Integrated Safety and Security Risk Analysis of Lane Keeping Assistance Using TARA

Abstract

Lane Keeping Assistance (LKA) systems play a critical role in enhancing vehicular safety and driving comfort by maintaining lane alignment and mitigating risks associated with driver distraction or drowsiness. These systems rely on sensor data to execute corrective steering or braking actions, yet their dependence on interconnected electronic components exposes them to a range of safety and cybersecurity threats. Attackers can exploit vulnerabilities in sensors, communication protocols, and Electronic Control Units (ECUs), potentially triggering false interventions or disabling safety functions. This paper presents a comparative Threat Analysis and Risk Assessment (TARA) of two LKA system architectures using the Medini Analyze tool. The first architecture employs a hierarchical controller with driver-intention detection and Electronic Stability Control (ESC)-based actuation. The second architecture employs a Learning-Based Model Predictive Control (LBMPC) framework enabling situation-adaptive decision-making. Through systematic identification and evaluation of threats and vulnerabilities, the analysis assesses risk levels associated with each design. The comparative analysis reveals trade-offs among architectural complexity, system robustness, and exposure to potential vulnerabilities, offering practical insights to improve the safety and security of LKA system designs.