Browsing by Author "Semiromi, Mahdi Babayi"

Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    The identification of non-driving activities with associated implication on the take-over process
    (MDPI, 2021-12-22) Yang, Lichao; Semiromi, Mahdi Babayi; Xing, Yang; Lv, Chen; Brighton, James; Zhao, Yifan
    In conditionally automated driving, the engagement of non-driving activities (NDAs) can be regarded as the main factor that affects the driver’s take-over performance, the investigation of which is of great importance to the design of an intelligent human–machine interface for a safe and smooth control transition. This paper introduces a 3D convolutional neural network-based system to recognize six types of driver behaviour (four types of NDAs and two types of driving activities) through two video feeds based on head and hand movement. Based on the interaction of driver and object, the selected NDAs are divided into active mode and passive mode. The proposed recognition system achieves 85.87% accuracy for the classification of six activities. The impact of NDAs on the perspective of the driver’s situation awareness and take-over quality in terms of both activity type and interaction mode is further investigated. The results show that at a similar level of achieved maximum lateral error, the engagement of NDAs demands more time for drivers to accomplish the control transition, especially for the active mode NDAs engagement, which is more mentally demanding and reduces drivers’ sensitiveness to the driving situation change. Moreover, the haptic feedback torque from the steering wheel could help to reduce the time of the transition process, which can be regarded as a productive assistance system for the take-over process.
  • Thumbnail Image
    ItemOpen Access
    The implication of non-driving activities on situation awareness and take-over performance in level 3 automation
    (IEEE, 2020-11-18) Yang, Lichao; Semiromi, Mahdi Babayi; Auger, Daniel J.; Dmitruk, Arkadiusz; Brighton, James; Zhao, Yifan
    The driver's take-over performance is of great importance for driving safety in conditionally automated driving since the driver is required to respond appropriately to control the vehicle if there is a system failure. The engagement of different non-driving activities (NDAs), considered as the main factor of the driver's take-over performance has been investigated in this study from both perspectives of the driver's situation awareness and take-over quality. The activities are divided into 2 groups, which are active interaction mode and passive interaction mode based on the engagement of human and object. The results suggest that the engagement of NDAs could reduce the driver's situation awareness. Driver's attention level is different for each activity. Particularly, active interaction mode NDAs requests more mentally demanding and drivers are not sensitive to the driving situation change when they are doing such activities. In addition, there is no significant difference in the maximum lateral error with NDAs engagement. However, it takes more time to achieve a safe control transition for drivers who are doing the NDAs. The active interaction mode NDAs request even more time. Moreover, the transition process could benefit from steering wheel haptic feedback torque, which can be considered as an effective take-over assistance system.