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HSCC+ICCPS 2027: ACM/IEEE International Conference on Cyber-Physical and Autonomous Systems

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HSCC+ICCPS
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投稿締切日:
2026-10-29 残り 34 日
通知日:
2027-01-28
開催日:
2027-05-17
開催地:
Boulder, Colorado, USA
閲覧: 21   フォロー: 0   参加: 0

会伴インデックス (CP-I)

43.3 / 100
全 5,687 件中 第 3,626 位 · 上位 64%

セキュリティ・プライバシー 分野 361 件中 第 223 位 ロボティクス・制御 分野 470 件中 第 252 位

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学術的評価 (35%) データなし — 中立の基準値 50 点として算入 —
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開催回数 (20%) データなし — 中立の基準値 50 点として算入 —
コミュニティの注目度 (10%)
5
公開情報の充実度 (15%)
35

使用した入力: 過去 24 か月にこのページを開いた研究者：1 人

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信頼度 25% — スコアのうち、中立の基準値ではなく実際に観測されたデータに基づく割合。 このスコアの算出方法 · ランキングを見る · アルゴリズム版 1.1 · 算出日 2026-09-25

論文募集

HSCC+ICCPS 2027 (ACM/IEEE International Conference on Cyber-Physical and Autonomous Systems) is an academic conference held in Boulder, Colorado, USA on 2027-05-17. The paper submission deadline is 2026-10-29. Acceptance notifications are sent on 2027-01-28.

Scope and tracks We invite original, high-quality research on cyber-physical, hybrid, and autonomous systems. The program is organized around three coordinated tracks: Foundations, Systems and Applications, and AI-enabled Autonomy. Work that bridges tracks is especially welcome. Authors should select every track that materially represents the paper's intellectual contribution, rather than choosing solely by application domain or by the presence of a particular technique. At least one track is required, and work spanning two or three tracks is welcome. The unified program committee will coordinate reviewing across the selected areas and may adjust a paper's classification for substantially better expert review after informing the authors. Foundations The Foundations track seeks fundamental advances in the modeling, analysis, design, and control of cyber-physical and hybrid systems. It welcomes theory, algorithms, and tools with rigorous guarantees, including computational work grounded in important applications. Topics include, but are not limited to: Models, logic, languages, and computation Algorithmic foundations for CPS Computational tools and complexity analysis Continuous-time and discrete-event models Formal methods Hybrid systems theory Logic and Automated Reasoning Mathematical foundations, computability and complexity Modeling paradigms Programming and specification languages Specification languages Analysis, synthesis, planning, and control Analysis, verification, validation, and testing methodologies Control Theory Differential games Formally verified AI, learning-enabled systems, and robotics High assurance techniques for CPS Network science and network-based control theory Nonlinear control theory Optimal control Planning Reachability analysis Stability analysis Temporal logic and runtime monitoring Theoretical foundations of safe autonomy Testing Systems and Applications The Systems and Applications track seeks advances whose primary contribution lies in system architecture, implementation, integration, experimental evaluation, deployment, or demonstrated impact in a physical setting. It welcomes research spanning platforms and applications, including industrial and government experience when the work offers a generalizable technical contribution. Topics include, but are not limited to: Architectures, platforms, and integration CPS architectures and platforms Human-machine and human-robot interaction Real-time, embedded, distributed, mobile, edge, and networked systems Sensing, actuation, and monitoring systems Software tools and platforms System integration and middleware for CPS Evaluation, deployment, security, and resilience Case studies Deployments and industrial applications Digital twins, benchmarks, and reproducible evaluations Real-world case studies and testbeds Security and privacy Security, privacy, and resilience design and implementations Application domains Aerospace and avionics Agriculture and environmental monitoring Automotive and transportation systems Energy and smart grid systems Healthcare and medical devices Manufacturing and Industry 4.0 Medical Cyber-Physical Systems Power Systems Robotics and autonomous systems Smart cities and infrastructure AI-enabled Autonomy The AI-enabled Autonomy track is a major pillar of the conference. It welcomes researchers in artificial intelligence, machine learning, robotics, computer vision, control, and formal methods whose work advances autonomous or learning-enabled systems that sense, decide, and act in the physical world. The track values both new AI capabilities and credible evidence that those capabilities can operate safely, robustly, and responsibly in cyber-physical settings. Topics include, but are not limited to: AI, learning, perception, and decision making AI-based situational awareness and decision-making Data-driven modeling and analysis Decision making under uncertainty and risk-aware autonomy Explainable AI for safety-critical systems Foundation models and transformers for CPS Generative AI for cyber-physical systems Learning-based control and planning Multi-agent autonomy, human-AI interaction, and collective robotics Multimodal and vision-language models for cyber-physical systems and robotics Neurosymbolic approaches to autonomy Physics-informed machine learning Reinforcement and imitation learning for autonomous systems Robot learning, embodied AI, perception, localization, mapping, and scene understanding Safe learning and verification of AI-enabled CPS Trustworthy autonomous-systems design and assurance Assurance cases for AI-enabled CPS Design, verification, test, and maintenance of autonomous systems HW/SW architectures for dependable and resilient autonomous systems Mastering emergent and evolving behavior (goals, constraints, …) Functional safety and cybersecurity of autonomous systems Human Factors in Autonomy Runtime assurance, fault tolerance, and adversarial robustness for autonomous systems Models and HW/SW mechanisms for self-X capabilities Design processes for trustworthy autonomous systems Merely using an AI component does not require submission to this track. A paper whose central contribution is a new formal verification algorithm may fit Foundations, while a paper centered on an implemented system architecture or field deployment may fit Systems and Applications. Papers whose core contribution advances learning, perception, autonomous decision making, or the assurance of those capabilities are natural fits for AI-enabled Autonomy.
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