Injury risk assessment based on pre-crash variables: The role of closing velocity and impact eccentricity

Gulino, Michelangelo-Santo; Gangi, Leonardo Di; Sortino, Alessio; Vangi, Dario

doi:10.1016/j.aap.2020.105864

Cited by 16 publications

(21 citation statements)

References 62 publications

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“…In [21], a dimensionless crash severity index is proposed to model the oblique collisions with a non-zero coefficient of restitution, which can accommodate more combinations of vehicle attributes and impact conditions, and the model is validated by comparing to the data from EuroNCAP. The authors further extended and applied this model in [22][23].…”

Section: Related Workmentioning

confidence: 99%

“…For detailed calculation of each predicted crash corresponding to a path sample, the vehicle-to-vehicle contact point at the beginning, namely point of impact (POI), is taken as the collision point for subsequent force analysis [22][23][24]. After determining the contact point, we need to determine the PDOF, then the force arms and other parameters of the two vehicles can also be obtained.…”

Section: Crash Severity Index Model For Optimal Path Selectionmentioning

confidence: 99%

See 1 more Smart Citation

Vehicle Crash Mitigation Strategy in Unavoidable Collision Scenarios: Focusing on Motion Planning by Considering a Generalized Crash Severity Model

Li¹,

Hu²

2022

Preprint

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Motion planning in dynamic environment is crucial to the automated driving safety. In extremely emergency scenarios with unavoidable collisions, especially those with complex impact patterns, the potential crash risk should be well considered in motion planning. This paper proposes a motion planning algorithm for unavoidable collisions, which directly embeds a generalized crash severity index model to vehicle-to-vehicle collisions of multiple impact patterns. Firstly, the clothoid curve is used to sample the vehicle trajectory before collision, and a two-degree-of-freedom model is adopted to predict the vehicle poses corresponding to each sample path. Then, the crash severity index model is to estimate the potential crash severity of all sample paths. To improve the inferring time efficiency, a neural network is constructed and deployed to approximate the nonlinear severity model. Finally, the crash-severity-optimal trajectory is tracked through model predictive control method. Results show that by combining the braking and steering interventions for better crash severity reduction, the proposed strategy can achieve better mitigation effects than commonly-used collision-avoidance strategies. The deployment of real car experiment and sensitivity analysis demonstrate that the planning algorithm can guarantee real-time and reliably safe performances.

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Section: Related Workmentioning

confidence: 99%

Section: Crash Severity Index Model For Optimal Path Selectionmentioning

confidence: 99%

Vehicle Crash Mitigation Strategy in Unavoidable Collision Scenarios: Focusing on Motion Planning by Considering a Generalized Crash Severity Model

Li¹,

Hu²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Nevertheless, improving the quality of the accident datasets still requires further consideration to improve crash severity. [17] employed and compared several statistical learning techniques including Regression of Logistics, Random Forest, Adaptive Regression Multivariates, and the Support Vector Machines as well as the Bayesian neural network to deal with binary classification problems. An imbalanced high-resolution database of road accidents in Austria is used to analyze the consequences of 40 different incident variables.…”

Section: Literature Reviewmentioning

confidence: 99%

Profiling Casualty Severity Levels of Road Accident Using Weighted Majority Voting

Awan¹,

Mehmood²,

Chaudhry³

et al. 2022

Computers, Materials &Amp; Continua

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To determine the individual circumstances that account for a road traffic accident, it is crucial to consider the unplanned connections amongst various factors related to a crash that results in high casualty levels. Analysis of the road accident data concentrated mainly on categorizing accidents into different types using individually built classification methods which limit the prediction accuracy and fitness of the model. In this article, we proposed a multi-model hybrid framework of the weighted majority voting (WMV) scheme with parallel structure, which is designed by integrating individually implemented multinomial logistic regression (MLR) and multilayer perceptron (MLP) classifiers using three different accident datasets i.e., IRTAD, NCDB, and FARS. The proposed WMV hybrid scheme overtook individual classifiers in terms of modern evaluation measures like ROC, RMSE, Kappa rate, classification accuracy, and performs better than state-of-theart approaches for the prediction of casualty severity level. Moreover, the proposed WMV hybrid scheme adds up to accident severity analysis through knowledge representation by revealing the role of different accident-related factors which expand the risk of casualty in a road crash. Critical aspects related to casualty severity recognized by the proposed WMV hybrid approach can surely support the traffic enforcement agencies to develop better road safety plans and ultimately save lives.

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“…The ratio between these two variations (F-value) could be used to derive the p-value metrics, i.e., the probability that no significant difference exists among the considered samples. Overall, the statistical analysis tests show the hypothesis that each of the parameters has null influence on the variables holds: in the context of road safetyrelated studies [36], a p-value lower than 0.05 is considered sufficient to reject the hypothesis that the parameter (factor) has no influence on the variable (response). Hence, as can be seen from Table 1 and apart from a few isolated cases, all parameters are statistically influential on the variables of interest; that is, the performances of the rider-vehicle system depend on the characteristics of both elements.…”

Section: Closed-circuit Testsmentioning

confidence: 99%

Exploring Performances of Electric Micro-Mobility Vehicles and Behavioural Patterns of Riders for In-Depth Accident Analysis

Gulino

Zonfrillo

Damaziak

et al. 2021

Designs

Self Cite

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(1) Background: Electric micro-mobility vehicles (i.e., e-bikes and e-scooters) represent a fast-growing portion of the circulating fleet, leading to a multiplication of accident cases also attributable to risky behaviours adopted by the riders. Still, data on vehicle performance and rider behaviour are sparse and difficult to interpret (if not unavailable). Information regarding the overall accident dynamics can, however, aid in identifying users’ risky riding behaviour that actually led to a harmful event, allowing one to propose efficient strategies and policies to reduce the occurrence of road criticalities. (2) Methods: Speed and acceleration data of six cyclists of traditional and electric bikes were extracted from six closed-circuit experiments and real road tests performed in the city of Florence (Italy) to derive their behavioural patterns in diverse road contexts. (3) Results: The application of analysis of variance and linear regression procedures to such data highlights differences between men and women in terms of performance/behaviour in standing start; additionally, the use of e-bikes favours a higher speed ride in correspondence to roundabouts and roads with/without the right of way. To thoroughly assess the rider’s responsibilities in an eventual accident, an ancillary procedure was highlighted to evaluate whether a micro-mobility vehicle complies with the applicable regulations. (4) Conclusion: With these results, the prospective recognition of rider behaviour was facilitated during the investigation process, and the abilities to extract such relevant information from in-depth accident data wereconsequently enhanced.

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Injury risk assessment based on pre-crash variables: The role of closing velocity and impact eccentricity

Cited by 16 publications

References 62 publications

Vehicle Crash Mitigation Strategy in Unavoidable Collision Scenarios: Focusing on Motion Planning by Considering a Generalized Crash Severity Model

Vehicle Crash Mitigation Strategy in Unavoidable Collision Scenarios: Focusing on Motion Planning by Considering a Generalized Crash Severity Model

Profiling Casualty Severity Levels of Road Accident Using Weighted Majority Voting

Exploring Performances of Electric Micro-Mobility Vehicles and Behavioural Patterns of Riders for In-Depth Accident Analysis

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