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

Abstract

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.

A Review of Driving Simulation Technology and Applications

“…Its 4 m diameter dome houses a complete 2005 Jaguar S-type. The FoV is 250 degrees using 9 projectors and the virtual environment is rendered at a frequency of 60Hz [82]. Its 8-DOF motion is achieved by the integration of a hexapod and an XY structure 5 m long [83].…”

A Car-Following Data Collecting Method Based on Binocular Stereo Vision

“…The relative velocity (RV) at time t i is described in (10): (10) in which RV CS i represents the relative velocity derived from smoothed camera data, RV GNSS i represents the relative velocity derived from GNSS RTK data. The absolute error of relative velocity (AE RV ) at time t i is calculated in (11).…”

“…Car-following models, describing the longitudinal motion of adjacent vehicles in the same lane, are widely applied in microscopic traffic flow simulation. To simulate car-following behaviors in the real world as reasonably as possible [10], car-following models were proposed, calibrated and validated based on experimental and empirical vehicle trajectory datasets [11]- [14], such as the open accessed datasets NGSIM (the Next Generation SIMulation) program [15]…”

A Comparison of Unimodal and Multimodal Measurements of Driver Stress in Real-World Driving Conditions

“…[46]. This approach has been employed in both real [47][43] [48] and simulated [44] [49] driving studies. However, the quality of cardiac data can be affected by movement artefacts [50] while skin conductance signals can be sensitive to temperature, humidity or participant age [51].…”