  Velocity estimation plays a central role in driverless vehicles, but standard
and affordable methods struggle to cope with extreme scenarios like aggressive
maneuvers due to the presence of high sideslip. To solve this, autonomous race
cars are usually equipped with expensive external velocity sensors. In this
paper, we present an end-to-end recurrent neural network that takes available
raw sensors as input (IMU, wheel odometry, and motor currents) and outputs
velocity estimates. The results are compared to two state-of-the-art Kalman
filters, which respectively include and exclude expensive velocity sensors. All
methods have been extensively tested on a formula student driverless race car
with very high sideslip (10{\deg} at the rear axle) and slip ratio (~20%),
operating close to the limits of handling. The proposed network is able to
estimate lateral velocity up to 15x better than the Kalman filter with the
equivalent sensor input and matches (0.06 m/s RMSE) the Kalman filter with the
expensive velocity sensor setup.
