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

• URL: http://arxiv.org/abs/2103.16101v1
• Date: Tue, 30 Mar 2021 06:22:40 GMT
• Title: Large Scale Autonomous Driving Scenarios Clustering with Self-supervised Feature Extraction
• Authors: Jinxin Zhao, Jin Fang, Zhixian Ye and Liangjun Zhang
• Abstract summary: This article proposes a comprehensive data clustering framework for a large set of vehicle driving data. Our approach thoroughly considers the traffic elements, including both in-traffic agent objects and map information. With the newly designed driving data clustering evaluation metrics based on data-augmentation, the accuracy assessment does not require a human-labeled data-set.
• Score: 6.804209932400134
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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