Behavioral adaptation of drivers when driving among automated vehicles
Journal article, 2022

Purpose
This paper aims to explore whether drivers would adapt their behavior when they drive among automated vehicles (AVs) compared to driving among manually driven vehicles (MVs).Understanding behavioral adaptation of drivers when they encounter AVs is crucial for assessing impacts of AVs in mixed-traffic situations. Here, mixed-traffic situations refer to situations where AVs share the roads with existing nonautomated vehicles such as conventional MVs.

Design/methodology/approach
A driving simulator study is designed to explore whether such behavioral adaptations exist. Two different driving scenarios were explored on a three-lane highway: driving on the main highway and merging from an on-ramp. For this study, 18 research participants were recruited.

Findings
Behavioral adaptation can be observed in terms of car-following speed, car-following time gap, number of lane change and overall driving speed. The adaptations are dependent on the driving scenario and whether the surrounding traffic was AVs or MVs. Although significant differences in behavior were found in more than 90% of the research participants, they adapted their behavior differently, and thus, magnitude of the behavioral adaptation remains unclear.

Originality/value
The observed behavioral adaptations in this paper were dependent on the driving scenario rather than the time gap between surrounding vehicles. This finding differs from previous studies, which have shown that drivers tend to adapt their behaviors with respect to the surrounding vehicles. Furthermore, the surrounding vehicles in this study are more “free flow'” compared to previous studies with a fixed formation such as platoons. Nevertheless, long-term observations are required to further support this claim.

Automated vehicles

behavioural adaptation

human-robot interactions

driver behaviours and assistance

driving simulator experiment

Author

Maytheewat Aramrattana

The Swedish National Road and Transport Research Institute (VTI)

Jiali Fu

The Swedish National Road and Transport Research Institute (VTI)

Selpi Selpi

Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI

Journal of Intelligent and Connected Vehicles

23999802 (eISSN)

Vol. 5 3 309-315

Heterogeneous Traffic Groups Cooperative Driving Behaviours Research under Mixed Traffic Condition

VINNOVA (2018-02891), 2019-04-01 -- 2021-03-31.

Areas of Advance

Transport

Subject Categories

Interaction Technologies

Vehicle Engineering

DOI

10.1108/JICV-07-2022-0031

More information

Latest update

1/3/2024 9