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

Automatic Clustering for Unsupervised Risk Diagnosis of Vehicle Driving for Smart Road