  This paper presents a novel approach to vehicle positioning that operates
without reliance on the global navigation satellite system (GNSS). Traditional
GNSS approaches are vulnerable to interference in certain environments,
rendering them unreliable in situations such as urban canyons, under flyovers,
or in low reception areas. This study proposes a vehicle positioning method
based on learning the road signature from accelerometer and gyroscope
measurements obtained by an inertial measurement unit (IMU) sensor. In our
approach, the route is divided into segments, each with a distinct signature
that the IMU can detect through the vibrations of a vehicle in response to
subtle changes in the road surface. The study presents two different
data-driven methods for learning the road segment from IMU measurements. One
method is based on convolutional neural networks and the other on ensemble
random forest applied to handcrafted features. Additionally, the authors
present an algorithm to deduce the position of a vehicle in real-time using the
learned road segment. The approach was applied in two positioning tasks: (i) a
car along a 6[km] route in a dense urban area; (ii) an e-scooter on a 1[km]
route that combined road and pavement surfaces. The mean error between the
proposed method's position and the ground truth was approximately 50[m] for the
car and 30[m] for the e-scooter. Compared to a solution based on time
integration of the IMU measurements, the proposed approach has a mean error of
more than 5 times better for e-scooters and 20 times better for cars.
