Abstract
In automated vehicles, the collaboration of human drivers and automated systems plays a decisive role in road safety, driver comfort, and acceptance of automated vehicles. A successful interaction requires a precise interpretation and investigation of all influencing factors such as driver state, system state, and surroundings (e.g., traffic, weather). This contribution discusses the detailed structure of the driver-vehicle interaction, which takes into account the driving situation and the driver state to improve driver performance. The interaction rules are derived from a controller that is fed by the driver state within a loop. The regulation of the driver state continues until the target state is reached or the criticality of the situation is resolved. In addition, a driver model is proposed that represents the driver’s decision-making process during the interaction between driver and vehicle and during the transition of driving tasks. The model includes the sensory perception process, decision-making, and motor response. The decision-making process during the interaction deals with the cognitive and emotional states of the driver. Based on the proposed driver-vehicle interaction loop and the driver model, an experiment with 38 participants is performed in a driving simulator to investigate (1) if both emotional and cognitive states become active during the decision-making process and (2) what the temporal sequence of the processes is. Finally, the evidence gathered from the experiment is analyzed. The results are consistent with the suggested driver model in terms of the cognitive and emotional state of the driver during the mode change from automated system to the human driver.
Highlights
Zusammenfassung In automatisierten Fahrzeugen spielt die Zusammenarbeit vom menschlichen Fahrer und automatisierten Systemen eine entscheidende Rolle für die Verkehrssicherheit, den Fahrerkomfort und die Akzeptanz von automatisierten Fahrzeugen
This structure takes into account the driver state and the situation criticality, and adapts the takeover request (TOR) in real-time according to these factors by receiving online feedback from them
The decision can result in an action, such as overtaking driving task, or it can only lead to a change in the driver state
Summary
Parasuraman [46] presents a model for the levels of humanmachine interaction that employs a human-centered perspective. The proposed model defines automation in four distinct classes based on the simple model of human information processing that includes four stages of sensory processing, perception, decision making, and response selection. It follows that automation can be applied to information acquisition, in which data are collected from the environment; information analysis, which involves extracting features of the input data; decision and action selection, where actions are recommended to the driver; and action implementation, with the automated system responding directly to the driving situation. The use of a human-centered model for the interaction levels facilitates the design and diagnosis of the driver-vehicle interaction concept
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