Hi! My name is Andrii Matviienko from Technical University of Darmstadt, Germany. I'm going to
present our work titled 'Quantified cycling safety towards a mobile sensing platform to
understand perceived safety of cyclists'. This work was conducted together with my colleagues
Florian heller from Hasselt University, Belgium and Bastian Pfleging from Eindhoven University
of Technology in Netherlands. Cycling safety is typically reflected in accident reports submitted
by police or it is assessed based on cyclists objective responses. Although the later one
provides data without necessarily encountering an accident and the former one reports a dangerous
situation post-hoc both of them rely on subjective assessments of the involved contacts and parties,
such as police staff and officers cyclists and other road users. In our work we go beyond this
established methods by exploring quantified cyclist data resources, e.g., physiological
data and body movement, to better understand the perceived safety of cyclists with the goal
to improve traffic safety. Previously researchers designed cyclist assistant systems that primarily
focused on the data provided from external sources, such as other road users broadcasting
their positions and velocity using Vehicle-to-x technology, e.g., Car2X, or from internal sources,
such as physiological data from sensors placed on the cyclist's body or on the bicycle itself.
This separation between external and internal sources based on the existing methods of data
collection assisted us in defining two dimensions in the proposed taxonomy for assessing cycling
safety. By comparing prior work on perceived cycling safety, we identified two dimensions
along which existing approaches can be assessed. These complementary dimensions form our taxonomy
of methods to assess cyclist safety. The first one is data source, which can be internal or external
and the second one is measurement, which can be objective or subjective. The first dimension
data source is split into internal, e.g., body movements or physiological data, and external
data, e.g., traffic cameras, car sensing and other infrastructure data sources. The second dimension
type of measurement (or measurement) covers subjective and objective aspects of the data,
which are measured and assessed. The subjective part includes sources
with cyclists, road users or police officers subjective estimations,
while objective methods rely on the quantitative assessment of input data from the sensors.
In this work we looked at earables as earbuds that do not only provide audio output, but also deliver
a rich sensor input from an inertial measurement unit. In cycling context such earables will allow
collecting sensor data to measure the amount of relative head head rotation as an indicator how
much a cyclist looks around. When using earables with an integrated 9-DoF IMU that returns absolute
orientation, we can measure the difference between the smartphone in the pocket of the
cyclist as a reference for the driving direction of the earbuds. This will help to determine in
which direction the cyclist looked, eventually getting an indication which potential elements of
interest, e.g., approaching cars or pedestrians, require additional attention. We derived five
use cases based on the accident reports that we considered as challenging for cyclists in terms
of decision making and which might require measures to improve perceived safety. While
situations 'street entering' and 'unprotected turn' are considered the most dangerous ones
for cyclists based on accident reports, we also consider situations such as 'protected left turn',
'obstacles' and 'lane change', which include a comparable level of difficulty, but are typically
missed in statistical reports. Following the proposed taxonomy and proposed crowdsourcing
approach we plan to implement a prototype and run a series of experiments with cyclists to test our
concept and assess other hypotheses related to the correlation between head movements and perceived
road safety. In this work we showcase one possible technical solution of collecting internal data
about cyclists from an objective perspective. With future development of Car2X technology we envision
not only received safety relevant information from other vehicles and infrastructure, but also
sending cyclist state and behavior to other users as an additional safety
indicator. That was quantified cycling safety. Thank you for your attention.