Stochastic hybrid models for predicting the behavior of drivers facing the yellow-light-dilemma

Hoehener, Daniel; Green, Paul A.; Vecchio, Domitilla Del

doi:10.1109/acc.2015.7171849

Cited by 8 publications

(5 citation statements)

References 30 publications

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“…The experiments reported here, two of three being conducted, were part of a project to model driver decision making at intersections and examine the value of augmented Proceedings of the Human Factors and Ergonomics Society 59th Annual Meeting -2015 reality warnings (Hoehener et al, 2015). The first experiment (baseline driving) is described in Lin et al, (2015) and a report on the second experiment is being produced in which honking and augmented reality warnings were added.…”

Section: Methodsmentioning

confidence: 99%

Honking Helps Overcome the Driving Too Slowly Problem in Driving Simulators

Jeong

Green

2015

Proceedings of the Human Factors and Ergonomics Society Annual

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In driving simulators, subjects sometimes drive more slowly than they would on real roads when not attending to driving. To address this problem, in two experiments, groups of 24 subjects drove through 138 intersections with green (53 %), yellow (36 %), or red lights (11 %). The speed limit was 35 mi/hr. In the second experiment, the following vehicle honked as needed.The number of honks per subject varied from 0 to 6 for driving too slowly (less than 25 mi/hr) and 0 to 8 for delayed responses when a traffic light changed from red to green. Older drivers received 2.4 times more honks than younger drivers. Within 5 s after the following vehicle honked between intersections, the mean speed driven increased by 5 mi/hr. Similarly, with honking, the speed 5 s after the light changed increased by 8 mi/hr, both changes that lead to more realistic driving.

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Section: Methodsmentioning

confidence: 99%

Honking Helps Overcome the Driving Too Slowly Problem in Driving Simulators

Jeong

Green

2015

Proceedings of the Human Factors and Ergonomics Society Annual

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“…Juan L et al [5] used the enhanced Gaussian Mixture Model and the Kalman Filter algorithm to determine the trajectory of motion of a target vehicle during a yellow light and predicted driving behavior using the Logit Model. Hoehner D A et al [6] constructed a stochastic Hybrid System Model for driver behavior in a yellow light dilemma area and calculated the probability of running a yellow light by combining a Gaussian process estimation and a Monte Carlo simulation. Bar-Gera H et al [7] quantified driver behavior during a yellow light signal using an exponential logistic regression and a four-parameter logistic regression.…”

Section: Introductionmentioning

confidence: 99%

Atlas Analysis of the Impact of the Interval Changes in Yellow Light Signals on Driving Behavior

Zheng

Qin

2021

IEEE Access

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The interval changes in yellow light signals significantly impact driving behaviors that affect traffic safety. This study aims to compare the applicability of different interval changes in yellow light signals from the perspectives of drivers. Data acquisition was conducted in a naturalistic driving experiment with equipment including V-BOX, Ergo LAB, and I-View HED4 to acquire vehicle running index data, driver physiological and psychological index data, and eye movement index data, respectively. Twenty experienced drivers were recruited as subjects and were categorized as safe, average, or dangerous drivers. Based on the data obtained, driving behaviors for different interval changes in yellow light signals were studied using an Atlas. Observations of the driving behavior Atlas reveal that there are notable differences in driving behaviors for different interval changes in yellow light signals. Also, it was found that dangerous drivers are more likely to be affected by the interval changes in yellow light signals. It is necessary to choose reasonable signal transition modes to improve the safety of intersections based on actual road conditions and types of drivers. The results can guide traffic management departments when choosing a reasonable transition method for yellow light signals and help formulate customized training programs for different types of drivers. In particular, the results help in providing training on driving speed and deceleration control for dangerous drivers, which will help improve traffic safety.

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“…For example, Elhenawy et al (2015) have used AI techniques including adaptive boosting (Ada-boost), Artificial Neural Networks (ANNs), and SVMs to model drivers' stop/go behavior at the yellow indication. In another study, Hoehener et al (2015) investigated drivers' dilemma when the traffic light changes to yellow. Using an ANN model based on Gaussian process theory, they introduced an upper bound for the crossing probability in the proposed situation.…”

Section: Introductionmentioning

confidence: 99%

Toward Human-Centered Design of Automated Vehicles: A Naturalistic Brake Policy

Rahmati

Abianeh

Tabesh

et al. 2021

Front. Future Transp.

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While safety is the ultimate goal in designing Connected and Automated Vehicles (CAVs), current automotive safety standards fail to explicitly define rules and regulations that ensure the safety of CAVs or those interacting with such vehicles. This study investigates CAV safety in mixed traffic environments with both human-driven and automated vehicles, focusing particularly on rear-end collisions at intersections. The central hypothesis is that the primary reason behind these crashes is the potential mismatch between CAVs’ braking decisions and human drivers’ expectations. To test this hypothesis, various Artificial Intelligence (AI) techniques, along with specialized statistical methods are adopted to learn and model the braking behavior of human drivers at intersections and compare the results to that of CAVs. Findings suggest systematical differences in CAVs’ and humans’ braking trajectories, revealing a mismatch between their braking patterns. Accordingly, a Markovian decision modeling framework is adopted to design a novel CAV braking profile that ensures 1) compatibility with human expectation, and 2) safe and comfortable maneuvers by CAVs in mixed driving environments. The findings of this study are expected to facilitate the development of higher levels of vehicle automation by providing guidelines to prevent rear-end collisions caused by existing differences in CAVs’ and humans’ braking strategies.

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Stochastic hybrid models for predicting the behavior of drivers facing the yellow-light-dilemma

Cited by 8 publications

References 30 publications

Honking Helps Overcome the Driving Too Slowly Problem in Driving Simulators

Honking Helps Overcome the Driving Too Slowly Problem in Driving Simulators

Atlas Analysis of the Impact of the Interval Changes in Yellow Light Signals on Driving Behavior

Toward Human-Centered Design of Automated Vehicles: A Naturalistic Brake Policy

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