How Much is Too Much? Learning Personalised Risk Thresholds in Real-World Driving

While naturalistic driving studies have become foundational for providing real-world driver behaviour data, the existing frameworks for identifying risk based on such data have two fundamental limitations: (i) they rely on predefined time windows and fixed thresholds to disentangle risky and normal episodes of driving behaviour, and (ii) they assume stationary behavioural distribution across drivers and trips. These limitations have hindered the ability of the existing frameworks to capture behavioural nuances, adapt to individual variability, or respond to stochastic fluctuations in driving contexts. Thus, there is a need for a unified framework that jointly adapts risk labels and model learning to per-driver behavioural dynamics, a gap this study aims to bridge. We present an adaptive and personalised risk detection framework, built on Belgian naturalistic driving data, integrating a rolling time window with bi-level optimisation and dynamically calibrating both model hyperparameters and driver-specific risk thresholds at the same time. The framework was tested using two safety indicators, speed-weighted time headway and harsh driving events, and three models: Random Forest, XGBoost, and Deep Neural Network (DNN). Speed-weighted time headway yielded more stable and context-sensitive classifications than harsh-event counts. XGBoost maintained consistent performance under changing thresholds, while the DNN excelled in early-risk detection at lower thresholds but exhibited higher variability. The ensemble calibration integrates model-specific thresholds and confidence scores into a unified risk decision, balancing sensitivity and stability. Overall, the framework demonstrates the potential of adaptive and personalised risk detection to enhance real-time safety feedback and support driver-specific interventions within intelligent transport systems.