Driver Adaptive Task Allocation: A Field Driving Study

By S. Lei, Takashi Toriizuka, Mattias Roetting

Adaptive task allocation (ATA) provides a new solution for human-machine interaction in a highly automated system. Previous research demonstrates that psychophysiological signals yield sensitive information about human functional states, which can be used to build a closed loop for human-machine interaction to reallocate the tasks upon the status of human operator. The present study investigates the feasibility of adaptive task allocation in a field driving context. Driver’s mental workload was evaluated by electroencephalogram (EEG) in real-time and this result was used to dynamically adapt a secondary task allocated to driver. The results showed that ATA has a potential benefit to maintain a driver’s workload in a moderate level. However, generally, no significant increases in task performance were found between ATA and without ATA conditions.


  • Adaptive task allocation
  • human-machine interaction
  • mental workload
  • car driving
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