Runtime geofencing for ground vehicles is rapidly emerging as a critical technology for enforcing Operational Design Domains (ODDs). However, existing solutions struggle to reconcile high-fidelity learning with the structural requirements of verifiable control. We address this by introducing PCARNN-DCBF, a novel pipeline integrating a Physics-encoded Control-Affine Residual Neural Network with a preview-based Discrete Control Barrier Function. Unlike generic learned models, PCARNN explicitly preserves the control-affine structure of vehicle dynamics, ensuring the linearity required for reliable optimization. This enables the DCBF to enforce polygonal keep-in constraints via a real-time Quadratic Program (QP) that handles high relative degree and mitigates actuator saturation. Experiments in CARLA across electric and combustion platforms demonstrate that this structure-preserving approach significantly outperforms analytical and unstructured neural baselines.

Verifying the safety of controllers is critical for many applications, but is especially challenging for systems with bounded inputs. Backup control barrier functions (bCBFs) offer a structured approach to synthesizing safe controllers that are guaranteed to satisfy input bounds by leveraging the knowledge of a backup controller. While powerful, bCBFs require solving a high-dimensional quadratic program at run-time, which may be too costly for computationally-constrained systems such as aerospace vehicles. We propose an approach that optimally interpolates between a nominal controller and the backup controller, and we derive the solution to this optimization problem in closed form. We prove that this closed-form controller is guaranteed to be safe while obeying input bounds. We demonstrate the effectiveness of the approach on a double integrator and a nonlinear fixed-wing aircraft example.

Recent advances in the development of robotic foundation models have led to promising end-to-end and general-purpose capabilities in robotic systems. Trained on vast datasets of simulated and real-world trajectories, these models map multimodal observations directly to action sequences for physical execution. Despite promising real-world capabilities, these models are still data-driven and, therefore, lack explicit notions of behavioral correctness. We address this gap by introducing SafeDec, a constrained decoding framework for autoregressive, robot foundation models that enforces invariant safety specifications on candidate action trajectories. Task-specific safety rules are expressed as Signal Temporal Logic (STL) formulas and are enforced at inference time with minimal overhead. Our method ensures that generated actions provably satisfy STL specifications under assumed dynamics at runtime without retraining , while remaining agnostic of the underlying policy. We evaluate SafeDec on tasks from the CHORES benchmark for state-of-the-art generalist policies (e.g., SPOC, Flare, PoliFormer) across hundreds of procedurally generated environments and show that our decoding-time interventions are useful not only for filtering unsafe actions but also for conditional action generation. Videos are available at constrained-robot-fms.github.io.

Geofences have attracted significant attention in the design of spatial and virtual regions for managing and engaging spatiotemporal events. By using geofences to monitor human activity across their boundaries, content providers can create spatially triggered events that include notifications about points of interest within a geofence by pushing spatial information to the devices of users. Traditionally, geofences were hand-crafted by providers. In addition to the hand-crafted approach, recent advances in collecting human mobility data through mobile devices can accelerate the automatic and data-driven design of geofences, also known as the geofence design problem. Previous approaches assume circular shapes; thus, their flexibility is insufficient, and they can only handle geofence-based applications for large areas with coarse resolutions. A challenge with using circular geofences in urban and high-resolution areas is that they often overlap and fail to align with political district boundaries and road segments, such as one-way streets and median barriers. In this study, we address the problem of extracting arbitrary shapes as geofences from human mobility data to mitigate this problem. In our formulation, we cast the existing optimization problems for circular geofences to 0-1 integer programming problems to represent arbitrary shapes. Although 0-1 integer programming problems are computationally hard, formulating them as quadratic (unconstrained) binary optimization problems enables efficient approximation of optimal solutions, because this allows the use of specialized quadratic solvers, such as the quantum annealing, and other state-of-the-art algorithms. We then develop and compare different formulation methods to extract discrete geofences. We confirmed that our new modeling approach enables flexible geofence design.

This week DJI, the world's leading drone manufacturer, announced a new policy removing enforcement of its "No Fly Zone" geofences in restricted areas. The sudden shift may lead to more drones hovering where they shouldn't, which could worsen a lingering national panic over flying objects in the sky. DJI, the China-based drone giant, says it will no longer enforce geofence barriers that prevent its products from flying over restricted areas like airports, wildfires, and government buildings. Though the company says these changes are intended to empower its users, they come amid a surge in drone sightings, some around critical infrastructure, that have stoked fears and fueled a growing tide of conspiracy theories. DJI's changes mean operators will have one less guardrail preventing them from flying into risky areas.

DJI has lifted its geofence that prevents users in the US from flying over restricted areas like nuclear power plants, airports and wildfires, the company wrote in a blog post on Monday. As of January 13th, areas previously called "restricted zones" or no-fly zones will be shown as "enhanced warning zones" that correspond to designated Federal Aviation Administration (FAA) areas. DJI's Fly app will display a warning about those areas but will no longer stop users from flying inside them, the company said. In the article, DJI wrote that the "in-app alerts will notify operators flying near FAA designated controlled airspace, placing control back in the hands of the drone operators, in line with regulatory principles of the operator bearing final responsibility." It added that technologies like Remote ID [introduced after DJI implemented geofencing] gives authorities "the tools needed to enforce existing rules," DJI's global policy chief Adam Welsh told The Verge.

Safety-critical failures often have fatal consequences in aerospace control. Control systems on aircraft, therefore, must ensure the strict satisfaction of safety constraints, preferably with formal guarantees of safe behavior. This paper establishes the safety-critical control of fixed-wing aircraft in collision avoidance and geofencing tasks. A control framework is developed wherein a run-time assurance (RTA) system modulates the nominal flight controller of the aircraft whenever necessary to prevent it from colliding with other aircraft or crossing a boundary (geofence) in space. The RTA is formulated as a safety filter using control barrier functions (CBFs) with formal guarantees of safe behavior. CBFs are constructed and compared for a nonlinear kinematic fixed-wing aircraft model. The proposed CBF-based controllers showcase the capability of safely executing simultaneous collision avoidance and geofencing, as demonstrated by simulations on the kinematic model and a high-fidelity dynamical model.

Identifying driving maneuvers plays an essential role on-board vehicles to monitor driving and driver states, as well as off-board to train and evaluate machine learning algorithms for automated driving for example. Maneuvers can be characterized by vehicle kinematics or data from its surroundings including other traffic participants. Extracting relevant maneuvers therefore requires analyzing time-series of (i) structured, multi-dimensional kinematic data, and (ii) unstructured, large data samples for video, radar, or LiDAR sensors. However, such data analysis requires scalable and computationally efficient approaches, especially for non-annotated data. In this paper, we are presenting a maneuver detection approach based on two variants of space-filling curves (Z-order and Hilbert) to detect maneuvers when passing roundabouts that do not use GPS data. We systematically evaluate their respective performance by including permutations of selections of kinematic signals at varying frequencies and compare them with two alternative baselines: All manually identified roundabouts, and roundabouts that are marked by geofences. We find that encoding just longitudinal and lateral accelerations sampled at 10Hz using a Hilbert space-filling curve is already successfully identifying roundabout maneuvers, which allows to avoid the use of potentially sensitive signals such as GPS locations to comply with data protection and privacy regulations like GDPR.

What will it take for a car to be able to drive itself anywhere a human can? Ask autonomous vehicle experts this question and the answer invariably includes a discussion of geofencing. In the broadest sense, geofencing is simply a virtual boundary around a physical area. In the world of self-driving cars, it describes a crucial subset of the operational design domain -- the geographic region where the vehicle is functional. Reaching full Level 5 autonomy means removing the "fence" from geofenced autonomous cars. Experts say that will require artificial intelligence that can make abstractions, inferences, and become smarter as it is being used.

The airworthiness and safety of a non-pedigreed autopilot must be verified, but the cost to formally do so can be prohibitive. We can bypass formal verification of non-pedigreed components by incorporating Runtime Safety Assurance (RTSA) as mechanism to ensure safety. RTSA consists of a meta-controller that observes the inputs and outputs of a non-pedigreed component and verifies formally specified behavior as the system operates. When the system is triggered, a verified recovery controller is deployed. Recovery controllers are designed to be safe but very likely disruptive to the operational objective of the system, and thus RTSA systems must balance safety and efficiency. The objective of this paper is to design a meta-controller capable of identifying unsafe situations with high accuracy. High dimensional and non-linear dynamics in which modern controllers are deployed along with the black-box nature of the nominal controllers make this a difficult problem. Current approaches rely heavily on domain expertise and human engineering. We frame the design of RTSA with the Markov decision process (MDP) framework and use reinforcement learning (RL) to solve it. Our learned meta-controller consistently exhibits superior performance in our experiments compared to our baseline, human engineered approach.