Data and Knowledge for Overtaking Scenarios in Autonomous Driving
Mariana Pinto Inês Dutra Joaquim Fonseca
Autonomous driving has become one of the most popular research topics within
Artificial Intelligence. An autonomous vehicle is understood as a system that
combines perception, decision-making, planning, and control. All of those tasks
require that the vehicle collects surrounding data in order to make a good
decision and action. In particular, the overtaking maneuver is one of the most
critical actions of driving. The process involves lane changes, acceleration
and deceleration actions, and estimation of the speed and distance of the
vehicle in front or in the lane in which it is moving. Despite the amount of
work available in the literature, just a few handle overtaking maneuvers and,
because overtaking can be risky, no real-world dataset is available. This work
contributes in this area by presenting a new synthetic dataset whose focus is
the overtaking maneuver. We start by performing a thorough review of the state
of the art in autonomous driving and then explore the main datasets found in
the literature (public and private, synthetic and real), highlighting their
limitations, and suggesting a new set of features whose focus is the overtaking
maneuver.