Frenetic at the SBST 2021 Tool Competition

Ezequiel Castellano, Ahmet Cetinkaya, Cedric Ho Thanh, Stefan Klikovits, Xiaoyi Zhang, Paolo Arcaini

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

Frenetic is a genetic approach that leverages a curvature-based road representation. Given an autonomous driving agent, the goal of Frenetic is to generate roads where the agent fails to stay within its lane. In other words, Frenetic tries to minimize the 'out of bound distance', which is the distance between the car and either edge of the lane if the car is within the lane, and proceeds to negative values once the car drives off. This work resembles classic aspects of genetic algorithms such as mutations and crossover, but introduces some nuances aiming at improving diversity of the generated roads.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE/ACM 14th International Workshop on Search-Based Software Testing, SBST 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages36-37
Number of pages2
ISBN (Electronic)9781665445719
DOIs
Publication statusPublished - 2021 May
Externally publishedYes
Event14th IEEE/ACM International Workshop on Search-Based Software Testing, SBST 2021 - Virtual, Online
Duration: 2021 May 222021 May 30

Publication series

NameProceedings - 2021 IEEE/ACM 14th International Workshop on Search-Based Software Testing, SBST 2021

Conference

Conference14th IEEE/ACM International Workshop on Search-Based Software Testing, SBST 2021
CityVirtual, Online
Period21/5/2221/5/30

ASJC Scopus subject areas

  • Software
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

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