Metrics for Evaluating Social Conformity of Crowd Navigation Algorithms
It addresses the problem of inconsistent evaluation for researchers in robotics and AI, enabling better comparison and generalization of navigation algorithms, though it is incremental as it builds on existing methods.
This paper tackles the inconsistency in evaluating social crowd navigation algorithms by proposing a unified set of metrics and a systematic protocol, testing four state-of-the-art algorithms to reveal generalization challenges and showing that the protocol improves performance.
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.