applications of IVs in different scenarios.

Fifty Years of Accident Analysis & Prevention: A Bibliometric and Scientometric Overview

Accident Analysis & Prevention (AA&P) is a leading academic journal established in 1969 that serves as an important scientific communication platform for road safety studies. To celebrate its 50^th anniversary of publishing outstanding and insightful studies, a multi-dimensional statistical and visualized analysis of the AA&P publications between 1969 and 2018 was performed using the Web of Science (WoS) Core Collection database, bibliometrics and mapping-knowledge-domain (MKD) analytical methods, and scientometric tools. It was shown that the annual number of AA&P's publications has grown exponentially and that over the course of its development, AA&P has been a leader in the field of road safety, both in terms of innovation and dissemination. By determining its key source countries and organizations, core authors, highly co-cited published documents, and high burst-strength publications, we showed that AA&P's areas of focus include the "effects of hazard and risk perception on driving behavior", "crash frequency modeling analysis", "intentional driving violations and aberrant driving behavior", "epidemiology, assessment and prevention of road traffic injuries", and "crash-injury severity modeling analysis". Furthermore, the key burst papers that have played an important role in advancing research and guiding AA&P in new directions - particularly those in the fields of crash frequency and crash-injury severity modeling analyses were identified. Finally, a modified Haddon matrix in the era of intelligent, connected and autonomous transportation systems is proposed to provide new insights into the emerging generation of road safety studies.

Quantifying accident risk and severity due to speed from the reaction point to the critical conflict in fatal motorcycle accidents

In fatal road vehicle accidents motorcycles are overrepresented per vehicle kilometre travelled. Fatal accidents involving motorcycles contain mode specific characteristics, and in common with fatal accidents involving all road users, speed typically presents as a significant contributory factor. The aim of the present study is to provide quantitative estimates for the contribution of speed in situations commencing from the reaction location to the safety critical event involving a motorcyclist and resulting in a fatal accident. The contribution of speed to the resulting accident risk and accident severity is considered from this reaction point. A speed-squared versus stopping distance domain, termed the severity-risk space, is examined to determine the accident measures. The defined accident measures, namely, accident risk, accident severity and accident severity risk are calculated for sixteen fatal accidents from a police dataset of recent UK motorcycle accidents. The estimates of the defined measures are provided in terms relative to values estimated for the vehicle travelling at the speed limit at the safety critical event. The relative accident risk in response to a safety critical situation shows a partial speed dependent reaction phase and a speed-squared dependent braking phase and ranges from 1.3 to 2.8. The speed-squared dependent accident severity measure ranges from 1.4 to 7.3 at pre-impact speeds. The relative accident severity risk shows speed squared to speed cubed dependency components during the reaction phase and a speed to the power of four dependent braking phase and ranges from 2.3 to 22.8. In eight cases the collision would have been avoided had the motorcyclist been travelling at the speed limit at the critical point and in the other eight cases the relative accident severity at impact ranged from 1.4 to 17.2. The speed-squared versus stopping distance domain provides an informative parameter space for considering the accident risk and accident severity dimensions of road user accidents.

S. Offset-based curvilinear path estimation for mid vehicle collision detection and avoidance system using MARS

Purpose

The purpose of this paper is to propose a novel curvilinear path estimation model employing multivariate adaptive regression splines (MARS) for mid vehicle collision avoidance. The two-phase path estimation scheme initially uses the offset (position) value of the front and the mid (host) vehicle to build the crisp model. The resulting crisp model is MARS regressed to deliver a closely aligned actual model in the second phase. This arrangement significantly narrows the gap between the estimated and the true path analyzed using the mean square error (MSE) for different offsets on Next Generation Simulation Interstate 80 (NGSIM I-80) data set. The presented model also covers parallel parking by encompassing the reverse motion of the host vehicle in the path estimation, thereby, making it amicable for real-road scenarios.

Design/methodology/approach

The two-phase path estimation scheme initially uses the offset (position) value of the front and the mid (host) vehicle to build the crisp model. The resulting crisp model is MARS regressed to deliver a closely aligned actual model in the second phase.

Findings

This arrangement significantly narrows the gap between the estimated and the true path studied using MSE for different offsets on real (Next Generation Simulation-NGSIM) data. The presented model also covers parallel parking by encompassing the reverse motion of the host vehicle in the path estimation. Thereby, making it amicable for real-road scenarios.

Originality/value

This paper builds a mathematical model that considers the offset and host (mid) vehicles for appropriate path fitting.

Strength or Motor Control: What Matters in High-Functioning Stroke?

Background: The two primary motor impairments that hinder function after stroke are declines in strength and motor control. The impact of motor impairments on functional capacity may vary with the severity of stroke motor impairments. In this study, we focus on high-functioning stroke individuals who experience mild to moderate motor impairments and often resume prior activities or return to work. These tasks require the ability to move independently, placing high demands on their functional mobility. Therefore, the purpose of this study was to quantify impairments in strength and motor control and their contribution to functional mobility in high-functioning stroke.

Methods:Twenty-one high-functioning stroke individuals (Fugl Meyer Lower Extremity Score = 28.67 ± 4.85; Functional Activity Index = 28.47 ± 7.04) and 21 age-matched healthy controls participated in this study. To examine motor impairments in strength and motor control, participants performed the following tasks with the paretic ankle (1) maximum voluntary contractions (MVC) and (2) visuomotor tracking of a sinusoidal trajectory. Strength was quantified as the maximum force produced during ankle plantarflexion and dorsiflexion. Motor control was quantified as (a) the accuracy and (b) variability of ankle movement during the visuomotor tracking task. For functional mobility, participants performed (1) overground walking for 7 meters and (2) simulated driving task. Functional mobility was determined by walking speed, stride length variability, and braking reaction time.

Results: Compared with the controls, the stroke group showed decreased plantarflexion strength, decreased accuracy, and increased variability of ankle movement. In addition, the stroke group demonstrated decreased walking speed, increased stride length variability, and increased braking reaction time. The multiple-linear regression model revealed that motor accuracy was a significant predictor of the walking speed and braking reaction time. Further, motor variability was a significant predictor of stride length variability. Finally, the dorsiflexion or plantarflexion strength did not predict walking speed, stride length variability or braking reaction time.

Conclusions: The impairments in motor control but not strength predict functional deficits in walking and driving in high-functioning stroke individuals. Therefore, rehabilitation interventions assessing and improving motor control will potentially enhance functional outcomes in high-functioning stroke survivors.

Impacts of speed variations on freeway crashes by severity and vehicle type

Speed variations are identified as potentially important predictors of freeway crash rates; however, their impacts on crashes are not entirely known. Existing findings tend to be inconsistent possibly because of the different definitions for speed variations, different crash type consideration or different modelling and data aggregation approaches. This study explores the relationships of speed variations with crashes on a freeway section in the UK. Crashes split by vehicle type (heavy and light vehicles) and by severity mode (killed/serious injury and slight injury crashes) are aggregated based on the similarities of the conditions just before their occurrence (condition-based approach) and modelled using Multivariate Poisson lognormal regression. The models control for speed variations along with other traffic and weather variables as well as their interactions. Speed variations are expressed as two separate variables namely the standard deviations of speed within the same lane and between-lanes over a five-minute interval. The results, similar for all crash types (by coefficient significance and sign), suggest that crash rates increase as the within lane speed variations raise, especially at higher traffic volumes. Higher speeds coupled with greater volume and high between-lanes speed variation also increase crash likelihood. Overall, the results suggest that specific combinations of traffic characteristics increase the likelihood of crash occurrences rather than their individual effects. Identification of these specific crash prone conditions could improve our understanding of crash risk and would support the development of more efficient safety countermeasures.