The use of partially automated driving systems raises concerns about potential responsibility issues, posing risk to the system safety, acceptance, and adoption of these technologies. The concept of meaningful human control has emerged in response to the responsibility gap problem, requiring the fulfillment of two conditions, tracking and tracing. While this concept has provided important philosophical and design insights on automated driving systems, there is currently little knowledge on how meaningful human control relates to subjective experiences of actual users of these systems. To address this gap, our study aimed to investigate the alignment between the degree of meaningful human control and drivers' perceptions of safety and trust in a real-world partially automated driving system. We utilized previously collected data from interviews with Tesla "Full Self-Driving"(FSD) Beta users, investigating the alignment between the user perception and how well the system was tracking the users' reasons. We found that tracking of users' reasons for driving tasks (such as safe maneuvers) correlated with perceived safety and trust, albeit with notable exceptions. Surprisingly, failure to track lane changing and braking reasons was not necessarily associated with negative perceptions of safety. However, the failure of the system to track expected maneuvers in dangerous situations always resulted in low trust and perceived lack of safety. Overall, our analyses highlight alignment points but also possible discrepancies between perceived safety and trust on the one hand, and meaningful human control on the other hand. Our results can help the developers of automated driving technology to design systems under meaningful human control and are perceived as safe and trustworthy.

Intersections are critical bottlenecks within urban transportation networks. Current models for simulating two-dimensional (2D) vehicular movements at intersections are met with limitations in accurately representing complex interactions and capturing vehicle dynamics. Accordingly, this paper proposes a novel microsimulation framework for trajectory planning and vehicular control at intersections. The model considers vehicle dynamics and control variables, such as acceleration and steering angle, and releases the popular assumption that there is full knowledge sharing or cooperation among vehicles at intersections. These features make the proposed framework more realistic compared to previous microsimulation attempts and applicable to traffic flow and environmental impact assessment studies. In addition, it efficiently operates in realtime for multiple vehicles, overcoming the limitations of offline methods. Moreover, the model is capable of accounting for driver/vehicle detection range, reaction time, and perception and prediction inaccuracies, which enhances its suitability for safety assessments. The evaluation in several scenarios indicates the ability of the proposed framework in realtime planning and following realistic and consistent 2D paths while avoiding collisions with other vehicles.