A multivariate method for evaluating safety from conflict extremes in real time

Fu, Chuanyun; Sayed, Tarek

doi:10.1016/j.amar.2022.100244

Cited by 22 publications

(5 citation statements)

References 67 publications

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“…Since CRCs in SHRP 2 were considered as events that required less urgent maneuvering, CRC could be incorporated into driving style identification; and 2. CRCs and near-crashes together may form comprehensive traffic conflicts and traffic conflicts may provide a clearer picture for understanding crash risk [54], real-time traffic safety estimation [55] and how identified driving styles are associated with them.…”

Section: Discussionmentioning

confidence: 99%

Modeling the Impact of Driving Styles on Crash Severity Level Using SHRP 2 Naturalistic Driving Data

Chen

2022

Safety

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Previous studies have examined driving styles and how they are associated with crash risks relying on self-report questionnaires to categorize respondents based on pre-defined driving styles. Naturalistic driving studies provide a unique opportunity to examine this relationship differently. The current study aimed to study how driving styles, derived from real-road driving, may relate to crash severity. To study the relationship, this study retrieved safety critical events (SCEs) from the SHRP 2 database and adopted joint modelling of the number of the aggregated crash severity levels (crash vs. non-crash) using the Diagonal Inflated Bivariate Poisson (DIBP) model. Variables examined included driving styles and various driver characteristics. Among driving styles examined, styles of maintenance of lower speeds and more adaptive responses to driving conditions were associated with fewer crashes given an SCE occurred. Longer driving experiences, more miles driven last year, and being female also reduced the number of crashes. Interestingly, older drivers were associated with both an increased number of crashes and increased number of non-crash SCEs. Future work may leverage more variables from the SHRP 2 database and widen the scope to examine different traffic conditions for a more complete picture of driving styles.

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Section: Discussionmentioning

confidence: 99%

Modeling the Impact of Driving Styles on Crash Severity Level Using SHRP 2 Naturalistic Driving Data

Chen

2022

Safety

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“…The validity of the use of Bayesian modelling is demonstrated by the use of this approach in thematic research related to traffic safety. Bayesian modelling within a scientific approach to traffic safety has been used, for example, to assess conflict extremes in real time or to model traffic accidents on an urban road network [36,37].…”

Section: The Principle Of the Mathematical Modelmentioning

confidence: 99%

A Unified Approach for Risk Assessment of Road Crash Barriers Using Bayesian Statistics

et al. 2023

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The aim of this article is to improve road safety. Specifically, it deals with the development of a mathematical model that will more accurately define the severity of a defect in a road restraint system. Currently, that evaluation is based only on the subjective perception of individual safety auditors. The mathematical model was developed based on the principle of Bayesian statistics. The determination of the specific risk was made by comparing the results of the model for two datasets. In the first case, the model was based on accident data correlated with recorded defects in road restraint systems. In the second case, the dataset represented accident events with crash barriers where no defect was identified. Based on the comparison, a total of 64 risk combinations were identified. The mathematical model confirmed 26 combinations (41% of all selected combinations of the defect levels of the crash barriers). Although not even half of the identified combinations were confirmed, more than 90% of all correlated records are found in these exposures of confirmed combinations. The verification was able to clearly define the risk of safety defects and thus brings potential accuracy to subsequent decision-making related to the repair of road restraint systems.

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“…The accident rate is a reflection of the success of the take-over and can be a qualitative judgment of the quality of the take-over. In automatic driving take-over studies, the take-over reaction time, the steering reaction time, the minimum TTC, and the maximum synthetic acceleration are the most commonly used take-over performance evaluation indexes [39][40][41][42], and are widely used by scholars.…”

Section: Evaluation Metricsmentioning

confidence: 99%

The Effect of Multifactor Interaction on the Quality of Human–Machine Co-Driving Vehicle Take-Over

et al. 2023

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This paper investigates the effects of non-driving related tasks, take-over request time, and take-over mode interactions on take-over performance in human–machine cooperative driving in a highway environment. Based on the driving simulation platform, a human–machine collaborative driving simulation experiment was designed with various take-over quality influencing factors. The non-driving related tasks included no task, listening to the radio, watching videos, playing games, and listening to the radio and playing games; the take-over request time was set to 6, 5, 4, and 3 s, and the take-over methods include passive and active take-over. Take-over test data were collected from 65 drivers. The results showed that different take-over request times had significant effects on driver take-over performance and vehicle take-over steady state (p < 0.05). Driver reaction time and minimum TTC decreased with decreasing take-over request time, maximum synthetic acceleration increased with decreasing take-over request time, accident rate increased significantly at 3 s take-over request time, and take-over safety was basically ensured at 4 s request time. Different non-driving related tasks have a significant effect on driver take-over performance (p < 0.05). Compared with no task, non-driving related tasks significantly increase driver reaction time, but they only have a small effect on vehicle take-over steady state. Vehicle take-over mode has a significant effect on human–machine cooperative driving take-over quality; compared with passive take-over mode, the take-over quality under active take-over mode is significantly lower.

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A multivariate method for evaluating safety from conflict extremes in real time

Cited by 22 publications

References 67 publications

Modeling the Impact of Driving Styles on Crash Severity Level Using SHRP 2 Naturalistic Driving Data

Modeling the Impact of Driving Styles on Crash Severity Level Using SHRP 2 Naturalistic Driving Data

A Unified Approach for Risk Assessment of Road Crash Barriers Using Bayesian Statistics

The Effect of Multifactor Interaction on the Quality of Human–Machine Co-Driving Vehicle Take-Over

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