Frenetic at the SBST 2021 Tool Competition

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

研究成果: Conference contribution

8 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings - 2021 IEEE/ACM 14th International Workshop on Search-Based Software Testing, SBST 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ページ36-37
ページ数2
ISBN(電子版)9781665445719
DOI
出版ステータスPublished - 2021 5月
外部発表はい
イベント14th IEEE/ACM International Workshop on Search-Based Software Testing, SBST 2021 - Virtual, Online
継続期間: 2021 5月 222021 5月 30

出版物シリーズ

名前Proceedings - 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

  • ソフトウェア
  • 安全性、リスク、信頼性、品質管理
  • 制御と最適化

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