Abstract: A highly accurate reference vehicle state is a requisite for the evaluation and validation
of Autonomous Driving (AD) and Advanced Driver Assistance Systems (ADASs). This highly
accurate vehicle state is usually obtained by means of Inertial Navigation Systems (INSs) that obtain
position, velocity, and Course Over Ground (COG) correction data from Satellite Navigation (SatNav).
However, SatNav is not always available, as is the case of roofed places, such as parking structures,
tunnels, or urban canyons. This leads to a degradation over time of the estimated vehicle state.
In the present paper, a methodology is proposed that consists on the use of a Machine Learning
(ML)-method (Transformer Neural Network—TNN) with the objective of generating highly accurate
velocity correction data from On-Board Diagnostics (OBD) data. The TNN obtains OBD data as input
and measurements from state-of-the-art reference sensors as a learning target. The results show that
the TNN is able to infer the velocity over ground with a Mean Absolute Error (MAE) of 0.167 km/h (0.046 m/s) when a database of 3,428,099 OBD measurements is considered. The accuracy decreases to 0.863 km/h (0.24 m/s) when only 5000 OBD measurements are used. Given that the obtained accuracy
closely resembles that of state-of-the-art reference sensors, it allows INSs to be provided with accurate
velocity correction data. An inference time of less than 40 ms for the generation of new correction
data is achieved, which suggests the possibility of online implementation. This supports a highly
accurate estimation of the vehicle state for the evaluation and validation of AD and ADAS, even in
SatNav-deprived environments.
