My name is Kevin Koch and I am very pleased to present our work on when do Drivers interact
with In-Vehicle Well-being Interventions.
This work was done in collaboration with Varun Mishra and colleagues at the University of
St. Gallen, Dartmouth College, ETH Zürich and the University of Bern.
The daily car commute is considered as one of the most unpleasant activities of the day
(often associated with negative situations such as traffic jams leading to loss of time and frustrations).
Nevertheless, it is an integral part of people’s life, for example, alone 57 million people
in the US commute everyday at least one hour with their car.
As a consequence, researchers (but also car manufactures) identified the car as an opportune
environment for novel well-being interventions to mitigate these negative experiences.
Examples of these interventions are guided breathing exercises, personalized voice assistants
or pleasant in-car scents, all developed with the goal in mind to support drivers.
Developing effective interventions is one problem, however, safely delivering them is another.
Interventions that are delivered when drivers are in stressful situations as in city traffic
may increase the chance for accidents.
Hence, researchers worked in recent times on understanding when drivers are safely interruptible.
Although both topics have recently gained momentum, there are still shortcomings.
Even though several in-vehicle interventions have been validated, research has mostly been
conducted in controlled environments such as driving simulators or on pre-defined circuits.
Moreover, prior work on driver interruptibility did not use real-world interventions.
Instead drivers were required to complete artificial tasks such as n-back tasks or were
simply asked if they were interruptible while driving.
Our work addresses these shortcomings by examining drivers’ interactions with real interventions
in a longitudinal study on open roads.
In our study, we inspected two main goals in more detail.
First, we wanted to understand when drivers' respond to in-vehicle interventions.
In particular, we were interested what influence factors exist that let drivers start or not
start an intervention and if they started an intervention what lead to their completion or cancellation.
Hereby, we inspected two time dimensions a) when they enter the car (knowing implicitly
what kind of trip they make) and b) when they are driving (the current moment).
Second, as the delivery and conductance of interventions may cause distractions, we wanted
to understand whether in-vehicle interventions influence the driving behavior.
To inspect our research goals, we developed and retrofitted an intervention system for
cars and used two in-vehicle interventions that proved as effective in our prior work.
The first intervention was a mindfulness exercise that was especially designed for driving.
Participants were guided to shift their focus between the environment (e.g., the street
or other traffic participants) and their current thoughts.
The second intervention was a personalized music experience.
Participants were asked to create a playlist of minimum 10 songs, however, with a certain twist.
Each song should be selected based on a positive memory from their life - the idea was that
by listening to the song they could relive this positive moment.
The interventions were delivered by a smartphone that was mounted in the interior and connected
to the car infotainment.
Furthermore, the system collected several driving behavior related sensors from the
car, such as vehicle speed, steering behavior, pedal usage or GPS locations.
During the study, we equipped 10 cars with our system and handed these cars to 10 participants
that they then used for their daily drives.
In total, we collected 1’009 trips.
As the aggregated map view of all trips shows, the dataset includes daily commuter trips,
but also trips made during leisure time.
Moreover, 600 interventions were during this time delivered, of which 402 were started
and 285 completed.
In our analysis, we derived features from our collected dataset to understand the two
prior mentioned research goals.
On the trip level, we calculated for example the trips' average speed, whether occupants
were in the car or the ratio of used road types throughout the trip.
On the moment analysis, we sampled short time windows of 15 secs. that described the current
situation as the current speed, the steering wheel usage or the current street type.
Further, we found confirmation of our findings in interviews with the participants.
When we look at the response behavior, we identified that participants are more likely
to conduct interventions on longer drives, without other passengers on board and while
being on a commute trip.
On the moment level, interventions were more likely in situations with little need for
change in driving behavior, in particular on highways or other major roads.
Hence, our analysis shows that routine trips as commutes and situations with less cognitive
load such as highways with steady speeds or steering behavior are favorable times for
interventions.
Moreover, we inspected the impact on driving and thus the safety implications of the interventions.
We identified no major changes, especially no increase in critical events such as sudden
brake maneuvers or steering maneuvers.
Instead, we found out that participants had signs for safer driving: they had less speed
violations, smoother steering or less sudden braking events.
Therefore, we see that the interventions seem to cause no harm, but may have positive effects
that can lead to drivers driving more calmly.
In the future, we envision that these findings can help to develop and validate an in-vehicle
intervention delivery system that follows two major decision points:
1. A prediction whether a trip is suitable for an intervention
2. If yes, a continuous prediction whether the driver can conduct in the current situation
an intervention or not.
Thank you for your attention and interest, please feel free to reach out if you have
any further questions and enjoy Ubicomp 2021.