Our paper on Incremental Intelligence is accepted to IROS 2021
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작성자 최고관리자 댓글 조회 작성일 21-10-14 15:19본문
The following paper is accepted to the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021):
Self-Balancing Online Dataset for Incremental Driving Intelligence
by Hyung-Suk Yoon, Chan Kim, Seong-Woo Kim, and Seung-Woo Seo
Abstract
Autonomous driving with imitation learning is vulnerable to the quality of an expert dataset. Typical driving involves situations or online data that are biased toward specific scenarios such as lane following or stop. This property causes an imbalance in the driving dataset, and it is highly likely to deteriorate the performance of autonomous driving with imitation learning. In this paper, we propose a dataset self-balancing system with biased online data and an imbalanced dataset. By estimating the probability distribution of a dataset, we compute the probability and novelty of online data and then filter only qualified novel data. In addition, using the computed probability distribution, we determine the data that are non-informative in the current dataset and then exchange them with novel online data. At last, by retraining the driving neural network with high-entropy data batches, our method achieves incremental driving intelligence. We demonstrated the effectiveness of our method through open-loop evaluation and ablation studies in a CARLA simulator; the results show that our proposed system effectively balances the dataset with 100 scenarios and decreases test loss over time.
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