Dynamic Risk Assessment: Leveraging Ensemble Learning for Context-Specific Risk Features

In the rapidly evolving landscape of Automated Driving Systems (ADS), the dynamic nature of driving environments poses significant challenges to traditional static risk assessment models. Addressing this, our paper introduces an approach focused on predicting severity and controllability ratings using context-specific risk features. We have developed a model that leverages the strengths of Random Forest (RF) and Gradient Boosting Decision Trees (GBDT) to adeptly navigate the complexities of diverse driving conditions.Central to our approach is the effective processing and analysis of data derived from risk-specific contexts, converting this information into actionable risk features through well-established mathematical formulations. This model excels in contextual understanding, providing a more detailed and accurate risk assessment compared to traditional methods. Unlike subjective approaches, our model offers an objective and data-driven analysis. A key implementation of our model is showcased in an Adaptive Cruise Control (ACC) system, particularly in highway lane-following scenarios under various driving conditions. This application highlights the model’s capability to dynamically assess risks and prioritize factors, thereby substantially improving Dynamic Risk Assessment (DRA) in ADS.In essence, our research successfully bridges the gap between static risk models and the dynamic nature of driving environments. By integrating ensemble learning models like RF and GBDT, we present an advanced and dynamic tool for risk assessment in ADS, marking an improvement in the field of automotive safety.