Large Scale Autonomous Driving Scenarios Clustering with Self-supervised Feature Extraction

• Published in: 2021 IEEE Intelligent Vehicles Symposium (IV) pp. 473 - 480
• Main Authors: Zhao, Jinxin, Fang, Jin, Ye, Zhixian, Zhang, Liangjun
• Format: Conference Proceeding
• Language: English
• Published: IEEE 11.07.2021
• Subjects: Benchmark testing; Clustering algorithms; Data models; Deep learning; Feature extraction; Measurement; Vehicle driving
• DOI: 10.1109/IV48863.2021.9575644

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 dataset. 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 dataset, 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.