Heterogeneity of Driving Behaviors in Different Car-Following Conditions

Pariota, Luigi; Galante, Francesco; Bifulco, Gennaro Nicola

doi:10.3311/pptr.8609

Cited by 10 publications

(4 citation statements)

References 16 publications

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“…Ossen and Hoogendoorn, 2011;Kim et al, 2013) and within drivers (e.g. Pariota et al, 2016). However, it has taken the form of statistical distributions and random parameters rather than being linked to individual characteristics.…”

Section: Introductionmentioning

confidence: 99%

Combining driving simulator and physiological sensor data in a latent variable model to incorporate the effect of stress in car-following behaviour

Paschalidis

Choudhury

Hess

2019

Analytic Methods in Accident Research

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Car-following models, which are used to predict the acceleration-deceleration decisions of drivers in the presence of a closely spaced lead vehicle, are critical components of traffic microsimulation tools and useful for safety evaluation. Existing car-following models primarily account for the effects of surrounding traffic conditions on a driver's decision to accelerate or decelerate. However, research in human factors and safety has demonstrated that driving decisions are also significantly affected by individuals' characteristics and their emotional states like stress, fatigue, etc. This motivates us to develop a car-following model where we explicitly account for the stress level of the driver and quantify its impact on acceleration-deceleration decisions. An extension of the GM stimulus-response model framework is proposed in this regard, where stress is treated as a latent (unobserved) variable, while the specification also accounts for the effects of drivers' sociodemographic characteristics. The proposed hybrid models are calibrated using data collected with the University of Leeds Driving Simulator where participants are deliberately subjected to stress in the form of aggressive surrounding vehicles, slow leaders and/or time pressure while driving in a motorway setting. Alongside commonly used variables, physiological measures of stress (i.e. heart rate, blood volume pulse, skin conductance) are collected with a non-intrusive wristband. These measurements are used as indicators of the latent stress level in a hybrid model framework and the model parameters are estimated using Maximum Likelihood Technique. Estimation results indicate that car-following behaviour is significantly influenced by stress alongside speed, headway and drivers' characteristics. The findings can be used to improve the fidelity of simulation tools and designing interventions to improve safety.

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Section: Introductionmentioning

confidence: 99%

Combining driving simulator and physiological sensor data in a latent variable model to incorporate the effect of stress in car-following behaviour

Paschalidis

Choudhury

Hess

2019

Analytic Methods in Accident Research

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“…Thus, an appropriate prediction algorithm for vehicle states is required to generate the deceleration trajectory as the set point of this autonomous braking control on diverse deceleration conditions. Also, this algorithm should consider individual driver characteristics to apply this automatic braking system more practically [4,9,10,11,12,13,14].…”

Section: Introductionmentioning

confidence: 99%

“…The first method is a parametric model-based prediction. The intelligent driver model is a representative parametric model for vehicle-state prediction according to driver characteristics [14,15,16,17,18,19]. This model consists of a mathematical equation based on the physical behavior for car-following situations with some explicit parameters.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Deceleration Prediction Model to Reflect Individual Driver Characteristics by Online Parameter Learning for Autonomous Regenerative Braking of Electric Vehicles

Min

Sim

Ahn

et al. 2019

Sensors

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The connected powertrain control, which uses intelligent transportation system information, has been widely researched to improve driver convenience and energy efficiency. The vehicle state prediction on decelerating driving conditions can be applied to automatic regenerative braking in electric vehicles. However, drivers can feel a sense of heterogeneity when regenerative control is performed based on prediction results from a general prediction model. As a result, a deceleration prediction model which represents individual driving characteristics is required to ensure a more comfortable experience with an automatic regenerative braking control. Thus, in this paper, we proposed a deceleration prediction model based on the parametric mathematical equation and explicit model parameters. The model is designed specifically for deceleration prediction by using the parametric equation that describes deceleration characteristics. Furthermore, the explicit model parameters are updated according to individual driver characteristics using the driver’s braking data during real driving situations. The proposed algorithm was integrated and validated on a real-time embedded system, and then, it was applied to the model-based regenerative control algorithm as a case study.

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“…Also, many of the recent results are based on relatively short trajectories recorded in normal traffic, which makes it difficult to estimate how much of the parameter variance and correlation is due to driver heterogeneity, and how much due to vehicle and driving situation heterogeneity. Pariota et al [ 25 ] measured a large and representative sample of drivers using an instrumented car, thus controlling for the vehicle-specific variation. They found substantial variation between and within different drivers’ car following behavior, operationalized as estimated equilibrium time headway and spacing.…”

Section: Introductionmentioning

confidence: 99%

Trade-off between jerk and time headway as an indicator of driving style

et al. 2017

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Variation in longitudinal control in driving has been discussed in both traffic psychology and transportation engineering. Traffic psychologists have concerned themselves with “driving style”, a habitual form of behavior marked by it’s stability, and its basis in psychological traits. Those working in traffic microsimulation have searched for quantitative ways to represent different driver-car systems in car following models. There has been unfortunately little overlap or theoretical consistency between these literatures. Here, we investigated relationships between directly observable measures (time headway, acceleration and jerk) in a simulated driving task where the driving context, vehicle and environment were controlled. We found individual differences in the way a trade-off was made between close but jerky vs. far and smooth following behavior. We call these “intensive” and “calm” driving, and suggest this trade-off can serve as an indicator of a possible latent factor underlying driving style. We posit that pursuing such latent factors for driving style may have implications for modelling driver heterogeneity across various domains in traffic simulation.

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Heterogeneity of Driving Behaviors in Different Car-Following Conditions

Abstract: Many application fields in transportation engineering

Cited by 10 publications

References 16 publications

Combining driving simulator and physiological sensor data in a latent variable model to incorporate the effect of stress in car-following behaviour

Combining driving simulator and physiological sensor data in a latent variable model to incorporate the effect of stress in car-following behaviour

Vehicle Deceleration Prediction Model to Reflect Individual Driver Characteristics by Online Parameter Learning for Autonomous Regenerative Braking of Electric Vehicles

Trade-off between jerk and time headway as an indicator of driving style

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