Data and Knowledge for Overtaking Scenarios in Autonomous Driving

TL;DR

This paper introduces a new synthetic dataset focused on overtaking maneuvers in autonomous driving, addressing the lack of real-world data and reviewing existing datasets and features.

Contribution

The work presents a novel synthetic dataset specifically designed for overtaking scenarios and reviews current datasets and features in autonomous driving research.

Findings

Identified limitations in existing datasets for overtaking maneuvers

Proposed new features focused on overtaking scenarios

Created a synthetic dataset to facilitate research in overtaking maneuvers

Abstract

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.