How do oncoming traffic and cyclist lane position influence cyclist overtaking by drivers?
Journal article, 2020

Overtaking cyclists is challenging for drivers because it requires a well-timed, safe interaction between the driver, the cyclist, and the oncoming traffic. Previous research has investigated this manoeuvre in different experimental environments, including naturalistic driving, naturalistic cycling, and simulator studies. These studies highlight the significance of oncoming traffic—but did not extensively examine the influence of the cyclist’s position within the lane. In this study, we performed a test-track experiment to investigate how oncoming traffic and position of the cyclist within the lane influence overtaking. Participants overtook a robot cyclist, which was controlled to ride in two different lateral positions within the lane. At the same time, an oncoming robot vehicle was controlled to meet the participant’s vehicle with either 6 or 9 s time-to-collision. The order of scenarios was randomized over participants. We analysed safety metrics for the four different overtaking phases, reflecting drivers’ safety margins to rear-end, head-on, and side-swipe collisions, in order to investigate the two binary factors: 1) time gap between ego vehicle and oncoming vehicle, and 2) cyclist lateral position. Finally, the effects of these two factors on the safety metrics and the overtaking strategy (either flying or accelerative depending on whether the overtaking happened before or after the oncoming vehicle had passed) were analysed. The results showed that, both when the cyclist rode closer to the centre of the lane and when the time gap to the oncoming vehicle was shorter, safety margins for all potential collisions decreased. Under these conditions, drivers—particularly female drivers—preferred accelerative over flying manoeuvres. Bayesian statistics modelled these results to inform the development of active safety systems that can support drivers in safely overtaking cyclists.

Cycling safety

driver behaviour

Bayesian modelling

driver assistance

test track


Alexander Rasch

Chalmers, Mechanics and Maritime Sciences (M2), Vehicle Safety

Christian-Nils Åkerberg Boda

Chalmers, Mechanics and Maritime Sciences (M2), Vehicle Safety

Prateek Thalya

Chalmers, Mechanics and Maritime Sciences (M2), Vehicle Safety


Tobias Aderum


Alessia Knauss


Marco Dozza

Chalmers, Mechanics and Maritime Sciences (M2), Vehicle Safety

Accident Analysis and Prevention

0001-4575 (ISSN)

Vol. 142 105569

DIV - Driver Interaction with Vulnerable Road Users

Autoliv AB, 2015-09-01 -- 2020-08-31.

Toyota Motor Europe, 2015-09-01 -- 2020-08-31.

MICA - Modelling Interaction between Cyclists and Automobiles

VINNOVA (2017-05522), 2018-03-09 -- 2019-12-31.

Areas of Advance


Subject Categories

Transport Systems and Logistics


Infrastructure Engineering

Vehicle Engineering



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