Analysis and modelling of drivers’ responses to safety-critical lane changes performed by heavy vehicles
Date
Type
Examensarbete för masterexamen
Programme
Model builders
Journal Title
Journal ISSN
Volume Title
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Abstract
According to the National Highway Transport Safety Administration (NHTSA),
around 240,000-610,000 crashes due to lane change are reported to the police annually.
In addition, over 300,000 lane change crashes remain unreported to the police.
These high crash numbers indicate that lane change maneuvers pose a high risk
to the lane changing and surrounding vehicles. Automated driving can possibly
reduce lane change crashes or mitigate their consequences by promoting safer lane
changes. In this context, this thesis mainly focuses on the analysis of safety critical
lane change maneuvers using Naturalistic Driving Data from the SHRP2 database.
The lane changes being considered in this thesis are performed by heavy vehicles, as
observed from the perspective of a following vehicle equipped with forward facing
camera. A total of 89 cases were found relevant for the scope of the study and manually
annotated to extract variables which could describe the interaction between
the heavy vehicles changing lane and the following passenger car. The annotated
variables were used to quantitatively describe the lane change maneuver and to determine
the factors responsible for the start of braking of the following passenger
car. The results show that lateral motion of the heavy vehicle and a combination of
lateral motion and blinking of the heavy vehicle are the main factor triggering the
braking of the following car’s driver. The speed difference of the following vehicle
between the points when heavy vehicle starts its lateral motion and when the following
vehicle starts to brake also has an impact on the braking of the following vehicle.
These results have potential application for the design and testing on critical lane
change maneuvers performed by an automated truck.
Description
Keywords
Automated driving, Human Factors, Driver models, Heavy Duty vehicles, Automated Trucks