Driver mental workload is an important factor in the operational safety of automated driving. In this study, workload was evaluated subjectively (NASA R-TLX) and objectively (auditory detection-response task) on Dutch public highways ($150 km) comparing manual and supervised automated driving in a Tesla Model S with moderators automation experience and traffic complexity. Participants (N = 16) were either automation-inexperienced drivers or automation-experienced Tesla owners. Complexity ranged from an engaging environment with a road geometry stimulating continuous traffic interaction, and a monotonic environment with lower traffic density and a simple road geometry. Perceived and objective workload increased with traffic complexity. When using the automation, automationexperienced drivers perceived a lower workload, while automation-inexperienced drivers perceived their workload to be similar to manual driving. However, the detection-response task indicated an increase in cognitive load with automation, in particular in complex traffic. This indicates that drivers underestimate the actual task load of attentive monitoring. The findings also highlight the relevance of using system-experienced participants and the importance of incorporating both objective and subjective measures when examining workload.
Current lane-based microscopic traffic simulators combine car-following and lane changing logic to describe the (often discrete) lateral vehicle motion on multi-lane road segments. However, the simulated lateral trajectories are physically unplausible and inside-lane behavior such as lane-keeping and curve negotiation cannot be modelled. In this work, we integrate lateral vehicle dynamics and yaw motion into a traffic simulation framework, aiming to describe lateral motion and vehicle interactions with more precision. The resulting framework consists of two coupled layers, an upper tactical level that plans maneuvers such as lane-changing; and a lower operational layer with a control module (steering and acceleration control) that operates in a closed loop with the bicycle model of vehicle dynamics. The feedback mechanism between the layers allows for dynamic trajectory re-planning. Unlike the microscopic traffic models, the proposed framework accounts for lateral vehicle dynamics and yaw motion; provides additional variables such as vehicle heading and front wheel steering angle; and is hence termed as submicroscopic. Case study results demonstrate the power of the framework to include lateral maneuvers such as curve negotiation, corrective steering, lane change abortion and fragmented lane changing. The framework was operationalized to model multi-lane traffic flow consisting of human-driven vehicles. At the macroscopic level, the traffic flow simulation can reproduce phenomena such as capacity drop. Thus the framework preserves the properties of the component models and at the same time describe the continuous 2-D planar movement of vehicles.
Safety measurement and its analysis have been challenging and well-researched topics in transportation. Conventionally, surrogate safety measures have been used as safety indicators in simulation models for safety assessment, in control formulations for driver assistance systems, and in data analysis of naturalistic driving studies. However, surrogate indicators give partial insights on traffic safety; that is, these indicators only indicate a predetermined set of possible precrash situations for an interacting vehicle pair. Recently, a safety indicator called the “driving safety field,” based on field theory, was proposed for two-dimensional vehicle interactions. However, the objectivity of its functional form and its validity have yet to be tested. A qualitative and quantitative comparison of different safety indicators was provided as a risk measure to demarcate their mathematical properties and evaluate their usefulness in quantifying trajectory risk. Five relevant safety indicators were compared: inverse time to collision, postencroachment time, potential indicator of collision with urgent decceleration, warning index, and safety field force. Their formulations were mathematically analyzed to yield qualitative insights and their values over simulated vehicle trajectories were evaluated to yield quantitative insights. The results acknowledge the limitations and demarcate the functional utilities of the selected safety indicators.
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