Effect of spatial relationship between curves on crash severity at horizontal curves in a mountainous terrain.

The study aimed to model the effect of spatial relationship between adjacent curves on the severity of curve-based crashes along with driver and crash causal characteristics, reflecting driver's short-term expectancy. The crash and other associated data was retrieved from the web-based Road Accident Data Management System available in Himachal Pradesh, India, and curvature attributes were extracted using GIS. Overall, the study included 1113 curve based crashes. The driver's perception of the sharpness of a curve was quantified as a single representative categorical factor based simultaneously on its length and radius using K-Medoid Clustering. Separate crash severity models catering to the two possible approach directions of the subject curve were developed reflecting its independent interaction with its corresponding adjacent curve in each direction. Partial Proportional Odds models were developed to overcome the predictive limitations of Ordinal and Multinomial logit models. Indicators of spatial relationship and the intensity of sharpness of the subject curve were found to be statistically significant. A sharp approach curve (radius:40-60 m) increased the risk of fatality by 2.16 times with a similar increase (2.5 times) observed for a short (length:30-60 m) adjacent curve. Adjacent curves turning in the same direction were 2.34 times more prone to fatalities. A very sharp subject curve with radius ≤ 40 m increased the risk of fatal crashes by 2.5 times, as did the short subject curves (30-60 m) (at least 3 times). Subject curves characterized by a short length and a very sharp curvature contributed relatively 3-4 times more to fatal crashes. The identified risk factors and their impact can help the relevant stakeholders to take appropriate actions and can further assist them in identifying high risk scenarios.

Sleep-deprived car-following: Indicators of rear-end crash potential.

Safety assessment among sleep-deprived drivers is a challenging research area with only a few sleep-related studies investigating safety performance during car-following. Therefore, this study aimed to measure the effects of partial sleep deprivation on driver safety during car-following. Fifty healthy male drivers with no prior history of any sleep-related disorders, drove the driving simulator in three conditions of varying sleep duration: a baseline (no sleep deprivation), test session (TS1) after one night of PSD (sleep ≤4.5 h/night) and TS2 after two consecutive nights of PSD. The reduced sleep in PSD sessions was monitored using an Actiwatch. Karolinska Sleepiness Scale was used to indicate loss of alertness among drivers. Each drive included a car-following task to measure longitudinal safety indicators based on speed and headway management: normalized time exposed to critical gap (TECG'), safety critical time headway and speed variability with respect to leading vehicle's speed (SPV). Crash potential index (CPI) was also determined from deceleration rate of drivers during car-following and was found correlated with other indicators. Therefore, to determine the aggregate influence of PSD on safety during car-following, CPI was modelled in terms of TECG, SPV, THW and other covariates. All safety metrics were modelled using generalized mixed effects regression models. The results showed that compared to the baseline drive, critical time headway decreased by 0.65 and 1.08 times whereas speed variability increased by 1.34 and 1.28 times during the TS1 and TS2, respectively, both indicating higher crash risk. However, decrease in TECG' by 64 % and 56 % during TS1 and TS2, respectively indicate compensatory measures to avoid risks due to sleep loss. A fractional regression model of crash potential revealed that low time-headway and higher speed variability and high time exposed to critical gap (TECG') significantly contribute to higher CPI values indicating higher safety risk. Other covariates such as sleep duration, professional driving experience and history of traffic violations were also associated with safety indicators and CPI, however no significant effects of age were noticed in the study. The study findings present the safety indicators sensitive to rear-end crashes specifically under PSD conditions, which can be used in designing collisions avoidance systems and strategies to improve overall traffic safety.

Exploring the effectiveness of a digital voice assistant to maintain driver alertness in partially automated vehicles.

OBJECTIVE: Vehicle automation shifts the driver's role from active operator to passive observer at the potential cost of degrading their alertness. This study investigated the role of an in-vehicle voice-based assistant (VA; conversing about traffic/road environment) to counter the disengaging and fatiguing effects of automation. METHOD: Twenty-four participants undertook two drives- with and without VA in a partially automated vehicle. Participants were subsequently categorized into high and low participation groups (based on their proportion of vocal exchanges with VA). The effectiveness of VA was assessed based on driver alertness measured using Karolinska Sleepiness Scale (KSS), eye-based sleepiness indicators and glance behavior, NASA-TLX workload rating and time to gain motor readiness in response to take-over request and performance rating made by the drivers. RESULTS: Paired samples t-tests comparison of alertness measures across the two drives were conducted. Lower KSS rating, larger pupil diameter, higher glances (rear-mirror, roadside vehicles and signals in the drive with VA) and higher feedback ratings of VA indicated the efficiency of VA in improving driver alertness during automation. However, there was no significant difference in alertness or glance behavior between the driver groups (high and low-PR), although the time to resume steering control was significantly lower in the higher engagement group. CONCLUSION: The study successfully demonstrated the advantages of using a voice assistant (VA) to counter these effects of passive fatigue, for example, by reducing the time to gain motor-readiness following a TOR. The findings show that despite the low engagement in spoken conversation, active listening also positively influenced driver alertness and awareness during the drive in an automated vehicle.

Effects of Partial Sleep Deprivation on Braking Response of Drivers in Hazard Scenarios.

This study aimed at modeling the Response Time (RT) and Total Braking Time (TBT) of drivers under Partial Sleep Deprivation (PSD). Fifty male participants drove the driving simulator in three experimental conditions: two test sessions and a baseline. The two test sessions were conducted after one and two nights of PSD (sleep = 4.25 ±â€¯0.5 h), respectively. Sleep reduction was recorded using a wrist-worn Actiwatch. The baseline session was conducted after full rest (7-8 h sleep/day for a week). The order of test sessions and baseline was randomized. Each test included two hazard events: 1) pedestrians crossing a road and 2) parked vehicles merging into a roadway. Karolinska Sleepiness Scale (KSS) and Sleepiness Symptoms Questionnaire (SSQ) ratings were also recorded during each drive. Four separate models using parametric accelerated failure time (AFT) with Weibull distribution were developed for RT and TBT in the two events. The models were chosen with clustered heterogeneity to account for intra-group heterogeneity due to repeated measures across tests. In the case of pedestrians crossing event, RT increased by 10% in the first test session and no significant effect observed on RT in the second test session. The overall TBT reduced by 25% and 28% during the first and second PSD sessions, respectively. In the case of vehicle merging event, both response time and total braking time delayed by 44% and 17% respectively after PSD. Other factors such as age, experience, work-rest hours, KSS and SSQ rating, often exercising, approaching speed and braking force were also found significant in the analysis. The parametric AFT approach adopted in this study showed the change in 'response time' and 'total braking time' concerning the type of hazard scenario and partial sleep-deprivation.

Effects of driver sleepiness and fatigue on violations among truck drivers in India.

This study aims at capturing the influence of driver drowsiness on prevalence of traffic violations among long-haul truck drivers. The study is based on a roadside survey of 453 long-haul truck drivers, stopping at eateries and rest locations on highways connected to three Indian cities- Mumbai, Indore and Nagpur. The survey questionnaire was categorized into three sections: driver demographics, work-rest schedules and safety critical driver behavior (violations and lapses) in the last five years. The questions regarding unsafe driving practices like speeding, overtaking were combined to form a single factor 'violations' using Principal Component Analysis (PCA). A generalized linear model using negative binomial regression predicted young drivers (aged below 25 years), long working hours, insufficient sleeping hours, driving after mid-night, sleepiness on the wheel and frequent traffic violations as significant contributors of violations among the long-haul truck drivers.

Pregnancy-related acute renal failure in eastern India.

OBJECTIVE: To study the causes, burden, and impact of pregnancy-related acute renal failure (PRARF) in a low-resource setting. METHODS: A prospective observational study of consecutive women admitted to the Seth Sukhlal Karnani Memorial Hospital, Kolkata, India, with PRARF between February 1, 2007, and May 31, 2009, was conducted. The information collected included causes and predisposing conditions leading to PRARF, clinical presentations, medical and obstetric management-including need for dialysis-and maternal outcome. RESULTS: The leading causes of PRARF among the 57 participants were sepsis 19 (33.3%), hemorrhage 16 (28.1%), and hypertensive disorders 15 (26.3%). The condition occurred in 11 (19.3%) cases before 20 weeks of gestation, 6 (10.5%) cases after 20 weeks of gestation, and 40 (70.2%) cases during puerperium. Most women were from rural areas and received inadequate prenatal care. Although 40 women had PRARF during puerperium, the precipitating event mostly started during the process of labor/delivery. Despite multidisciplinary care, mortality was high (28.1%); cause-specific fatality was highest with sepsis (36.8%) and hemorrhage (25.0%). Many women who survived experienced prolonged morbidity. CONCLUSIONS: Pregnancy-related acute renal failure is potentially fatal but largely preventable. Universal prenatal care and greater access to emergency obstetric services, especially in rural India, could avert PRARF and its consequences.

Successful pregnancy outcome among women with end-stage renal disease requiring haemodialysis.

Pregnancy is rare in women with end-stage renal disease, and perinatal outcome remains suboptimal because of prematurity and foetal growth restriction. Successful obstetrical outcome in two women presented with chronic renal failure requiring serial haemodialysis and multiple blood transfusions during pregnancy is reported. Both women had vaginal delivery of low birth weight neonates--2100 g and 1540 g at 33 and 37 weeks' gestations respectively. With specialised neonatal care, both neonates survived, and the mothers were counselled for renal replacement therapy.