Driver State Detection Using Simulated Cognitive Load Protocols
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Solution Overview
Problem
Existing methods for collecting data on human psychophysiological states during driving are prone to biases and inconsistencies, making it difficult to derive reliable results for developing advanced driver assistance systems (ADAS).
Innovation Solution
A structured protocol involving multiple driving stages in a car simulator, including monotonous and complex scenarios, with simultaneous cognitive tests and data collection using physiological sensors, such as heart rate, EEG, and eye movement data, along with self-reported data, to gather comprehensive and reliable data on driver psychophysiological states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is collected during real-life driving experiments, then the data reflects actual driving conditions, but biases are introduced affecting reliability
Solution Approach 1:
A car simulator is introduced as an intermediary between the driver and real road conditions. The simulator recreates various driving scenarios (monotonous highway driving, complex urban road driving) in a controlled laboratory environment, eliminating biases from real-life experiments while maintaining ecological validity through realistic simulation scenarios.
2Measurement precision
If multiple physiological sensors are used to collect comprehensive data, then measurement precision improves, but device complexity increases
Solution Approach 1:
The car simulator serves as a multi-functional platform that simultaneously performs driving scenario presentation, cognitive test administration, and physiological data collection. Multiple sensors (heart rate, EEG, eye movement, respiration) are integrated into a unified data collection system, reducing overall system complexity through functional consolidation.
3Loss of information
If cognitive tests are performed simultaneously with driving, then cognitive load measurement is achieved, but driving task performance may be affected
Solution Approach 1:
The N-back cognitive test requires partial attention and working memory engagement rather than full cognitive capacity. This partial cognitive load is sufficient to measure cognitive resource allocation while maintaining safe driving performance, avoiding excessive cognitive demands that would compromise driving safety.
4Adaptability or versatility
If multiple driving scenarios are tested, then adaptability of the system is improved, but loss of time increases
Solution Approach 1:
The experimental protocol uses periodic alternation between different driving scenarios (monotonous highway driving, complex urban road driving) with cognitive tests administered at specific intervals. This structured periodic approach efficiently covers multiple scenarios while managing total experiment duration through systematic scenario rotation.
Data Source
AI summary
It is herein disclosed a method of collecting data for determining a psychophysiological state of a human driver, comprising collecting data throughout a driving session in a car simulator, wherein the driving session comprises a plurality of driving stages, wherein the plurality of driving stages comprise, at least: a first stage of driving by a driver in a first scenario; a second stage of driving by the driver in a first scenario, wherein a first cognitive test is performed by the driver simultaneously with the driving; a third stage of driving by the driver in a second scenario; a fourth stage of driving by the driver in the second scenario, wherein a second cognitive test is performed by the driver simultaneously with the driving; wherein said collecting data comprises: collecting, by a plurality of detection means, physiological data on the driver throughout the driving session; wherein the physiological data collected comprises at least one of: heart rate data, electroencephalogram (EEG) data, eye movement data, and respiration data; and collecting driver behaviour data.