论文标题

评估人群导航算法的社会一致性的指标

Metrics for Evaluating Social Conformity of Crowd Navigation Algorithms

论文作者

Wang, Junxian, Chan, Wesley P., Carreno-Medrano, Pamela, Cosgun, Akansel, Croft, Elizabeth

论文摘要

由于对“社会行为”的多样化定义,通过人群进行培训和评估自动机器人导航的最新协议和指标是不一致的。这使得很难(即使不是不可能)有效地比较已发布的导航算法。此外,由于缺乏良好的评估协议,由于缺乏培训多样性,因此导致的算法可能无法概括。为了解决这些差距,本文通过提出一套一致的指标来促进人群导航算法的更全面的评估和客观比较,这些指标既说明效率和社会符合性,又是一个系统的协议,其中包括多个人群导航方案的多种复杂性,以进行评估。我们根据此协议测试了四种最先进的算法。结果表明,某些最先进的算法在概括方面有很多挑战,并且使用我们的协议进行培训,我们能够提高算法的性能。我们证明,一组提议的指标提供了更多的洞察力,并有效地区分了这些算法在效率和社会符合性方面的性能。

Recent protocols and metrics for training and evaluating autonomous robot navigation through crowds are inconsistent due to diversified definitions of "social behavior". This makes it difficult, if not impossible, to effectively compare published navigation algorithms. Furthermore, with the lack of a good evaluation protocol, resulting algorithms may fail to generalize, due to lack of diversity in training. To address these gaps, this paper facilitates a more comprehensive evaluation and objective comparison of crowd navigation algorithms by proposing a consistent set of metrics that accounts for both efficiency and social conformity, and a systematic protocol comprising multiple crowd navigation scenarios of varying complexity for evaluation. We tested four state-of-the-art algorithms under this protocol. Results revealed that some state-of-the-art algorithms have much challenge in generalizing, and using our protocol for training, we were able to improve the algorithm's performance. We demonstrate that the set of proposed metrics provides more insight and effectively differentiates the performance of these algorithms with respect to efficiency and social conformity.

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