Smartphone-based Hard-braking Event Detection at Scale for Road Safety
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- URL: http://arxiv.org/abs/2202.01934v1
- Date: Fri, 4 Feb 2022 01:30:32 GMT
- Title: Smartphone-based Hard-braking Event Detection at Scale for Road Safety
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- Authors: Luyang Liu, David Racz, Kara Vaillancourt, Julie Michelman, Matt
Barnes, Stefan Mellem, Paul Eastham, Bradley Green, Charles Armstrong, Rishi
Bal, Shawn O'Banion, Feng Guo
- Abstract summary: Road crashes are the sixth leading cause of lost disability-adjusted life-years (DALYs) worldwide.
This paper presents a scalable approach for detecting hard-braking events using the kinematics data collected from smartphone sensors.
We train a Transformer-based machine learning model for hard-braking event detection using concurrent sensor readings from smartphones and vehicle sensors from drivers who connect their phone to the vehicle while navigating in Google Maps.
- Score: 6.451490979743455
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Road crashes are the sixth leading cause of lost disability-adjusted
life-years (DALYs) worldwide. One major challenge in traffic safety research is
the sparsity of crashes, which makes it difficult to achieve a fine-grain
understanding of crash causations and predict future crash risk in a timely
manner. Hard-braking events have been widely used as a safety surrogate due to
their relatively high prevalence and ease of detection with embedded vehicle
sensors. As an alternative to using sensors fixed in vehicles, this paper
presents a scalable approach for detecting hard-braking events using the
kinematics data collected from smartphone sensors. We train a Transformer-based
machine learning model for hard-braking event detection using concurrent sensor
readings from smartphones and vehicle sensors from drivers who connect their
phone to the vehicle while navigating in Google Maps. The detection model shows
superior performance with a $0.83$ Area under the Precision-Recall Curve
(PR-AUC), which is $3.8\times$better than a GPS speed-based heuristic model,
and $166.6\times$better than an accelerometer-based heuristic model. The
detected hard-braking events are strongly correlated with crashes from publicly
available datasets, supporting their use as a safety surrogate. In addition, we
conduct model fairness and selection bias evaluation to ensure that the safety
benefits are equally shared. The developed methodology can benefit many safety
applications such as identifying safety hot spots at road network level,
evaluating the safety of new user interfaces, as well as using routing to
improve traffic safety.
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