Early risk diagnosis and driving anomaly detection from vehicle stream are of great benefits in a range of advanced solutions towards Smart Road and crash prevention, although there are intrinsic challenges, especially lack of ground truth, definition of multiple risk exposures. This study proposes a domain-specific automatic clustering (termed Autocluster) to self-learn the optimal models for unsupervised risk assessment, which integrates key steps of risk clustering into an auto-optimisable pipeline, including feature and algorithm selection, hyperparameter auto-tuning. Firstly, based on surrogate conflict measures, indicator-guided feature extraction is conducted to construct temporal-spatial and kinematical risk features. Then we develop an elimination-based model reliance importance (EMRI) method to unsupervised-select the useful features. Secondly, we propose balanced Silhouette Index (bSI) to evaluate the internal quality of imbalanced clustering. A loss function is designed that considers the clustering performance in terms of internal quality, inter-cluster variation, and model stability. Thirdly, based on Bayesian optimisation, the algorithm selection and hyperparameter auto-tuning are self-learned to generate the best clustering partitions. Various algorithms are comprehensively investigated. Herein, NGSIM vehicle trajectory data is used for test-bedding. Findings show that Autocluster is reliable and promising to diagnose multiple distinct risk exposures inherent to generalised driving behaviour. Besides, we also delve into risk clustering, such as, algorithms heterogeneity, Silhouette analysis, hierarchical clustering flows, etc. Meanwhile, the Autocluster is also a method for unsupervised multi-risk data labelling and indicator threshold calibration. Furthermore, Autocluster is useful to tackle the challenges in imbalanced clustering without ground truth or priori knowledge

中文翻译：

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自动聚类用于智能道路车辆驾驶的无监督风险诊断

尽管存在一些内在的挑战，尤其是缺乏基本事实，多种风险暴露的定义，但在智能道路和防撞预防的一系列高级解决方案中，早期风险诊断和车辆车流异常检测具有很大的优势。这项研究提出了一种特定领域的自动聚类（称为Autocluster），用于自学习无监督风险评估的最佳模型，该模型将风险聚类的关键步骤集成到了一个自动优化的管道中，包括功能和算法选择，超参数自动调整。首先，基于替代冲突度量，进行指标指导的特征提取以构造时空和运动风险特征。然后，我们开发了一种基于消除的模型依赖重要性（EMRI）方法来无监督地选择有用的功能。其次，我们提出了平衡剪影指数（bSI）来评估不平衡聚类的内部质量。设计了一个损失函数，该函数从内部质量，集群间差异和模型稳定性方面考虑了集群性能。第三，基于贝叶斯优化，可以自学习算法选择和超参数自动调整，以生成最佳的聚类分区。对各种算法进行了全面研究。在此，将NGSIM车辆的轨迹数据用于测试床上用品。研究结果表明，Autocluster是可靠的，并且有望诊断出广义驾驶行为固有的多种不同的风险敞口。此外，我们还研究了风险聚类，例如算法异质性，Silhouette分析，分层聚类流等。Autocluster还是一种无监督的多风险数据标记和指标阈值校准的方法。此外，Autocluster对于解决不平衡集群的挑战也很有用，而无需基础事实或先验知识