Large Scale Autonomous Driving Scenarios Clustering with Self-supervised Feature Extraction
Published in IV 2021, 2021
Recommended citation:
Zhao, Jinxin, Jin Fang, Zhixian Ye, and Liangjun Zhang. "Large scale autonomous driving scenarios clustering with self-supervised feature extraction." In 2021 IEEE Intelligent Vehicles Symposium (IV), pp. 473-480. IEEE, 2021.https://arxiv.org/pdf/2103.16101
The clustering of autonomous driving scenario data can substantially benefit the autonomous driving validation and simulation systems by improving the simulation tests' completeness and fidelity. This article proposes a comprehensive data clustering framework for a large set of vehicle driving data. Existing algorithms utilize handcrafted features whose quality relies on the judgments of human experts. Additionally, the related feature compression methods are not scalable for a large data-set. Our approach thoroughly considers the traffic elements, including both in-traffic agent objects and map information. Meanwhile, we proposed a self-supervised deep learning approach for spatial and temporal feature extraction to avoid biased data representation. With the newly designed driving data clustering evaluation metrics based on data-augmentation, the accuracy assessment does not require a human-labeled data-set, which is subject to human bias. Via such unprejudiced evaluation metrics, we have shown our approach surpasses the existing methods that rely on handcrafted feature extractions.
Recommended citation: Zhao, Jinxin, Jin Fang, Zhixian Ye, and Liangjun Zhang. “Large scale autonomous driving scenarios clustering with self-supervised feature extraction.” In 2021 IEEE Intelligent Vehicles Symposium (IV), pp. 473-480. IEEE, 2021.