Classification of Automated Lane-Change Styles by Modeling and Analyzing Truck Driver Behavior: A Driving Simulator Study

Lane change is a highly demanding driving task. A number of traffic accidents are induced by erroneous maneuvers. An automated lane-change system has the potential to reduce the driver workload and improve driving safety. A challenge is to improve the driver acceptance of the automated system. From...

Full description

Saved in:
Bibliographic Details
Published in:IEEE open journal of intelligent transportation systems 2022, Vol.3, p.1-1
Main Authors: Wang, Zheng, Guan, Muhua, Lan, Jin, Yang, Bo, Kaizuka, Tsutomu, Taki, Junichi, Nakano, Kimihiko
Format: Article
Language:English
Subjects:
Acceptance tests
automated driving
Automation
Behavioral sciences
Decision making
Driver behavior
driving styles
Evaluation
Human factors
human-machine systems
Intelligent transportation systems
Lane changing
Modelling
Parameter identification
Safety
Simulation
Task analysis
Traffic accidents
Vehicle safety
Vehicles
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Lane change is a highly demanding driving task. A number of traffic accidents are induced by erroneous maneuvers. An automated lane-change system has the potential to reduce the driver workload and improve driving safety. A challenge is to improve the driver acceptance of the automated system. From the perspective of human factors, an automated system with different styles would improve user acceptance, because drivers could drive with different styles in different driving scenarios. This paper proposes a method to design different lane-change styles for automated driving by analyzing and modeling truck-driver behavior. A truck driving simulator experiment with 12 participants was conducted to identify the driver-model parameters. The lane change styles were classified into three types: aggressive, medium, and conservative. The proposed automated lane-change system was evaluated by another truck driving simulator experiment with the same 12 participants. Moreover, the effects of different lane-change decision-making styles on the driver experience and acceptance were evaluated from the perspectives of both the ego truck and surrounding vehicles. The evaluation results demonstrate that different lane-change decision-making styles can be distinguished by drivers. Overall, the three styles were evaluated by the human drivers as being safe and reliable. The main contribution of this study is that it provides the insights into the design of an automated driving system with different driving styles. Furthermore, these observations can be applied to commercial automated trucks.
ISSN:2687-7813
2687-7813
DOI:10.1109/OJITS.2022.3222442