Driver cognitive load

fects the driver’s cognitive load is critical for optimizing the user experience and safety during system design and through adaptive interfaces. This work aims to explore au-tomatic detection of driver cognitive workload through physiology and vehicle data. Although mental workload is not directly observable [10]. Objective: Understanding the cognitive load of drivers is crucial for road safety. Brain sensing has the potential to provide an objective measure of driver cognitive load. We aim to develop an advanced machine learning framework for classifying driver cognitive load using functional near-infrared spectroscopy (fNIRS). L. Harms for the measurement of drivers' cognitive load. The time taken by the driver to complete a calculation task was measured and related to positions on prespecified driving routes. The study is a continuation of a previous study described by Hams (1 ).It.

with driving performance measures of cognitive load. Two common pupillometric cognitive load measures are the index of cognitive activity (ICA) and the mean pupil diameter. ICA uses the frequency of minute dilations of the pupil [Marshall ]. This method is used almost exclusively with head-mounted eye trackers owing to their high precision. and react to, a driver's cognitive load. Sensors embedded throughout the car track a wide variety of driver activities, including driving conditions, driver reactions, control usage, driver reflexes, passenger interaction, and driver attention. The raw sensor readings are compiled and parsed, and used to assess the cognitive and stress load. to driver’s cognitive workload have been studied. A short list of them includes: lane position deviation, the number of lane departure, lane departure durations, speed deviation, lateral 1The ground truth of driver’s workload is usually assessed by subjective measures, secondary-task performance, or the analysis of the task.

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