Risk-Aware Nonlinear Model Predictive Control for Autonomous Navigation: Confidence-Based Obstacle Constraints and Time-to-Collision Safe Trajectory Planning

Nonlinear Model Predictive Control (NMPC) has established itself as a robust framework for autonomous navigation problems by effectively handling nonlinear dynamics and complex constraints in real-time. Despite its success in various applications, challenges remain in integrating robust obstacle avoidance while navigating in dynamic and uncertain environment. To enhance safety, risk assessment metrics like Time-To-Collision (TTC) are widely used for evaluating collision risks. Extensions to these metrics allow their application across diverse road scenarios, making them valuable as cost function components or constraints within risk-aware NMPC formulations. In this work, we propose novel obstacle constraint formulations that account for uncertainties in obstacle positions using confidence thresholds, along with TTC-based constraints to ensure safe trajectory planning. Our approaches are validated through extensive simulations in urban scenarios, including overtaking maneuvers and intersections, demonstrating their effectiveness in mitigating risks. Detailed implementations and results are publicly available to support further research and development.