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Understanding the intention of vehicles in the surrounding traffic is crucial for an autonomous vehicle to successfully accomplish its driving tasks in complex traffic scenarios such as highway forced merging. In this paper, we consider a behavioral model that incorporates both social behaviors and personal objectives of the interacting drivers. Leveraging this model, we develop a receding-horizon control-based decision-making strategy, that estimates online the other drivers' intentions using Bayesian filtering and incorporates predictions of nearby vehicles' behaviors under uncertain intentions. The effectiveness of the proposed decision-making strategy is demonstrated and evaluated based on simulation studies in comparison with a game theoretic controller and a real-world traffic dataset. 

A parameter governor-based control scheme is developed to enforce various constraints, such as the Line of Sight (LoS) cone angle, the thrust limit, and the relative approach velocity during rendezvous missions in a near rectilinear halo orbit (NRHO) in the Earth-Moon system. The parameter governor is an add-on scheme to the nominal closed-loop system, which dynamically adjusts controller parameters in order to enforce the constraints. For the application to the rendezvous mission, we utilize the Time Shift Governor (TSG) which time shifts the target trajectory commanded to the Deputy spacecraft controller. The time shift is gradually reduced to zero so that the virtual target trajectory gradually converges to the Chief spacecraft trajectory as time evolves, and the rendezvous mission can be accomplished. Simulation results are reported that demonstrate the effectiveness of the proposed control scheme. 

This paper illustrates an approach to integrate learning into spacecraft automated rendezvous, proximity maneuvering, and docking (ARPOD) operations. Spacecraft rendezvous plays a significant role in many spacecraft missions including orbital transfers, ISS re-supply, on-orbit refueling and servicing, and debris removal. On one hand, precise modeling and prediction of spacecraft dynamics can be challenging due to the uncertainties and perturbation forces in the spacecraft operating environment and due to multi-layered structure of its nominal control system. On the other hand, spacecraft maneuvers need to satisfy required constraints (thrust limits, line of sight cone constraints, relative velocity of approach, etc.) to ensure safety and achieve ARPOD objectives. This paper considers an application of a learning-based reference governor (LRG) to enforce constraints without relying on a dynamic model of the spacecraft during the mission. Similar to the conventional Reference Governor (RG), the LRG is an add-on supervisor to a closed-loop control system, serving as a pre-filter on the command generated by the ARPOD planner. As the RG, LRG modifies, if it becomes necessary, the command to a constraint-admissible reference to enforce specified constraints. The LRG is distinguished, however, by the ability to rely on learning instead of an explicit model of the system, and guarantees constraints satisfaction during and after the learning. Simulations of spacecraft constrained relative motion maneuvers on a low Earth orbit are reported that demonstrate the effectiveness of the proposed approach.