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Objective This study investigated users’ subjective evaluation of three highly automated driving styles, in terms of comfort and naturalness, when negotiating a UK road in a high-fidelity, motion-based, driving simulator. Background Comfort and naturalness play an important role in contributing to users’ acceptance and trust of automated vehicles (AVs), although not much is understood about the types of driving style which are considered comfortable or natural. Method A driving simulator study, simulating roads with different road geometries and speed limits, was conducted. Twenty-four participants experienced three highly automated driving styles, two of which were recordings from human drivers, and the other was based on a machine learning (ML) algorithm, termed Defensive, Aggressive, and Turner, respectively. Participants evaluated comfort or naturalness of each driving style, for each road segment, and completed a Sensation Seeking questionnaire, which assessed their risk-taking propensity. Results Participants regarded both human-like driving styles as more comfortable and natural, compared with the less human-like, ML-based, driving controller. Particularly, between the two human-like controllers, the Defensive style was considered more comfortable, especially for the more challenging road environments. Differences in preference for controller by driver trait were also observed, with the Aggressive driving style evaluated as more natural by the high sensation seekers. Conclusion Participants were able to distinguish between human- and machine-like AV controllers. A range of psychological concepts must be considered for the subjective evaluation of controllers. Application Insights into how different driver groups evaluate automated vehicle controllers are important in designing more acceptable systems.

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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.