scholarly journals Safety Assessment and a Parametric Study of Forward Collision-Avoidance Assist Based on Real-World Crash Simulations

2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
MohammadReza Seyedi ◽  
MohammadReza Koloushani ◽  
Sungmoon Jung ◽  
Arda Vanli
Keyword(s):  
Reaction Time ◽  
Real World ◽  
Injury Risk ◽  
Injury Severity ◽  
Reconstruction Method ◽  
Vehicle Speed ◽  
Related Field ◽  
Detection Range ◽  
Crash Data ◽  
Input Parameters

In this study, we selected four real-world rear-end crash scenarios with different crash characteristics. The vehicles involved in those crashes were not equipped with any crash avoidance systems. We then used the accident reconstruction method to build those crash scenarios in PC-Crash software. Then, different FCW/AEB safety algorithms have been defined for a subject vehicle model in each crash scenario and each scenario was simulated for a set of input parameters such as vehicle speed, brake intensity, and driver reaction time. The range and distribution of input parameters were extracted from the related field crash data and available literature. A total number of 16000 simulations have been conducted which produced input-output datasets for further investigations. Finally, the effects of input parameters on simulation outcomes including crash occurrence, AEB activation, injury risk, and vehicle damage have been quantified using the Boruta algorithm. The results indicated that the overall effectiveness of the AEB system was a 57% reduction of rear-end crashes, a 52% reduction of injury severity (striking vehicle’s passengers), and a 47% reduction of damages for striking vehicles. The results also showed that the available AEB algorithms were more effective for the average speed equal to or less than 80 kmph. The speed of the subject vehicle, type of AEB algorithm, sensor detection range, and driver reaction time were the most important parameters on crash outcomes. In addition, the results indicated that the performance of FCW had a direct impact on the effectiveness of the AEB system for the integrated FCW + AEB system.

scholarly journals Is Acceleration a Valid Proxy for Injury Risk in Minimal Damage Traffic Crashes? A Comparative Review of Volunteer, ADL and Real-World Studies

2021 ◽  
Vol 18 (6) ◽  
pp. 2901
Author(s):  
Paul S. Nolet ◽  
Larry Nordhoff ◽  
Vicki L. Kristman ◽  
Arthur C. Croft ◽  
Maurice P. Zeegers ◽  
...  
Keyword(s):  
Real World ◽  
Injury Risk ◽  
Angular Acceleration ◽  
Traffic Crashes ◽  
Rear Impact ◽  
Comparative Review ◽  
Minimal Damage ◽  
Injury Causation ◽  
Crash Tests ◽  
Biomechanical Approach

Injury claims associated with minimal damage rear impact traffic crashes are often defended using a “biomechanical approach,” in which the occupant forces of the crash are compared to the forces of activities of daily living (ADLs), resulting in the conclusion that the risk of injury from the crash is the same as for ADLs. The purpose of the present investigation is to evaluate the scientific validity of the central operating premise of the biomechanical approach to injury causation; that occupant acceleration is a scientifically valid proxy for injury risk. Data were abstracted, pooled, and compared from three categories of published literature: (1) volunteer rear impact crash testing studies, (2) ADL studies, and (3) observational studies of real-world rear impacts. We compared the occupant accelerations of minimal or no damage (i.e., 3 to 11 kph speed change or “delta V”) rear impact crash tests to the accelerations described in 6 of the most commonly reported ADLs in the reviewed studies. As a final step, the injury risk observed in real world crashes was compared to the results of the pooled crash test and ADL analyses, controlling for delta V. The results of the analyses indicated that average peak linear and angular acceleration forces observed at the head during rear impact crash tests were typically at least several times greater than average forces observed during ADLs. In contrast, the injury risk of real-world minimal damage rear impact crashes was estimated to be at least 2000 times greater than for any ADL. The results of our analysis indicate that the principle underlying the biomechanical injury causation approach, that occupant acceleration is a proxy for injury risk, is scientifically invalid. The biomechanical approach to injury causation in minimal damage crashes invariably results in the vast underestimation of the actual risk of such crashes, and should be discontinued as it is a scientifically invalid practice.

scholarly journals Analysis of Severe Injuries in Crashes Involving Large Trucks Using K-Prototypes Clustering-Based GBDT Model

Safety ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 32
Author(s):  
Syed As-Sadeq Tahfim ◽  
Chen Yan
Keyword(s):  
Injury Severity ◽  
Unobserved Heterogeneity ◽  
The United States ◽  
Clustering Methods ◽  
Severe Injuries ◽  
Traffic Crash ◽  
Crash Data ◽  
Boosted Decision Trees ◽  
The Individual ◽  
Crash Characteristics

The unobserved heterogeneity in traffic crash data hides certain relationships between the contributory factors and injury severity. The literature has been limited in exploring different types of clustering methods for the analysis of the injury severity in crashes involving large trucks. Additionally, the variability of data type in traffic crash data has rarely been addressed. This study explored the application of the k-prototypes clustering method to countermeasure the unobserved heterogeneity in large truck-involved crashes that had occurred in the United States between the period of 2016 to 2019. The study segmented the entire dataset (EDS) into three homogeneous clusters. Four gradient boosted decision trees (GBDT) models were developed on the EDS and individual clusters to predict the injury severity in crashes involving large trucks. The list of input features included crash characteristics, truck characteristics, roadway attributes, time and location of the crash, and environmental factors. Each cluster-based GBDT model was compared with the EDS-based model. Two of the three cluster-based models showed significant improvement in their predicting performances. Additionally, feature analysis using the SHAP (Shapley additive explanations) method identified few new important features in each cluster and showed that some features have a different degree of effects on severe injuries in the individual clusters. The current study concluded that the k-prototypes clustering-based GBDT model is a promising approach to reveal hidden insights, which can be used to improve safety measures, roadway conditions and policies for the prevention of severe injuries in crashes involving large trucks.

Examining Driver Injury Severity in Single-Vehicle Road Departure Crashes Involving Large Trucks

2021 ◽  
pp. 036119812110101
Author(s):  
Mehdi Hosseinpour ◽  
Kirolos Haleem
Keyword(s):  
Rural Areas ◽  
Injury Severity ◽  
Factors Affecting ◽  
Crash Data ◽  
Natural Logarithm ◽  
Concrete Barriers ◽  
Local Roads ◽  
Single Vehicle ◽  
Safety Countermeasures ◽  
Injury Severities

Road departure (RD) crashes are among the most severe crashes that can result in fatal or serious injuries, especially when involving large trucks. Most previous studies neglected to incorporate both roadside and median hazards into large-truck RD crash severity analysis. The objective of this study was to identify the significant factors affecting driver injury severity in single-vehicle RD crashes involving large trucks. A random-parameters ordered probit (RPOP) model was developed using extensive crash data collected on roadways in the state of Kentucky between 2015 and 2019. The RPOP model results showed that the effect of local roadways, the natural logarithm of annual average daily traffic (AADT), the presence of median concrete barriers, cable barrier-involved collisions, and dry surfaces were found to be random across the crash observations. The results also showed that older drivers, ejected drivers, and drivers trapped in their truck were more likely to sustain severe single-vehicle RD crashes. Other variables increasing the probability of driver injury severity have included rural areas, dry road surfaces, higher speed limits, single-unit truck types, principal arterials, overturning-consequences, truck fire occurrence, segments with median concrete barriers, and roadside fixed object strikes. On the other hand, wearing seatbelt, local roads and minor collectors, higher AADT, and hitting median cable barriers were associated with lower injury severities. Potential safety countermeasures from the study findings include installing median cable barriers and flattening steep roadside embankments along those roadway stretches with high history of RD large-truck-related crashes.

scholarly journals Real-world car-to-pedestrian-crash data from an urban centre

2010 ◽  
Vol 4 (1) ◽  
Author(s):  
Uli Schmucker ◽  
Melissa Beirau ◽  
Matthias Frank ◽  
Dirk Stengel ◽  
Gerrit Matthes ◽  
...  
Keyword(s):  
Real World ◽  
Urban Centre ◽  
Crash Data

Machine Learning Methods to Analyze Injury Severity of Drivers from Different Age and Gender Groups

2018 ◽  
Vol 2672 (38) ◽  
pp. 171-183 ◽  
Author(s):  
Somayeh Mafi ◽  
Yassir AbdelRazig ◽  
Ryan Doczy
Keyword(s):  
Machine Learning ◽  
Injury Severity ◽  
Road Traffic ◽  
Sensitive Data ◽  
Learning Methods ◽  
Safety Measures ◽  
Crash Data ◽  
Machine Learning Methods ◽  
And Gender ◽  
Gender Groups

Access to non-biased and accurate models capable of predicting driver injury severity of collision events is vital for determining what safety measures should be implemented at intersections. Inadequate models can underestimate the potential for collision events to result in driver fatalities or injuries, which can lead to improperly assessing the safety criteria of an intersection. This study investigates how injury severity differs between drivers of various ages and gender groups using cost-sensitive data-mining models. Previous research efforts have used machine learning methods for predicting injury severity; however, these studies did not consider the consequences (cost) of incorrect predictions. This paper addresses this shortfall by considering the monetary cost of incorrect injury severity predictions when developing C4.5, instance-based (IB), and random forest (RF) machine-learning models. One model of each method was developed for four distinct cohorts of drivers (i.e., younger males, younger females, older males, and older females). Each model considered a selection of driver, vehicular, road/traffic, environmental, and crash parameters for determining if they significantly influenced driver injury severity. A five-year period of two-vehicle crash data collected at signalized intersections in the metropolitan area of Miami, Florida was used in the models. Results indicated that cost-sensitive learning classifiers were superior to regular classifiers at accurately predicting injuries and fatalities of crashes. Among cost-sensitive models, RF outperformed C4.5 and IB models in predicting driver injury severity for four groups of drivers. The models displayed substantial differences in injury severity determinants across the age/gender cohorts.

Morphomic Analysis of Thoracic Injury Risk in Live Subjects From Real World Motor Vehicle Crashes

2014 ◽  
Vol 186 (2) ◽  
pp. 659-660
Author(s):  
E.T. Chang ◽  
S. Holcombe ◽  
C. Kohoyda-Inglis ◽  
J.B. MacWilliams ◽  
C. Parenteau ◽  
...  
Keyword(s):  
Real World ◽  
Injury Risk ◽  
Motor Vehicle ◽  
Motor Vehicle Crashes ◽  
Thoracic Injury ◽  
Vehicle Crashes

scholarly journals Identification of Factors Influencing Injury Severity Prediction (ISP) in Real World Accident Based on NASS-CDS

2015 ◽  
Vol 6 (4) ◽  
pp. 119-125
Author(s):  
Pal Chinmoy ◽  
Tomosaburo Okabe ◽  
Kulothungan Vimalathithan ◽  
Sangolla Narahari ◽  
Manoharan Jeyabharath ◽  
...  
Keyword(s):  
Real World ◽  
Injury Severity ◽  
Factors Influencing ◽  
Severity Prediction

scholarly journals Central Composite Design and Desirability of RSM for Optimization of Biodiesel Production from Mixture of Animal Waste Fat Oil and used Cooking Oil

2020 ◽  
Vol 9 (4) ◽  
pp. 1565-1571
Keyword(s):  
Reaction Time ◽  
Sodium Hydroxide ◽  
Maximum Yield ◽  
Animal Waste ◽  
Sodium Hydroxide Concentration ◽  
Used Cooking Oil ◽  
Input Parameters ◽  
Desirability Approach ◽  
Central Composite ◽  
Waste Fat

In this work, central composite design(CCD) and desirability approach of Response surface methodology (RSM) has been used for optimization of biodiesel yield produced from mixture of animal waste fat oil and used cooking oil (AWO) in the ratio of 1:1through alkaline transesterification process. In this work, methanol quantity, reaction time and sodium hydroxide concentration are selected as input parameters and yield selected as response. The combined effect of methanol quantity, reaction time and sodium hydroxide concentration were investigated and optimized by using RSM. The second order model is generated to predict yield as a function of methanol quantity, reaction time and sodium hydroxide concentration. A statistical model predicted the maximum yield of 96.9779% at 35ml methanol quantity (% v/v of oil), 75 min. reaction time and 0.6g (% wt./v of oil) of sodium hydroxide. Experimentally, the maximum yield of 97% was obtained at the above optimized input parameters. The variation of 0.02% was observed between experimental and predicted values. In this work, an attempt has also made to use desirability approach of RSM to optimize the input parameters to predict maximum yield. Desirability approach predicts maximum yield (97.075%) at CH3OH (35.832% vol. /vol. of oil), NaOH (0.604 % wt./vol. of oil) and reaction time (79.054min.) was found for the AWO.

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