Trust-Aware Safe Control for Autonomous Navigation: Estimation of System-to-Human Trust for Trust-Adaptive Control Barrier Functions

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 system-to-human trust (SHT) estimation to ensure safe and reliable navigation in human-populated environments. In the context of this article, we refer to SHT as the confidence score that a system has in an agent/pedestrian’s attentiveness. Pedestrian SHT values are computed based on features, extracted from camera sensor images, such as mutual eye contact, smartphone usage, and pose fluctuations and 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 behavior 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 efficiency of autonomous navigation systems, especially self-driving vehicles.