Driver Cognitive Demand Detection Using Biosignal Neural Networks
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Solution Overview
Problem
Existing systems lack the ability to effectively detect and respond to the cognitive demand of a vehicle driver, particularly in situations where the driver is distracted or experiencing stress, which can adversely affect driving performance and safety.
Innovation Solution
A computer-implemented method and system that utilize biosignals, such as heart interbeat interval and eye metrics, to determine a user's cognitive demand level. This involves recording and processing biosignals using an artificial neural network trained on datasets that include both biosignal data and cognitive demand information, allowing for real-time determination of cognitive demand levels and subsequent adaptive responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If advanced driver assistance systems are configured for constant and low cognitive demand, then system simplicity is maintained, but the system cannot effectively detect or respond to varying driver cognitive states
Solution Approach 1:
The system uses the driver's own biosignals (eye metrics, heart rate variability) to automatically determine cognitive demand levels without requiring external assessment or complex intervention protocols. The neural network processes these self-generated signals to adapt system behavior autonomously
Solution Approach 2:
The system changes operational parameters based on detected cognitive demand levels. When high cognitive demand is detected, the system modifies vehicle settings (e.g., climate control, infotainment) and activates safety features to reduce the driver's cognitive burden, transitioning from a static configuration to a dynamically adaptive one
2Measurement precision
If biosignals are recorded and processed using an artificial neural network, then cognitive demand detection accuracy is improved, but computational requirements and processing complexity increase
Solution Approach 1:
The system extracts only the most relevant features from biosignal data (eye metrics such as blink rate and duration, heart rate variability indicators) and feeds these extracted features to the neural network, rather than processing raw biosignal streams. This reduces computational complexity while maintaining detection accuracy
Solution Approach 2:
Biosignals are pre-processed and features are extracted before being supplied to the neural network. This preliminary processing step prepares the data in an optimized format that reduces the computational burden on the neural network during real-time cognitive demand assessment
3Reliability
If the system activates safety features and adjusts vehicle settings in response to high cognitive demand, then driving safety is enhanced, but system response time and intervention complexity increase
Solution Approach 1:
The system pre-configures safety features and vehicle settings that can be rapidly activated based on cognitive demand levels. By preparing these interventions in advance and using pre-processed biosignal features, the system can transition from detection to action with minimal delay, enhancing safety without excessive response time
Solution Approach 2:
The system dynamically adjusts vehicle operations based on real-time cognitive demand assessment. When high cognitive demand is detected, the system actively modifies climate control settings, infotainment system state, and safety feature activation to reduce driver burden, creating a dynamic response rather than a static pre-programmed sequence
Data Source
AI summary
System and computer-implemented method for determining a cognitive demand level of a user, the method comprising: recording, as a first training data subset, one or more first biosignals of a user, wherein the user is occupied with at least one first task, training an artificial neural network on a training dataset to determine a cognitive demand level indicative of cognitive demand the user is experiencing; recording one or more second biosignals of a user of a vehicle as an input dataset; and processing the input dataset by the trained artificial neural network to determine the cognitive demand level indicative of cognitive demand the user is experiencing.