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Smooth Reference Line Generation for a Race Track with Gates based on Defined Borders | IEEE Conference Publication | IEEE Xplore

Smooth Reference Line Generation for a Race Track with Gates based on Defined Borders


Abstract:

As racing sports pushed the technological limits of vehicles in the past, automated racing has the potential to directly evaluate new technologies from research in a comp...Show More

Abstract:

As racing sports pushed the technological limits of vehicles in the past, automated racing has the potential to directly evaluate new technologies from research in a competitive environment. The required speed of implementation and the fair evaluation criteria enable very fast progress in selecting potential strategies, architectures and algorithms. By this, automated racing strongly contributes to science as well as to future every day implementations of driving automation. In this publication, the approaches used by the team Autonomous-Racing-Graz at the ROBORACE final races in season alpha are revealed. The contribution describes how to generate a smooth reference line in case of non-smooth borders and additional precision gates on a race track. The generation of an initial smooth reference line is key to assure convergence in later optimization. The described data processing is based on track geometry defined by borders only. The specific challenge addressed in the approach was the incorporation of tight, short gates and the low computational effort of the algorithms, enabling fast tuning and adaptation during racing events onsite.
Date of Conference: 19 October 2020 - 13 November 2020
Date Added to IEEE Xplore: 08 January 2021
ISBN Information:

ISSN Information:

Conference Location: Las Vegas, NV, USA

I. Introduction

Autonomous driving has made increasing interest in academic and industrial research in the past two decades. The aim is to design a vehicle having the same (or even better) driving skills compared to a human driver in terms of safety, performance and efficiency [1]. This paper specifically focuses on autonomous racing: a part of highly automated driving, with the objective to compete with other automated vehicles or human drivers on a race track in different driving challenges.

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