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1. A Reference Architecture of Human Cyber-Physical Systems – Part I: Fundamental Concepts

   https://doi.org/10.1145/3622879  

   Damm, Werner; Hess, David; Schweda, Mark; Sztipanovits, Janos; Bengler, Klaus; Biebl, Bianca; Fränzle, Martin; Hagemann, Willem; Held, Moritz; Ihme, Klas; et al (January 2024, ACM Transactions on Cyber-Physical Systems)

   Chenyang Lu (Ed.)

   We propose a reference architecture of safety-critical or industry-critical human cyber-physical systems (CPSs) capable of expressing essential classes of system-level interactions between CPS and humans relevant for the societal acceptance of such systems. To reach this quality gate, the expressivity of the model must go beyond classical viewpoints such as operational, functional, and architectural views and views used for safety and security analysis. The model does so by incorporating elements of such systems for mutual introspections in situational awareness, capabilities, and intentions to enable a synergetic, trusted relation in the interaction of humans and CPSs, which we see as a prerequisite for their societal acceptance. The reference architecture is represented as a metamodel incorporating conceptual and behavioral semantic aspects. We illustrate the key concepts of the metamodel with examples from cooperative autonomous driving, the operating room of the future, cockpit-tower interaction, and crisis management. 

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2. Agentic Driving Coach: Robustness and Determinism of Agentic AI-Powered Human-in-the-Loop Cyber-Physical Systems

   https://doi.org/10.1109/FMSys72110.2026.00013  

   Prahlad, Deeksha; Fan, Daniel; Kim, Hokeun (May 2026, IEEE)

   Foundation models, including large language models (LLMs), are increasingly used for human-in-the-loop (HITL) cyber-physical systems (CPS) because foundation model-based AI agents can potentially interact with both the physical environments and human users. However, the unpredictable behavior of human users and AI agents, in addition to the dynamically changing physical environments, leads to uncontrollable nondeterminism. To address this urgent challenge of enabling agentic AI-powered HITL CPS, we propose a reactor-model-of-computation (MoC)-based approach, realized by the open-source Lingua Franca (LF) framework. We also carry out a concrete case study using the agentic driving coach as an application of HITL CPS. By evaluating the LF-based agentic HITL CPS, we identify practical challenges in reintroducing determinism into such agentic HITL CPS and present pathways to address them. 

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3. RAMPART: Reinforcing Autonomous Multi-agent Protection through Adversarial Resistance in Transportation

   https://doi.org/10.1145/3643137  

   Hossain, Md Tamjid; La, Hung; Badsha, Shahriar (January 2024, ACM Journal on Autonomous Transportation Systems)

   In the field of multi-agent autonomous transportation, such as automated payload delivery or highway on-ramp merging, agents routinely exchange knowledge to optimize their shared objective and adapt to environmental novelties through Cooperative Multi-Agent Reinforcement Learning (CMARL) algorithms. This knowledge exchange between agents allows these systems to operate efficiently and adapt to dynamic environments. However, this cooperative learning process is susceptible to adversarial poisoning attacks, as highlighted by contemporary research. Particularly, the poisoning attacks where malicious agents inject deceptive information camouflaged within the differential noise, a pivotal element for differential privacy (DP)-based CMARL algorithms, pose formidable challenges to identify and overcome. The consequences of not addressing this issue are far-reaching, potentially jeopardizing safety-critical operations and the integrity of data privacy in these applications. Existing research has strived to develop anomaly detection-based defense models to counteract conventional poisoning methods. Nonetheless, the recurring necessity for model offloading and retraining with labeled anomalous data undermines their practicality, considering the inherently dynamic nature of the safety-critical autonomous transportation applications. Further, it is imperative to maintain data privacy, ensure high performance, and adapt to environmental changes. Motivated by these challenges, this paper introduces a novel defense mechanism against stealthy adversarial poisoning attacks in the autonomous transportation domain, termed Reinforcing Autonomous Multi-agent Protection through Adversarial Resistance in Transportation (RAMPART). Leveraging a GAN model at each local node, RAMPART effectively filters out malicious advice in an unsupervised manner, whilst generating synthetic samples for each state-action pair to accommodate environmental uncertainties and eliminate the need for labeled training data. Our extensive experimental analysis, conducted in a Private Payload Delivery Network (PPDN) —a common application in the autonomous multi-agent transportation domain—demonstrates thatRAMPART successfully defends against a DP-exploited poisoning attack with a\(30\% \)attack ratio, achieving an F1 score of 0.852 and accuracy of\(96.3\% \)in heavy-traffic environments. 

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4. Deep Meta Coordination Graphs for Multi-Agent Reinforcement Learning

   https://doi.org/10.65109/IIKC6491  

   Gupta, Nikunj; Hare, James Zachary; Milzman, Jesse; Kannan, Rajgopal; Prasanna, Viktor (May 2026, International Foundation for Autonomous Agents and Multiagent Systems)

   Extended Abstract This paper presents deep meta coordination graphs (DMCG) for learning cooperative policies in multi-agent reinforcement learning (MARL). Coordination graph formulations encode local interactions and accordingly factorize the joint value function of all agents to improve efficiency in MARL. DMCG learns a more expressive representation of agent interactions and use them to integrate agent information through graph convolutional networks. The goal is to enable an evolving coordination graph to guide effective coordination in cooperative MARL tasks. The graphs are jointly optimized with agents’ value functions to learn to implicitly reason about joint actions, facilitating the end-to-end learning of interaction representations and coordinated policies. We demonstrate that DMCG consistently achieves state-of-the-art coordination performance and sample efficiency on challenging cooperative tasks, outperforming several prior graph-based and non-graph-based MARL baselines. Through several ablations, we also isolate the impact of individual components in DMCG, showing that the observed improvements are due to the meaningful design choices in this approach. All codes can be found here: https://github.com/Nikunj-Gupta/dmcg-marl. 

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5. Move Beyond Trajectories: Distribution Space Coupling for Crowd Navigation

   https://doi.org/10.15607/RSS.2021.XVII.053  

   Sun, Muchen; Baldini, Francesca; Trautman, Peter; Murphey, Todd (July 2021, Robotics: Science and Systems)

   null (Ed.)

   Cooperatively avoiding collision is a critical functionality for robots navigating in dense human crowds, failure of which could lead to either overaggressive or overcautious behavior. A necessary condition for cooperative collision avoidance is to couple the prediction of the agents’ trajectories with the planning of the robot’s trajectory. However, it is unclear that trajectory based cooperative collision avoidance captures the correct agent attributes. In this work we migrate from trajectory based coupling to a formalism that couples agent preference distributions. In particular, we show that preference distributions (probability density functions representing agents’ intentions) can capture higher order statistics of agent behaviors, such as willingness to cooperate. Thus, coupling in distribution space exploits more information about inter-agent cooperation than coupling in trajectory space. We thus introduce a general objective for coupled prediction and planning in distribution space, and propose an iterative best response optimization method based on variational analysis with guaranteed sufficient decrease. Based on this analysis, we develop a sampling-based motion planning framework called DistNav1 that runs in real time on a laptop CPU. We evaluate our approach on challenging scenarios from both real world datasets and simulation environments, and benchmark against a wide variety of model based and machine learning based approaches. The safety and efficiency statistics of our approach outperform all other models. Finally, we find that DistNav is competitive with human safety and efficiency performance. 

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