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Title: The identification of non-driving activities with associated implication on the take-over process
Authors: Yang, Lichao
Semiromi, Babayi Mahdi
Xing, Yang
Lv, Chen
Brighton, James
Zhao, Yifan
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Yang, L., Semiromi, B. M., Xing, Y., Lv, C., Brighton, J. & Zhao, Y. (2022). The identification of non-driving activities with associated implication on the take-over process. Sensors, 22(1), 42-.
Journal: Sensors
Abstract: 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.
ISSN: 1424-8220
DOI: 10.3390/s22010042
Schools: School of Electrical and Electronic Engineering 
Rights: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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