Human-Machine Interaction for Automated Vehicles: Driver Status Monitoring and the Takeover Process explains how to design an intelligent human-machine interface by characterizing driver behavior before and during the takeover process. Multiple solutions are presented to accommodate different sensing technologies, driving environments and driving styles. Depending on the availability and location of the camera, the recognition of driving and non-driving tasks can be based on eye gaze, head movement, hand gesture or a combination. Technical solutions to recognize drivers various behaviors in adaptive automated driving are described with associated implications to the driving quality.
Finally, cutting-edge insights to improve the human-machine-interface design for safety and driving efficiency are also provided, based on the use of this sensing capability to measure drivers’ cognition capability.
1. Chapter one Introduction - p. 1 2. Chapter two Driver behaviour recognition based on eye gaze - p. 7 3. Chapter three Driver behaviour recognition based on hand-gesture - p. 43 4. Chapter four Driver behaviour recognition based on head movement - p. 67 5. Chapter five Driver behaviour recognition based on the fusion of head movement and hand movement - p. 85 6. Chapter six Real-time driver behaviour recognition - p. 109 7. Chapter seven The implication of non-driving tasks on the take-over process - p. 131 8. Chapter eight Driver workload estimation - p. 147 9. Chapter nine Neuromuscular dynamics characterisation for human–machine interface - p. 167 10. Chapter ten Driver steering intention prediction using neuromuscular dynamics - p. 189 11. Chapter eleven Intelligent haptic interface design for human–machine interaction in automated vehicles - p. 217 12. Index - p. 241
Human-Machine Interaction for Automated Vehicles: Driver Status Monitoring and the Takeover Process explains how to design an intelligent human-machine interface by characterizing driver behavior before and during the takeover process. Multiple solutions are presented to accommodate different sensing technologies, driving environments and driving styles. Depending on the availability and location of the camera, the recognition of driving and non-driving tasks can be based on eye gaze, head movement, hand gesture or a combination. Technical solutions to recognize drivers various behaviors in adaptive automated driving are described with associated implications to the driving quality.
Finally, cutting-edge insights to improve the human-machine-interface design for safety and driving efficiency are also provided, based on the use of this sensing capability to measure drivers’ cognition capability.
1. Chapter one Introduction - p. 1 2. Chapter two Driver behaviour recognition based on eye gaze - p. 7 3. Chapter three Driver behaviour recognition based on hand-gesture - p. 43 4. Chapter four Driver behaviour recognition based on head movement - p. 67 5. Chapter five Driver behaviour recognition based on the fusion of head movement and hand movement - p. 85 6. Chapter six Real-time driver behaviour recognition - p. 109 7. Chapter seven The implication of non-driving tasks on the take-over process - p. 131 8. Chapter eight Driver workload estimation - p. 147 9. Chapter nine Neuromuscular dynamics characterisation for human–machine interface - p. 167 10. Chapter ten Driver steering intention prediction using neuromuscular dynamics - p. 189 11. Chapter eleven Intelligent haptic interface design for human–machine interaction in automated vehicles - p. 217 12. Index - p. 241
