  A trust-aware safe control system for autonomous navigation in the presence
of humans, specifically pedestrians, is presented. The system combines model
predictive control (MPC) with control barrier functions (CBFs) and trust
estimation to ensure safe and reliable navigation in complex environments.
Pedestrian trust values are computed based on features, extracted from camera
sensor images, such as mutual eye contact and smartphone usage. These trust
values are integrated into the MPC controller's CBF constraints, allowing the
autonomous vehicle to make informed decisions considering pedestrian behavior.
Simulations conducted in the CARLA driving simulator demonstrate the
feasibility and effectiveness of the proposed system, showcasing more
conservative behaviour around inattentive pedestrians and vice versa. The
results highlight the practicality of the system in real-world applications,
providing a promising approach to enhance the safety and reliability of
autonomous navigation systems, especially self-driving vehicles.
