Comprehensive study on context-aware and behavioural anomaly risk assessment and prevention system : A hybrid AI-driven approach to risky driving pattern detection

Abstract

The number of car accidents is occurring on a global basis at an unprecedented scale, and as a consequence, this leads to an increasing demand for improving the driving risk assessment systems. Existing systems are often inadequate, as they fail to sufficiently incorporate both driver behavior and environmental context and thus often fail to perform well in real-world driving scenarios. In this work, we propose a hybrid and context-aware driving risk assessment framework, which models driver behavior and environmental conditions by employing a progressive fusion-based framework. The framework utilizes the US Accidents dataset (2016-2023) as context and risk factor, and a mobile-phone based Driving Behavior dataset capturing driving behavior. Individual classification and regression models are trained using custom built neural networks in the first stage in order to learn feature representations that are non-linear. Later, two fusion strategies are introduced, including decision level fusion (combining neural networks using probability combinations) and late fusion (combining the prediction of the classifier models with the predicted risks from context information), respectively. Besides this, in order to effectively model driver behavior over time, sequence based learning is employed to model behaviors, and a refinement layer is applied to further boost the classification accuracy of models. Our proposed approach generates a continuous and interpretable risk score and outperforms traditional methods and the single classifier by producing satisfactory classification performances. This work also emphasizes that the combined use of deep learning and fusion is an efficient approach for large-scale, reliable and multimodal driving risk assessment.