Driver experience and cognitive workload in different traffic environments
ABSTRACT How do levels of cognitive workload differ between experienced and inexperienced drivers? In this study we explored cognitive workload and driver experience, using a secondary task method, the peripheral detection task (PDT) in a field study. The main results showed a large and statistically significant difference in cognitive workload levels between experienced and inexperienced drivers. Inexperienced, low mileage drivers had on average approximately 250 milliseconds (ms) longer reaction times to a peripheral stimulus, than the experienced drivers. It would, therefore, appear that drivers with better training and experience were able to automate the driving task more effectively than their less experienced counterparts in accordance with theoretical psychological models. It has been suggested that increased training and experience may provide attention resource savings that can benefit the driver in handling new or unexpected traffic situations.
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ABSTRACT: Great advances in simulation-based vehicle system design and development of various driver assistance systems have enhanced the research on improved modeling of driver steering skills. However, little effort has been made on developing driver steering skill models while capturing the uncertainties or statistical properties of the vehicle-road system. In this paper, a stochastic model predictive control (SMPC) approach is proposed to model the driver steering skill, which effectively incorporates the random variations in the road friction and roughness, a multipoint preview approach, and a piecewise affine (PWA) model structure that are developed to mimic the driver's perception of the desired path and the nonlinear internal vehicle dynamics. The SMPC method is then used to generate a steering command by minimization of a cost function, including the lateral path error and ease of driver control. In the analyses, first, the experimental data of Hongqi HQ430 are used to validate the driver steering skill controller. Then, the parametric studies of control performance during a nonlinear steering maneuver are provided. Finally, further discussions about the driver's adaption and the indication on vehicle dynamics tuning are given. The proposed switching-based SMPC driver steering control framework offers a new approach for driver behavior modeling.IEEE Transactions on Intelligent Transportation Systems 02/2015; 16(1):365-375. DOI:10.1109/TITS.2014.2334623 · 2.47 Impact Factor
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ABSTRACT: In complex situations, elderly rides 1.7 km/h faster on an e-bike than on a normal bike.•In simple situations, elderly rides 3.6 km/h faster on an e-bike than on a normal bike.•Workload is higher in complex traffic situations than in simple traffic situations.•Elderly on e-bikes rides as fast as younger cyclists on normal bikes.•Mental workload is not higher on an e-bike than on a conventional bicycle.Accident Analysis & Prevention 01/2015; 74. DOI:10.1016/j.aap.2014.10.018 · 1.87 Impact Factor
Article: Mental workload and driving[Show abstract] [Hide abstract]
ABSTRACT: The aim of this review is to identify the most representative measures of subjective and objective mental workload in driving, and to understand how the subjective and objective levels of mental workload influence the performance as a function of situation complexity and driving experience, i.e., to verify whether the increase of situation complexity and the lack of experience increase the subjective and physiological levels of mental workload and lead to driving performance impairments. This review will be useful to both researchers designing an experimental study of mental workload and to designers of drivers' training content. In the first part, we will broach the theoretical approach with two factors of mental workload and performance, i.e., situation complexity and driving experience. Indeed, a low complex situation (e.g., highways), or conversely a high complex situation (e.g., town) can provoke an overload. Additionally, performing the driving tasks implies producing a high effort for novice drivers who have not totally automated the driving activity. In the second part, we will focus on subjective measures of mental workload. A comparison of questionnaires usually used in driving will allow identifying the most appropriate ones as a function of different criteria. Moreover, we will review the empirical studies to verify if the subjective level of mental workload is high in simple and very complex situations, especially for novice drivers compared to the experienced ones. In the third part, we will focus on physiological measures. A comparison of physiological indicators will be realized in order to identify the most correlated to mental workload. An empirical review will also take the effect of situation complexity and experience on these physiological indicators into consideration. Finally, a more nuanced comparison between subjective and physiological measures will be established from the impact on situation complexity and experience.Frontiers in Psychology 12/2014; 5:1344. DOI:10.3389/fpsyg.2014.01344 · 2.80 Impact Factor