Modeling drivers’ reaction when being tailgated: A Random Forests Method

Xu, Yueru; Bao, Shan; Pradhan, Anuj K.

doi:10.1016/j.jsr.2021.05.004

Cited by 18 publications

(8 citation statements)

References 30 publications

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“…Although the fact that the leading, not the following, driver mostly acts to maintain this gap is not uncommon for human drivers and has been observed under some conditions [ 37 ], it is not the most common behaviour for reducing the risk during car following [ 38 ]. We identified two causes for this model behaviour.…”

Section: Resultsmentioning

confidence: 99%

Modelling communication-enabled traffic interactions

2023

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A major challenge for autonomous vehicles is handling interactions with human-driven vehicles—for example, in highway merging. A better understanding and computational modelling of human interactive behaviour could help address this challenge. However, existing modelling approaches predominantly neglect communication between drivers and assume that one modelled driver in the interaction responds to the other, but does not actively influence their behaviour. Here, we argue that addressing these two limitations is crucial for the accurate modelling of interactions. We propose a new computational framework addressing these limitations. Similar to game-theoretic approaches, we model a joint interactive system rather than an isolated driver who only responds to their environment. Contrary to game theory, our framework explicitly incorporates communication between two drivers and bounded rationality in each driver’s behaviours. We demonstrate our model’s potential in a simplified merging scenario of two vehicles, illustrating that it generates plausible interactive behaviour (e.g. aggressive and conservative merging). Furthermore, human-like gap-keeping behaviour emerged in a car-following scenario directly from risk perception without the explicit implementation of time or distance gaps in the model’s decision-making. These results suggest that our framework is a promising approach to interaction modelling that can support the development of interaction-aware autonomous vehicles.

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Section: Resultsmentioning

confidence: 99%

Modelling communication-enabled traffic interactions

2023

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“…Results of this study also showed that young drivers under the age of 30 had higher chances of causing more severe injuries for bicyclists than other drivers. This could be attributed to young drivers being more likely to be involved in aggressive and risky driving behaviors which increase the odds of severe safety consequences ( 38 , 39 ). These results signify the high importance of regulating young drivers’ driving behavior.…”

Section: Discussionmentioning

confidence: 99%

Identifying Factors Related to Crash Injury Levels Involving Bicyclists at Different Locations Through Crash Data Analysis

Ammar

Misra

Feng

et al. 2023

Transportation Research Record: Journal of the Transportation R

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The safety of vulnerable road users, including bicyclists, has become an increasing societal concern. Factors characterizing bicyclists’ crashes with motor vehicles may affect bicyclists’ injuries differently depending on the location of these crashes. The purpose of this paper is to provide a comprehensive analysis for identifying significant factors that affect bicyclists’ injury levels from crashes occurring at travel lanes and at non-travel lanes (e.g., crosswalks and bicycle lanes). For this purpose, this study applied Multinomial Logistic Regression on the Crash Report Sampling System data for three consecutive years. Bicyclists’ injuries were categorized into three levels: (1) Possible, (2) Moderate, and (3) Severe. The study found that running a separate model for each location provided better performances than running an aggregated model for both locations. Results showed common factors significantly associated with an increased likelihood of moderate and/or severe injuries at both locations. Five unique factors were associated with higher likelihoods of these moderate and/or severe injuries to bicyclists in the Travel Lane model, whereas two unique factors were found related to increased odds of these injuries to bicyclists in the Non-Travel Lane model. The results of this study contribute to a better understanding of bicyclists’ crash scenarios and the development of potential countermeasures by alternating some circumstances characterizing these crashes, when possible, to reduce potential injuries.

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“…In this analysis, the driving behaviors of adult and teen drivers were examined using three types of highway driving measures: tailgating ratio; speeding ratio; and lane-changing frequency. To define a tailgating event, a minimum time headway of one second or less between two vehicles was measured, and this duration must last for at least one second ( 25 ) as shown in Figure 1 a . Speeding was determined if the vehicle speed was greater than 75 miles per hour (mph), or 33.53 meters per second (m/s), which corresponds to five mph above the maximum post-highway speed limit in Michigan.…”

Section: Methodsmentioning

confidence: 99%

Driving Style Classification and the Effectiveness of Advanced Driving Assistance Systems: Differences between Teen and Adult Drivers

Ammar

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Advanced driving assistance systems (ADAS) are designed to reduce potential crash risks and enhance driving safety. However, drivers’ interactions with ADAS may vary depending on their individual driving styles and characteristics. This study proposes a novel approach to classifying driving styles and explores how age and gender affect interactions with ADAS. The study utilized two naturalistic driving data sets comprising 148 drivers from four age groups: teens; younger adults; middle-aged adults; and older adults. Data were collected during two periods: baseline (without ADAS); and treatment (with ADAS). First, the K-means clustering algorithm was employed to divide trips into one conservative and two aggressive groups based on three driving behavior metrics: tailgating; speeding; and lane-changing. The aggressive-trip ratios were then calculated for each driver during each of the two periods. The Bayesian Gaussian mixture model was applied to determine the threshold values of the aggressive-trip ratios to classify drivers as conservative, moderate, or aggressive during each period. This allowed for identifying changes in driving style upon the activation of ADAS. The subsequent multinomial logistic regression model results showed that driving styles vary across age groups, with teens being the most aggressive drivers. Certain changes in driving style were observed, with some conservative drivers becoming aggressive or moderate and some aggressive drivers becoming conservative or moderate, but these differences were statistically non-significant. The findings of this study indicate that warning-based ADAS may not elicit significant changes in driving style, particularly among teenage drivers who are consistently the most aggressive drivers.

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Modeling drivers’ reaction when being tailgated: A Random Forests Method

Cited by 18 publications

References 30 publications

Modelling communication-enabled traffic interactions

Modelling communication-enabled traffic interactions

Identifying Factors Related to Crash Injury Levels Involving Bicyclists at Different Locations Through Crash Data Analysis

Driving Style Classification and the Effectiveness of Advanced Driving Assistance Systems: Differences between Teen and Adult Drivers

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