The rapid development of computer information technology has increased the complexity of the human–computer interface, especially in supervision tasks. It is difficult for users to pay attention to multiple information and make decisions in real-time, which needs higher requirements for the design of a human–computer interface. The research designs a novel adaptive human–computer interface using the Adaptive Interface Design (AID) framework: Combining the real-time user attention and system state as the input layer of AID; Building a hybrid entropy attention allocation model as the decision layer of AID; Adjusting the real-time saliency of the information in the interface as the output layer of AID. The comparative experiments in supervision tasks are conducted to test the performance of the users in the designed adaptive interface and traditional interface. The results show that the designed adaptive human–computer interface can effectively improve the supervisors’ performance and reduce the risk of system failure.

With the rapid increase of high-speed train driving automation, the human-computer interaction of high-speed train driving is changing. It is a major dilemma to maintain a well-interactive process between the driver and the driving system for the intelligent train operation. Nevertheless, the enhancement of the driver’s situation awareness (SA) is an effective way to promote the process of driver-driving system interaction. To achieve this purpose, we proposed an enhanced SA model based on the perceptual cycle model (PCM). The model helps to identify the high-speed train DMI interface elements that affect the different SA level of drivers and provides theoretical support for describing the SA change pattern of driving tasks. We proposed a new display mode (ESA mode) for enhancing driver SA according to this model, and conducted driving simulator experiments to explore the differences of SA change patterns between the ESA mode and the normal mode (NSA mode) for 16 subjects. The experimental results showed that the ESA mode successfully overcame the subjects’ SA recession in the NSA mode. The interaction with enhanced SA can help the driver remain vigilant and further improve the general driving performance during monotonous process of automated high-speed train driving.

Complexity of emergency procedures have direct impacts on the accomplishment of tasks in metro system. In order to ensure the efficient operation of automated metro in emergency scenarios, it is necessary to understand the potential relationship between system change and emergency procedures. To solve this problem, an evaluation method for emergency procedures is proposed. Firstly, on the basis of investigation, interview and empirical analysis of different types of automated metro lines, combined with Team-Cognitive Work Analysis(CWA) method and network theory, the team emergency task network is established. Then four characteristic indexes, i.e. degree, average shortest path length, agglomeration coefficient and overall network efficiency, are selected as the analysis metrics of emergency task network complexity. The complexity of different emergency procedures under grades of automation(GOA) 4 and same emergency procedure under GOA1-GOA4 are comprehensively analyzed. The research results reveal that no matter what level of automation system, the more nodes and communication between nodes, the larger network scale is, which will lead to the emergency procedures more complicated, and vice versa. The driver's behavior plays a decisive role in the variation of communicating frequencies.

At present, when the train is running between the stations, the driver cannot see the train route situation of the station ahead. Only when the train approaches the station, will the driver be able to confirm the train route information by looking at the home signal or calling the station attendant. If the train route information cannot be confirmed in time, the driver cannot control the train in accordance with the route condition, which may endanger the train safety. This article proposes a method for extending the visual distance of train routes. In this way, the driver can see the image of train route set by the station in front of the cab at any time, several kilometers away. Train route information visual distance extension (TRIVDE) improves the driver's cognitive environment and enhances the accuracy of the driver's confirmation of the train route. This article describes the realization of TRIVDE. The cognitive reliability model is established to compare the cognitive error probability of driver's confirmation of train route information before and after visual extension. TRIVDE method can improve the safety of train operation by reducing the probability of driver's cognitive error of train route.

Summary A measure of team task complexity for emergency procedures in fully automatic metro is proposed using a Euclidean norm based on two factors: internal complexity of network nodes Nodecom − inside and external complexity of network nodes Nodecom − outside. The development of the operation complexity measure followed four steps. First, team task complexity network for emergency procedures was constructed. Second, the task complexity (TC) method used as complexity measure of individual was adopted to measure the internal complexity of network nodes. Then, the external complexity of network nodes was measured by structure entropy. Finally, the team task complexity values for emergency procedures were determined by the Euclidean norm of the two factors. To verify the validity of this team task complexity measure, a one‐factor experiment was designed to test the proposed model. Thirteen team subjects participated in the experiment and performed 5 typical emergency scenes. Both objective indexes (TeamTIME) and subjective indexes (TeamWORKLOAD and TeamSUBCOMPLEXITY) were used in the experiment. The data analysis showed that the TeamTIME, TeamWORKLOAD, and TeamSUBCOMPLEXITY could be predicted well from the team task complexity value. The proposed team task complexity measure can be used for evaluation of emergency procedures design in fully automatic metro.

The present study investigates the displays on the operation desk in two locomotive cabs. Planning the layout of displays provides the fundamental understanding for the design of operation desk. Before planning the layout, designers should know the relevant importance of each display. A method based on fuzzy logic to rank the displays on the operation desk in locomotive cabs was developed. After evaluating the factors which affect display unit rank, the proposed method takes six different fuzzy operators to comprehensively synthesize the results in order to compensate the integration provided by a mixed fuzzy operator. The result has been demonstrated to be rational in the practical design.