Related papers: Risk Management of Unmanned Aerial Vehicles

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Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report v1.5 [61.787178868669265]
This technical report presents an updated and granular assessment of five critical dimensions: cyber offense, persuasion and manipulation, strategic deception, uncontrolled AI R&D, and self-replication.<n>This work reflects our current understanding of AI frontier risks and urges collective action to mitigate these challenges.
arXiv Detail & Related papers (2026-02-16T04:30:06Z)
Toward Quantitative Modeling of Cybersecurity Risks Due to AI Misuse [50.87630846876635]
We develop nine detailed cyber risk models.<n>Each model decomposes attacks into steps using the MITRE ATT&CK framework.<n>Individual estimates are aggregated through Monte Carlo simulation.
arXiv Detail & Related papers (2025-12-09T17:54:17Z)
The Role of Risk Modeling in Advanced AI Risk Management [33.357295564462284]
Rapidly advancing artificial intelligence (AI) systems introduce novel, uncertain, and potentially catastrophic risks.<n>Managing these risks requires a mature risk-management infrastructure whose cornerstone is rigorous risk modeling.<n>We argue that advanced-AI governance should adopt a similar dual approach and that verifiable, provably-safe AI architectures are urgently needed.
arXiv Detail & Related papers (2025-12-09T15:37:33Z)
From nuclear safety to LLM security: Applying non-probabilistic risk management strategies to build safe and secure LLM-powered systems [49.1574468325115]
Large language models (LLMs) offer unprecedented and growing capabilities, but also introduce complex safety and security challenges.<n>Previous research found that risk management in various fields of engineering such as nuclear or civil engineering is often solved by generic (i.e. field-agnostic) strategies.<n>Here we show how emerging risks in LLM-powered systems could be met with 100+ of these non-probabilistic strategies to risk management.
arXiv Detail & Related papers (2025-05-20T16:07:41Z)
An Approach to Technical AGI Safety and Security [72.83728459135101]
We develop an approach to address the risk of harms consequential enough to significantly harm humanity. We focus on technical approaches to misuse and misalignment. We briefly outline how these ingredients could be combined to produce safety cases for AGI systems.
arXiv Detail & Related papers (2025-04-02T15:59:31Z)
Mapping AI Benchmark Data to Quantitative Risk Estimates Through Expert Elicitation [0.7889270818022226]
We show how existing AI benchmarks can be used to facilitate the creation of risk estimates. We describe the results of a pilot study in which experts use information from Cybench, an AI benchmark, to generate probability estimates.
arXiv Detail & Related papers (2025-03-06T10:39:47Z)
EAIRiskBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [47.69642609574771]
Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction. Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results. However, the deployment of these agents in physical environments presents significant safety challenges. This study introduces EAIRiskBench, a novel framework for automated physical risk assessment in EAI scenarios.
arXiv Detail & Related papers (2024-08-08T13:19:37Z)
Intercepting Unauthorized Aerial Robots in Controlled Airspace Using Reinforcement Learning [2.519319150166215]
The proliferation of unmanned aerial vehicles (UAVs) in controlled airspace presents significant risks. This work addresses the need for robust, adaptive systems capable of managing such threats through the use of Reinforcement Learning (RL) We present a novel approach utilizing RL to train fixed-wing UAV pursuer agents for intercepting dynamic evader targets.
arXiv Detail & Related papers (2024-07-09T14:45:47Z)
Risks and Opportunities of Open-Source Generative AI [64.86989162783648]
Applications of Generative AI (Gen AI) are expected to revolutionize a number of different areas, ranging from science & medicine to education. The potential for these seismic changes has triggered a lively debate about the potential risks of the technology, and resulted in calls for tighter regulation. This regulation is likely to put at risk the budding field of open-source generative AI.
arXiv Detail & Related papers (2024-05-14T13:37:36Z)
Data-Adaptive Tradeoffs among Multiple Risks in Distribution-Free Prediction [55.77015419028725]
We develop methods that permit valid control of risk when threshold and tradeoff parameters are chosen adaptively. Our methodology supports monotone and nearly-monotone risks, but otherwise makes no distributional assumptions.
arXiv Detail & Related papers (2024-03-28T17:28:06Z)
Towards a Standardized Reinforcement Learning Framework for AAM Contingency Management [0.0]
We develop a contingency management problem as a Markov Decision Process (MDP) and integrate it into the AAM-Gym simulation framework. This enables rapid prototyping of reinforcement learning algorithms and evaluation of existing systems.
arXiv Detail & Related papers (2023-11-17T13:54:02Z)
A Counterfactual Safety Margin Perspective on the Scoring of Autonomous Vehicles' Riskiness [52.27309191283943]
This paper presents a data-driven framework for assessing the risk of different AVs' behaviors. We propose the notion of counterfactual safety margin, which represents the minimum deviation from nominal behavior that could cause a collision.
arXiv Detail & Related papers (2023-08-02T09:48:08Z)
To Risk or Not to Risk: Learning with Risk Quantification for IoT Task Offloading in UAVs [3.6118662460334527]
Deep reinforcement learning technique is presented for task offloading decision-making algorithms for a smart farm Internet of Things network. The technique uses financial concepts such as cost functions and conditional variable at risk (CVaR) in order to quantify the damage that may be caused by each risky action. The proposed CVaR-based technique was compared to other deep reinforcement learning techniques and two fixed rule-based techniques. The simulation results show that the CVaR-based risk method eliminated the most dangerous risk, which was exceeding the deadline for a fire detection task.
arXiv Detail & Related papers (2023-02-14T23:50:37Z)
Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation Models [103.71308117592963]
We present an algorithm for training self-destructing models leveraging techniques from meta-learning and adversarial learning. In a small-scale experiment, we show MLAC can largely prevent a BERT-style model from being re-purposed to perform gender identification.
arXiv Detail & Related papers (2022-11-27T21:43:45Z)
Risk Measurement, Risk Entropy, and Autonomous Driving Risk Modeling [0.0]
This article examines the emerging technical difficulties, new ideas, and methods of risk modeling under autonomous driving scenarios. It provides technical feasibility for realizing risk assessment and car insurance pricing under a computer simulation environment.
arXiv Detail & Related papers (2021-09-15T11:00:18Z)
Efficient UAV Trajectory-Planning using Economic Reinforcement Learning [65.91405908268662]
We introduce REPlanner, a novel reinforcement learning algorithm inspired by economic transactions to distribute tasks between UAVs. We formulate the path planning problem as a multi-agent economic game, where agents can cooperate and compete for resources. As the system computes task distributions via UAV cooperation, it is highly resilient to any change in the swarm size.
arXiv Detail & Related papers (2021-03-03T20:54:19Z)

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