Learning from Drivers: A Case-Based Reasoning Framework for Takeover Control in Conditionally Automated Vehicles
The rapid evolution of intelligent transportation systems has positioned conditionally automated vehicles (CAVs, SAE Level 3) at the forefront of automotive innovation. These vehicles represent a critical transition between human-driven and fully autonomous systems, in which safety and reliability depend on effective management of takeover control (TOC) events. This paper introduces a Case-Based Reasoning (CBR) framework to model, evaluate, and improve decision-making during control transitions using empirical and contextual data from both human and vehicular agents. The framework follows the CBR cognitive cycle of retrieval, reuse, revision, and retention to compare new TOC scenarios with previously observed cases. Each case integrates multimodal information, including driver personal traits, non-driving-related tasks, traffic density, and takeover urgency, as well as temporal and spatial performance metrics such as takeover time and steering behaviour. The time budget to system limitation is used as the determining outcome variable. By capturing and reusing experiential knowledge, the proposed framework enables adaptive and interpretable decision-making for Level 3 automation. It supports bidirectional learning between drivers and automated systems and provides a foundation for future Levels 4 and 5 vehicles to incorporate human-like reasoning in safety-critical decisions.