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Frenetic at the SBST 2021 Tool Competition

机译:在SBST 2021工具比赛的狂热

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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.
机译:狂热是一种利用曲率的道路代表的遗传方法。 鉴于自主驾驶代理商,狂热的目标是产生道路,代理人无法保持在其车道内。 换句话说,狂热试图最小化“出距离距离”,这是如果汽车在车道内的车道和通道的任一边缘之间的距离,并且一旦汽车驱动关闭一旦轿厢驱动到负值。 这项工作类似于遗传算法的经典方面,例如突变和交叉,但介绍了一些旨在改善所产生的道路的多样性的细微差别。

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