Trust-aware Safe Control for Autonomous Navigation: Estimation of System-to-human Trust for Trust-adaptive Control Barrier Functions

TL;DR

This paper introduces a trust-aware control system for autonomous vehicles that estimates pedestrian trust levels from visual cues and integrates this information into control barrier functions, improving safety and decision-making.

Contribution

It presents a novel integration of trust estimation from camera data into MPC with CBFs for adaptive, safe autonomous navigation around pedestrians.

Findings

System effectively estimates pedestrian trust from visual features.

Trust-aware control results in more conservative behavior around inattentive pedestrians.

Simulation demonstrates improved safety and decision-making in complex environments.

Abstract

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.