The field of automated driving is rapidly evolving, with a growing focus on addressing the complexities and challenges associated with level 3 autonomy. Researchers are exploring new approaches to improve safety, comfort, and driver-vehicle interaction. One key area of investigation is the development of risk-aware adaptive automation, which involves continuously adjusting the operational level of automation based on real-time risk assessments. This approach has the potential to enhance safety and reduce the risk of accidents. Additionally, there is a growing interest in using physiological and behavioral indicators, such as electrodermal activity and gaze parameters, to detect driver cognitive distraction and develop more effective driver monitoring systems. Noteworthy papers in this area include: DEV: A Driver-Environment-Vehicle Closed-Loop Framework for Risk-Aware Adaptive Automation of Driving, which proposes a comprehensive framework for risk-aware adaptive driving automation. Are Electrodermal Activity-Based Indicators of Driver Cognitive Distraction Robust to Varying Traffic Conditions and Adaptive Cruise Control Use, which investigates the robustness of electrodermal activity-based indicators of driver cognitive distraction.