Self-Balancing Online Dataset for Incremental Driving Intelligence

Hyung-Suk Yoon, Chan Kim, Seong-Woo Kim, 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.