Affiliation:
1. Guangxi Key Laboratory of Intelligent Transportation System, Guilin University of Electronic Technology, Guilin 541004, China
2. Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China
3. State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Abstract
This paper investigates the effects of non-driving related tasks, take-over request time, and take-over mode interactions on take-over performance in human–machine cooperative driving in a highway environment. Based on the driving simulation platform, a human–machine collaborative driving simulation experiment was designed with various take-over quality influencing factors. The non-driving related tasks included no task, listening to the radio, watching videos, playing games, and listening to the radio and playing games; the take-over request time was set to 6, 5, 4, and 3 s, and the take-over methods include passive and active take-over. Take-over test data were collected from 65 drivers. The results showed that different take-over request times had significant effects on driver take-over performance and vehicle take-over steady state (p < 0.05). Driver reaction time and minimum TTC decreased with decreasing take-over request time, maximum synthetic acceleration increased with decreasing take-over request time, accident rate increased significantly at 3 s take-over request time, and take-over safety was basically ensured at 4 s request time. Different non-driving related tasks have a significant effect on driver take-over performance (p < 0.05). Compared with no task, non-driving related tasks significantly increase driver reaction time, but they only have a small effect on vehicle take-over steady state. Vehicle take-over mode has a significant effect on human–machine cooperative driving take-over quality; compared with passive take-over mode, the take-over quality under active take-over mode is significantly lower.
Funder
National Natural Science Foundation of China
Guangxi Science and Technology Base and Talent Special Project
Guilin Key Research and Development Program
Innovation Project of GUET Graduate Education
Subject
Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction
Cited by
1 articles.
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1. Human-Machine Co-Driving Mode Switching Method of Autonomous Vehicles based on Traffic Scenarios;2023 5th International Conference on Intelligent Control, Measurement and Signal Processing (ICMSP);2023-05-19