Driver experience and cognitive workload in different traffic environments. Accident Anal Prev

Stockholm University, Tukholma, Stockholm, Sweden
Accident Analysis & Prevention (Impact Factor: 1.87). 10/2006; 38(5):887-94. DOI: 10.1016/j.aap.2006.02.014
Source: PubMed


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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    • "Based on different prediction models, many SMPC methods have been proposed and effectively applied to various optimization control problems [17], [18]. Analysis and modeling driver skill can be conducted from various aspects, such as behavioral and eye-related measures [19], and the driver's cognitive workload [20]. In [15], the nonlinear tire force is applied to reflect the driver's different knowledge of nonlinear vehicle dynamics. "
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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 · 2.38 Impact Factor
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    • "Van Winsum et al., 1999) and also in real life conditions (e.g. Patten et al., 2006). The PDT is based on the finding that when mental workload increases, the functional visual field shrinks (Miura, 1986). "
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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
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    • "The general use of average cruise speeds [9] [10] does not accurately capture the exogenous influences and variation in driver behaviour in response to the road, geometry, or prevailing traffic environment [11]. These influences are exhibited via the individual and platoon cruise speed variations, influenced by queue discharge, midblock lane changing, route selection, driver anticipation of signal phasing, and other motivation or distraction factors [12] [13] [14] [15] [16]. "
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    ABSTRACT: This paper proposes a new queue prediction model based on the data that can be collected from a single loop detector positioned at the stop line of signalised intersections. A number of different model forms were explored using an enhanced NGSIM dataset. These data were filtered to represent the data that can be typically collected from a stop line detector loop. The best six models resulted in an accuracy ranging from 83% to 95% to correctly predict the state of vehicle’s discharge close to the stop line that is whether it is a queued or platooned vehicle. When combined with a logical filter to group sequential vehicles, it enables a traffic controller to estimate the most likely queue length. The proposed model will form part of a new offset optimizer algorithm currently under development.
    The Open Transportation Journal 12/2014; 8(1):73-82. DOI:10.2174/1874447801408010073
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