SBST 2021工具竞赛中的狂热者

2021 IEEE/ACM 14th International Workshop on Search-Based Software Testing (SBST) Pub Date : 2021-05-01 DOI:10.1109/SBST52555.2021.00016

Ezequiel Castellano, A. Cetinkaya, Cédric Ho Thanh, Stefan Klikovits, Xiaoyi Zhang, Paolo Arcaini

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引用次数: 19

摘要

狂热是一种利用基于曲率的道路表示的遗传方法。给定一个自动驾驶代理，Frenetic的目标是生成代理无法保持在其车道内的道路。换句话说，Frenetic试图最小化“超限距离”，即如果汽车在车道内，汽车与车道两侧之间的距离，一旦汽车驶离车道，则将其变为负值。这项工作类似于遗传算法的经典方面，如突变和交叉，但引入了一些细微的差别，旨在提高生成道路的多样性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Frenetic at the SBST 2021 Tool Competition

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.

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来源期刊

2021 IEEE/ACM 14th International Workshop on Search-Based Software Testing (SBST)

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